Impact of Driverless cars on Urban environments and Mobility of future

Architecture Urban Planning Construction Engineering Course: Masters in Urban Planning and Policy design July 2018

Author: Rahul Parmar Thesis tutor : Prof. Luca Studer

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Abstract Driverless cars or autonomous cars have become topic of debate in various forums for both governments and academia. A lot of research is focussed on the technical aspects which deals with what kind of technologies will be needed in order to achieve the aims of having a completely driverless car. Other popular research in this field focus on the market penetration and sales aspect. Not a lot of research is done on the possible effects that this technology will have on our cities. Autonomy along with electrification and sharing is likely to change the face of individual transport in next three decades. These revolutions can be independent or in conjunction with each other. This research tries to understand and grasp the changes in mobility behaviour through a scenario building approach. Various topics that relate to mobility and cities are analysed based on available research. The aim of the research is focus on the way this technology can impact our urban environments and set the base for policy changes at different levels.

Planners, Policy makers, technologists and other new stakeholders (service provider) need to take a holistic and multi-disciplinary approach in understanding and assessing the effect this technology will have in our lives and cities. This work take a speculative approach by building scenarios that are on extreme ends but the aim is to stay grounded and understand the arrays of possibilities. Driverless technology is going to hit the market with or without the support of local governments but if governments are proactive then it provides a great opportunity to solve the existing problem in our cities for e.g. congestion, parking, safety, quality of urban environment , GHG emissions etc.

Abstract (Italian) Le auto senza conducente o le auto autonome sono diventate argomento di dibattito in vari forum sia per i governi che per il mondo accademico. Molte ricerche sono focalizzate sugli aspetti tecnici che riguardano il tipo di tecnologie necessarie per raggiungere gli obiettivi di avere un'auto completamente senza conducente. Altre ricerche popolari in questo campo si concentrano sulla penetrazione del mercato e l'aspetto delle vendite. Non vengono fatte molte ricerche sui possibili effetti che questa tecnologia avrà sulle nostre città. L'autonomia insieme all'elettrificazione e alla condivisione cambierà probabilmente il volto del trasporto individuale nei prossimi trent'anni. Queste rivoluzioni possono essere indipendenti o in combinazione tra loro. Questa ricerca cerca di comprendere e comprendere i cambiamenti nel comportamento della mobilità attraverso un approccio di costruzione di scenari. Diversi argomenti relativi alla mobilità e alle città vengono analizzati in base alla ricerca disponibile. L'obiettivo della ricerca è focalizzato sul modo in cui questa tecnologia può avere un impatto sui nostri ambienti urbani e impostare la base per cambiamenti politici a diversi livelli. Pianificatori, responsabili politici, tecnologi e altri nuovi soggetti interessati (fornitore di servizi) devono adottare un approccio olistico e multidisciplinare per comprendere e valutare l'effetto che questa tecnologia avrà nelle nostre vite e nelle nostre città. Questo lavoro prende un approccio speculativo costruendo scenari che sono ai limiti estremi, ma l'obiettivo è quello di rimanere radicati e comprendere gli array di possibilità. La tecnologia senza conducente sta andando a colpire il mercato con o senza il supporto dei governi locali, ma se i governi sono proattivi, fornisce una grande opportunità per risolvere il problema esistente nelle nostre città per es. congestione, parcheggio, sicurezza, qualità dell'ambiente urbano, emissioni di gas serra, ecc.

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Acknowledgement First of all I would like to thank all the authors whose works I have referred in order to write this research.

If it was not for these researcher my work would not have taken shape the way it did.

Secondly, I would like to thank my tutor Prof. Luca Studer and his colleagues in the lab for giving his valuable feedback whenever necessary. His feedbacks were integral in finishing this work.

Thirdly, I would like to thank my colleagues at MIC (Mobility in chain) for interesting discussions and ideas that helped me to understand mobility of cities in a better way. I have learned a lot about work ethics, how to approach a problem and form constructive arguments while I am working at MIC for past 1 year.

Lastly, I would like to thank Filippo Bazzoni (Senior Consultant Systematica srl) for giving the head start to take this topic of research. It has been a great learning experience for me and the initial push was important to get the research going. I would also like to thank Rawad Choubassi (Partner) and team at Systematica srl when I was working there for a period of 6 months for giving me necessary inputs to kick start this research. Some of the ideas I have used here were conceptualised while I was working there.

Furthermore, I would like to thank my parents, brother and close friends for their constant support all throughout this year. This support was indeed important to keep me motivated besides all odds.

Thanks to science and academic community for making this journey worthwhile, a lot of this to come in future.

Towards an informed society‌‌

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Introduction ............................................................................................................................................... 0 1.1

Historical development ..................................................................................................................... 2

1.2

Timeline and consumer readiness for AVs in market ........................................................................ 5

3 important revolution in Mobility .......................................................................................................... 10 2.1

Electrification ................................................................................................................................... 10

2.2

Automation ...................................................................................................................................... 13

2.3

Shared Mobility ............................................................................................................................... 16

3 Scenarios of Implementation of AVs .................................................................................................... 19 3.1.1

Limited CAV penetration ......................................................................................................... 19

3.1.2

Private CAV as a new mode of transportation (Private CAVS, more convenience etc. .......... 20

3.1.3

MaaS (Mobility as a service, Fleet CAVs, shared CAVs, on demand CAVs) ............................. 22

Impact on Traffic...................................................................................................................................... 26 4.1

Induced Demand ............................................................................................................................. 26

4.2

Vehicle Miles travelled .................................................................................................................... 26

4.3

Congestion ....................................................................................................................................... 26

4.4

Vehicle Fleet and Occupancy ........................................................................................................... 27

4.4.1 5

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Conclusion of the effects ......................................................................................................... 31

Social impact of AVs ................................................................................................................................ 32 5.1

Mobility for all ................................................................................................................................. 32

5.2

Car Ownership ................................................................................................................................. 34

5.3

Productivity benefits of the population .......................................................................................... 34

5.4

Consumer readiness to automated driving ..................................................................................... 35

5.5

Change in employment patterns ..................................................................................................... 37

5.5.1

Uber Freight ............................................................................................................................. 37

5.5.2

Conclusion of social impacts.................................................................................................... 38

Impact on Parking .................................................................................................................................... 40 6.1

Parking and land use........................................................................................................................ 40

6.2

Types of Parking .............................................................................................................................. 40

6.3

Parking demand ............................................................................................................................... 41

6.4

Parking structure ............................................................................................................................. 44

6.5

Drop offs and street design ............................................................................................................. 47

6.6

Adaptation in parking structure design ........................................................................................... 48

Impact on Public transport ...................................................................................................................... 51 7.1

Public bus in an era of AVs .............................................................................................................. 52

7.2

Other modes of PT emerge ............................................................................................................. 54 vi

7.3 8

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PT in different scenarios .................................................................................................................. 55

Transport Costs........................................................................................................................................ 57 8.1

Private cost ...................................................................................................................................... 57

8.2

Cost affecting the Mobility choices ................................................................................................. 58

8.3

Cost from accidents & congestion ................................................................................................... 62

8.4

Parking and Land cost ...................................................................................................................... 64

8.5

Platooning and delivery cost ........................................................................................................... 65

Ethics safety and insurance policies for driverless cars........................................................................... 68 9.1

Freeing people from the necessity of driving? ................................................................................ 68

9.2

Should humans drive in the age of driverless cars? ........................................................................ 70

9.3

The Trolley problem ........................................................................................................................ 71

9.4

Impact on pedestrian and cyclists ................................................................................................... 72

10 Impact on Urban sprawl and densities .................................................................................................... 77 11 Environment impact of AVs ..................................................................................................................... 80 12 Conclusion, recommendations and way forward.................................................................................... 84 12.1

Short term recommendations ......................................................................................................... 85

12.1.1

Monitor the progress of technology ....................................................................................... 85

12.1.2

Integrate AVs in local plans & goals ........................................................................................ 85

12.1.3

Establish relationship with local stakeholders and service providers ..................................... 86

12.1.4

Revaluate the future local plans and infrastructure needs ..................................................... 86

12.2

Long term recommendations and activities .................................................................................... 86

12.2.1

Update travel demand model & road capacity ....................................................................... 86

12.2.2

Assess transit services and fleet requirement ......................................................................... 86

12.2.3

Forecast financial implications on revenues ........................................................................... 87

12.2.4

Parking management plan ....................................................................................................... 87

12.2.5

Update urban design guidelines for streets and junctions ..................................................... 88

12.2.6

Managing road pricing and congestion charges ...................................................................... 88

12.2.7

Update land use policies and zoning in order to discourage sprawl. ...................................... 88

13 Bibliography ............................................................................................................................................. 90 14 Appendix .................................................................................................................................................. 97

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Figure 1: Evolution of technology for automation (Boston Consulting group) ................................................. 2 Figure 2: Pace of technology adoption speeding up (Ticoll, 2015), Page 19 ..................................................... 3 Figure 3: Development of Autonomous vehicles over time (Issac, 2015), page 23 .......................................... 4 Figure 4: Timeline and level of adoption for AVs .............................................................................................. 6 Figure 5: Predictions by different sources (Ticoll, 2015), page 18 .................................................................... 6 Figure 6: Responsiveness of local governments for AVs in local plans (DuPuis, et al., 2015) ........................... 7 Figure 7: Readiness index for countries for Driverless cars (Threlfall, 2018) .................................................... 9 Figure 8:EV sales for different countries & average cost of batteries (MC Kinsy & Company, 2016) page 16 10 Figure 9: Electricity demand for EV Vs. total demand (MC Kinsy & Company, 2016) page 17 ....................... 11 Figure 10: Different levels of automations NHTSA .......................................................................................... 13 Figure 11: Global market for automated and autonomous driving including related services in( $billions) (Rรถmer, et al., 2016) page 9 ............................................................................................................................ 14 Figure 12: Cost of introducing automation by different companies ............................................................... 14 Figure 13: Global self-driving minutes saved (Rรถmer, et al., 2016) ................................................................ 15 Figure 14: Use of car vehicles by time (hours oer day) (BCS car sharing, 2009) page 29................................ 17 Figure 15:Pillar of MaaS strategy KPMG 2018................................................................................................. 22 Figure 16: Components of Mobility as a service (MaaS strategy) ................................................................... 23 Figure 17: Increased efficiency of a taxi system with algorithm. Carlo Ratti ,2018 ........................................ 28 Figure 18: Trip generation, fleet distribution and parking demand for a Master Plan in RIyadh (work done with Systematica S.r.l, 2017) ........................................................................................................................... 29 Figure 19:Infographic on a study done in Lisbon. (Urban Land institute, 2015) ............................................. 30 Figure 20: Robotic shuttle in Nishaka town, Japan, Photo taken on 8 September, Reuters /Issei Kateo ....... 32 Figure 21: Driverless school buses for young children in the neighbourhood areas ...................................... 33 Figure 22: Consumer readiness to driverless cars (Boston consulting group) (Lang, et al., 2016) ................. 35 Figure 23:Average age oftruck drivers in USA, ATG, 2018 .............................................................................. 37 Figure 24: On street parking in a European city .............................................................................................. 41 Figure 25: Fleet and Parking management strategy bu understanding the demand of CAVs all throughout the day ............................................................................................................................................................. 42 Figure 26: Active fleet and passive fleet strategy ........................................................................................... 42 Figure 27: Business as usual parking structure with standard parking isle and parking bays (Source: Mobility in chain parking study) .................................................................................................................................... 44 Figure 28: Assisted parking feature with cameras and easier manueveribility in tight space (level 2,3) automation, (source: Mobility in chain Parking study) ................................................................................... 45 Figure 29: Fully functional piloted parking , no human assistance required within the property of parking structure ,(source: Mobility in chain, parking study) ...................................................................................... 45 Figure 30:Complete autnomous scenario level5 automation, single ownership fleets .................................. 46 Figure 31: Series and parallel drop offs (source : Mobility in Chain, Parking and drop off study) .................. 47 Figure 32: Autnomous Parking of future (Arrow street architects) ................................................................ 48 Figure 33: Piloted parking AUDI future initiaves ............................................................................................. 49 Figure 34: Efficient use of land use and property value (AUDI urban initiatives) ........................................... 50 Figure 35: Autonomous buses by mercedes ................................................................................................... 52 Figure 36: Autonomous bus by Japan basd venture capital between SB bank and Yahoo ............................. 53 Figure 37: Autonomous shuttle system EZ10 .................................................................................................. 55 Figure 38: Cost of Private cars (traditional) and Autnomous cars with sharing (Fixed cost vs Operating cost), KPMG white paper 2015, page 14 ................................................................................................................... 59 Figure 39:Personal Transport cost comparison ............................................................................................... 61 Figure 40: Cost of congestion in different countries (Centre for Economic and business research , 2014) page 60-67 ....................................................................................................................................................... 63 Figure 41: Cost of an electric truck (Tesla ) vs traditional diesal driven truck ................................................ 67 viii

Figure 42: Cost of platooning electric trucks vs Diesal trucks ......................................................................... 67 Figure 43: Currents accidents vs Future accidents with driverless cars .......................................................... 69 Figure 44: Distribution of deaths related to accidents by continents (WHO, 2015) ....................................... 72 Figure 45: an illustration of for pedestrians in the era of autonomous cars .................................................. 73 Figure 46: An illustration of a communication device for informing driverless cars about the intention of pedestrians ...................................................................................................................................................... 74 Figure 47: An illustration of gesture and hand signals for the driverless cars to stop .................................... 74 Figure 48: Concept structure of a city in the era of driverless vehicles .......................................................... 77 Figure 49:Vision of future cities as given by Mckinsky & Co. .......................................................................... 82 Figure 50:Vision for future of Singapore (Ministry of transport) .................................................................... 82 Figure 51: Vision of future of cities by Mathew Spremuli (https://www.treehugger.com/cars/our-streetsmay-be-clogged-self-driving-cars.html)) ......................................................................................................... 83 Figure 52:WSP Parkinson Brinckerwells Farrels .............................................................................................. 83

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1 Introduction Throughout the history, humans have used various means of transportation to transfer goods, knowledge and people across the boundaries. Historically there are two ways to transport humans and cargo either through land or through water. In the recent times, air transport had also been an integral part of human life and it is a very common knowledge.

But in the past 150 years cars or road vehicles have taken precedence in comparison to any modes in terms of individual travel mode all across the world. The number of cars in the world have increased drastically post world war era.

Over the years we have changed the cars according to our needs and wishes from 1960s classic models to modern day SUVs we have experimented and augmented the cars to an extent that it has become a part of human travel behaviour, travelling in a car is obvious as well considered as a mandatory item to own in modern lifestyles (there are many exceptions to this case). We have introduced various luxury as well safety features in the car to make it more desirable; dynamic lights, stereo options, safety features for drivers (air bags, protective body) and passengers alike. One thing we have not been able to change or replace in this whole equation is the driver.

Firstly, every year nearly 1.3 million people die due to a car accident and more than 20million are injured or are disabled for life (Association for Safe international road travel, 2002-2017). Research’s claims that almost 94% of these accidents are caused due to human induced errors. (South Side Injury Attorneys, 2016). Humans are often tired, in effect of an alcohol, distracted, stressed or miss some details to judge the distance and speed; hence cause an error. Human behaviour like anger and recklessness also causes one of leading cause in such incidents.

Secondly, a car remains parked more than 95% of time, average usage is 1.5-2hours (Rajasingham, s.d.); even a lay man would understand that we are not efficient in how we are approaching our policies that regards to our usage of the vehicles and its parking. Fewer than 17% of household vehicles in the US are in use at any specific time in a typical day (Ticoll, 2015). Furthermore, a person spends almost 17 hours a year trying to find a parking spot and as high as 30% of traffic can be a recirculating traffic during the peak hours, which a matter of concern for traffic planners and mobility experts (Pawel Gora, 2016). But this scenario can change a lot if we have efficient systems to park our cars and each car knows where other cars are moving and there are ways in which we can make these processes efficient and less time consuming.

We often do not look at how much space parking has taken up in our cities, some estimates suggest it can be as high as 25-30% in some of the cities (gardner, 2011), for a country with 250million cars estimates suggest there are 800 million car park places (3 for each car owned), does not seem far from reality and practice. One car is parked at home, office and leisure activities. We are taking up so much space just for the matter of convenience and it has long lasting environmental concerns. (Makinen, 2017) Thirdly, there is one car for every kind of use like going to office, grocery shopping and taking kids to the beach. This is one of the reasons why families have started owning more than one car as purpose of trips is different for different people but the vehicle is same. Hence from the research we know that families in the developed world and especially in USA own just too many cars and each of these cars need to parked at least 3 places, work, home and on streets for leisure and shopping. Only 30% of space is taken up by cars that are parked and the rest 60% of these spaces are vacant without a car. We can already observe significantly the early signs that the importance of private car ownership is declining and shared mobility is increasing. In the US, for example, the share of young people (16 to 24 years) that hold a driver’s license has dropped from 76 percent in 2000 to 71 percent in 2013, at the same time the number of car sharing

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members in North America and Germany has grown by more than 30 percent annually over the last five years. (Mckinsy & Company, 2016) Lastly, the problem of last mile connectivity is something transport planners and engineers have battled for decades since the start of Public transport revolution. In the recent times, we know that in the dense urban fabric, we have different hierarchies and capacity of transport that serves our network but the basic underlining argument is that Public transport will never feed everyone with the same kind of convenience and frequency, hence last mile connectivity is very important in order to provide accessible mobility options for the masses. In order to curb this problem there are numerous dock less bike sharing, free-floating car sharing system, on demand car and ride sharing have been introduced in the recent years. But these systems have an inherent problem which relates to the fact that these systems are usually calibrated from a supply stand point and demand never reaches supply in an efficient way. One of the reasons is because humans are intertwined in the system and creating a model that is based on supply inherently leads to oversupply like it is happening in the case of Bike sharing companies that are fighting for a limited market space. Many cities in the world are facing the problem of oversupply of these free-floating bikes and in the end, they end up in the scrapyard.

So, what happens when this human factor is removed from the equation and given it to a machine that can sense day and night, in good weather or bad weather, never gets tired and always has full attention that it is programmed to do. This description is no longer a science fiction as in the recent times with the advancement of computers and especially programming methods like deep learning a machine is able to identify patterns, take decisions quicker, and in a better way than a human can. With increasing cognitive abilities in machines and especially machine learning and deep learning algorithms, we are reaching a point when Autonomous vehicles are a reality and it is just a matter of time when it hits the roads and start having impact on our lifestyle and our urban environment.

Autonomous vehicles will provide seamless resonance between the demand and supply paying respect to the geography and city. If certain threshold of cars need to be there in an office complex than it will be there, it doesn’t need to be stored or parked in the nearby location of city centre hence the space is optimized. But in this positive outlook there are many challenges and negative aspects that needs to be considered. Now we have one car even with minimum occupancy it at least has one person riding it. But what happens if a traffic of empty cars will line up and make the traffic of future far worse than it is now. In order to avoid such things a careful consideration of Policies integrating other modes of transportation need to be embedded in local planning process.

“As one of the sprinters at the London Design Sprint said, ‘our minds are still thinking too much in the now’.

This might be because it is hard to predict human behaviour. A look into the past, and the impact that subservience to the motor vehicle has meant for many of our current cites, shows us that we cannot blindly embrace the new technology; we need to (re)build our cities with foresight and a plan to capture and capitalise on what the future technology will offer”. (Global design Sprints (Sumary), 2017) The aim of this research is to understand and map what will be the impact of these autonomous cars on our experience of urban life. How will our travel behaviour change if we no longer need to stress ourselves from driving 2 hours a day? How will the trip generation affect when there is a flexibility to the car occupancy and sharing a car is more easy and affordable option then owning one. What will happen to all the parking lots in and around our cities, which might no longer be needed, how it will change in terms of space allocation and functioning? The streets and neighbourhood might work completely different then they do now, there might be a need of more drop offs and less parking? Can the modern-day parking spots be converted in drop offs? What will happen to the sprawl in the urban areas and beyond? Will the mega polis extend its boundaries or there will be new hierarchies that will form within hyper accessible centres and connected suburbs? Will we see compact cities that works on the principle of hyper accessibility or we 1

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will have ever growing suburbs and the difference between the suburbs and rural areas will further reduce.

This research tries to understand and partially answer some of these questions that have surrounded our thought process about Autonomous vehicles in recent times.

The approach of the research is not to speculate but rather to understand different possibilities that might arise due to different preferences and Policies of the government and agencies. We should be well aware of different ways this technology will influence our lives, hence be prepared for it. The study aims to peep into next 2-3 decades about how the mobility will change in the light of AVs and how will cities need to transform in order to assimilate with the growing technological advancement and changing human mobility behaviours. Time is right for the planners to take into account different technologies that change human behaviours and hence the city structure.

1.1 Historical development The biggest challenge or speculation around the implementation of AVs is the time when it will be available in the market and how far will it go as an impact on our transportation practices. In the following paragraphs, we will discuss the timeline and projections made by different parties and stakeholders for the implementation of AVs. It is important to understand the general trend in terms of sales of AVs vs. normal cars and when will the technology mature enough to have a considerable impact on the transportation of the future.

Experiments with autonomous and connected cars have been conducted since at least 1925 (Radio Auto

Figure 1: Evolution of technology for automation (Boston Consulting group)

(1925)), when a driverless car was driving on streets of Milwaukee. However, the car was not truly autonomous, it was radio-cooperated from a second car (this could be considered as a case of V2V communication). The first truly autonomous cars, capable of driving without any human intervention, appeared in the 1980s, being results of pioneering research work conducted by the team of E. Dickmann (Mercedes-Benz van with cameras, sensors and sophisticated computer vision strategies, Dickmann and Zapp (1988)) and, independently, by the team from Carnegie Mellon University (projects NavLav and Autonomous Land Vehicle, Kanade et al. (1986)). Even these first approaches, designed in the era of computers with lower computational power than nowadays, were based on very advanced tools and concepts, such as lidar (remote sensing technology which measures distance by illuminating a target with a laser and analyzing the reflected light), transporter (microprocessor designed for parallel processing), Kalman filters, neural networks. Nowadays, many major automotive manufacturers are testing driverless cars technology. Also, IT companies (e.g., Google AV (2015), Apple titan project (2015)) and research institutes (e.g., VisLab (2015), Oxford University (2015), FUM (2015)) are working on their models of selfdriving vehicles, including also electric power supply. In some U.S. states (e.g., Nevada, Florida, California, Michigan) autonomous cars are already permitted (NCSL (2015), UoW (2015)). Some other countries have

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Figure 2: Pace of technology adoption speeding up (Ticoll, 2015), Page 19

allowed testing autonomous cars in traffic as well. There are interesting projects aiming to demonstrate autonomous vehicles potential, e.g., CityMobil2 (2015), Beta City Initiative (2015).

This is development in car technology is partially the result of development in the technology of integrated circuits. According to Moore law every two years the transistors in a dense circuit doubles and hence the computing power in the same space. And as the technology gets more and more used the cheaper it becomes in the market; this fact does need any explanation as it can see in all the modern-day electronics for phones. TV, computers and also industrial grade equipment. (PLC). (Ticoll, 2015) One other notable discovery is the advancement in machine learning and deep learning algorithms. This in recent times has allowed us to develop systems that can sense and learn on its own. Deep learning algorithms use imagery in order to take decisions on its own by processing and computing the big data. This type of technology is very crucial in Autonomous car technology as most of the sensing is done by visually registering the environments through different sensors (LIDAR). Furthermore, as it will be discussed in the later sections the drive towards electrification and sustainable energy in motoring and the levels of automation as a result of the technology mentioned before will provide a great budding environment for AVs to be cost effective, convenient and safe for the urban environment.

It has been said in a lot of popular research and opinion that now the discussion surrounding the AVs is not “if” but “when”. To most of technologist and Transport research organization this seems inevitable and hence there is a pressing need to accommodate and discuss the possible effects it can have on urban environments and future of mobility.

After discussing the technological implication for the deployment of AVs it is also important to understand and evaluate the consumer readiness for accepting AVs as a mode of transportation. The question also revolves around the fact that people are concerned with their safety within the vehicle and perceived reliability. It is very interesting to know that The Boston Consulting Group and the World Economic Forum (the Forum (Lang, et al., 2016)) conducted qualitative and quantitative research among more than 5,500 consumers in 27 cities in ten countries—to date, the largest survey fully dedicated to autonomous driving.

From New York to Kolkata, Berlin to Beijing, consumers are surprisingly and remarkably knowledgeable about SDVs and their potential benefits, and, by and large, they are more than willing to give them a try.

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Figure 3: Development of Autonomous vehicles over time (Issac, 2015), page 23

Overall, 58% of respondents said they would take a ride in an AV, and 69% said that they would take a ride in a partially self-driving car. Willingness is highest among younger consumers—63% of those aged 29 or younger are willing to ride in an SDV compared with 46% of consumers aged 51 or older. In the research it was also found that Asian consumers are most willing to adopt the AVs. The cities and regions where the problems of congestion, pollution and inconvenience is greater the more likely the users are read to adopt new ways to solve the problems. But in the Asian cities the level of investment on infrastructure and other unforeseeable effects like employment are to be well thought before pursuing the grand experiment.

(Lang, 2016). Most of the users in the survey expected that AVs will be either hybrid or electric which we previously discussed regarding the three important revolutions in the field of transport.

It becomes crucial to understand the kind of companies that are able and willing to provide these AVs in the market. There are many players in the market of AVs which we can see and numbers are growing with each passing day. The investment in Automation and electrification has increased many folds in past years and it is likely to increase in the coming years. There are majorly 2 categories of Players in the market.

First group of companies are the traditional OEMs who want to adopt automation in phases in order to keep their customers loyal who will still own these vehicles. Hence companies like Nissan, Daimler, Ford, GM etc. are trying to move from partial automation to complete automation. Partial automation feature includes parking assist, lane assist and Autonomous driving on the freeways etc. In this approach the assumption is that human drivers will be able to let go of the vehicle in certain areas like freeways and highways and parking in a basement etc. and ultimately leading to complete automation when humans are not required to take control under any circumstances. In case of partial automation, the human driver needs to be in full consciousness in case an emergency arises. It has been talked about in many forums and 4

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research’s that human drivers is unlikely to respond in the most efficient way once he is distracted for a long time. Imagine a situation on highway where the human drivers are not attentive and there is an emergency or an accident where human driver is supposed to intervene, the chances of a casualty increases multi fold in this kind of situation. It is debated that when human is not attentive for 75% of time than for the rest 25% of time he/she will not be able to make a crucial decision and over the period of time the skills for driving will deteriorate.

Second group of companies are the ones that are majorly software providers in the current context and want to provide the best software that will be able to automate the car. The Google self-driving car is in the prototype stage as of 2014 and adding more miles each passing day. The vehicles are projected to bring in an additional $80B in revenue by 2030. Additionally, 25% of cars will be self-driving by 2030. The new entrant, Google, is expected to capture 8% of the total car market by 2035 (Jiang, et al., 2015). Big tech giants like Google (AVs), Apple (titan project) and Uber are partnering with OEMs in order to achieve the best results. In this approach the aim is to achieve complete autonomy as fast as possible and make it safer than human drivers and have the fleet of these vehicles for various purposes. It can be an on-demand service or a car sharing kind of service. These cars are less likely to be privately owned. The companies in the first category will still try to keep the ownership of these cars to individuals and hence have a limited sharing feature like Teslas (people can choose if they want to share the cars with other while they are parked and they get some benefits for doing such action). But the second group of companies will try to move towards an idea of selling mobility instead of vehicles and hence they do not need people to buy cars and they are more likely to work under an umbrella ownership of different parties rather than individuals.

1.2 Timeline and consumer readiness for AVs in market There is a big uncertainty regarding the timeline when the AVs will become relevant among the academic community as well as for Policy makers who want to steer this revolution so that they can maximize the benefits from it. Of the all the uncertainties that surround this topic there are couple of things which are more relevant and predictable. AVs will become a revolution or not in all the context remains a question but an attempt from the technology providers is a serious one unlike any other revolution in past. The amount of money invested in this project worldwide is of considerable amount as well as players who are in it. So, it’s not the question of “If” but “When”. Also, it is to be noted that the trends of similarity will be highly concentrated on how the cities are organized rather than nations. Cities in a way will diverge from the nation narrative which has been the building block of various revolutions in past. So, New York will be more related to Shanghai than to let’s say Kansas City or Houston. This trend is already present but this similarity is likely to get more real in urban planning and mobility perspective. So, when it comes to policies it will be cities deciding most of it under a very broad umbrella policy of nations. This is going to percolate in a most of the nation states.

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Figure 5: Predictions by different sources (Ticoll, 2015), page 18

In the illustration it can be seen that various market research groups have predicated the relevance of AVs in the timeline. There are some differences when it comes to different agencies but one thing is common that makes next 30 years very crucial and tide changing in terms of how our mobility and cities will be organized.

Figure 4: Timeline and level of adoption for AVs

From all the multiple sources one thing is clear that the next 3 decades are going to be very crucial on how we deploy this technology (MC Kinsy & Company, 2016). A progressive scenario could see ~50 percent of passenger vehicles sold in 2030 being highly autonomous and ~15 percent being fully autonomous (Mckinsy & Company, 2016). There will obviously be early adopters and cities or regions who will be better at integrating this technology in the future mobility plans as compared to others. The year 2020 is likely the time when a lot of automakers are releasing the cars with partial automation features the leading ones are Tesla, Ford, General Motors, Audi. The automations features are likely to improve and reach full autonomy within the next decade (Issac, 2015).But on the contrary as discussed before tech giants and sharing app companies like Google, Apple , Lyft and Uber are likely to make the jump with using technology and making available fully autonomous cars within the next decade. Recently Uber CEO commented that no matter how much we try driverless cars are at least one decade away (Titcomb, 2018). In this category Google has the most experience in terms of Km travelled and hence has a better chance to throw it to the market.

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Introduction

After driving more than a million miles Google Waymo project seems to be getting close to introducing the product in the market after legislative hurdles are met with. But all in all, Driverless cars will be available in market in the next decade that when it will start to penetrate the developed markets. The projections made by various sources claim that exponential boom in the sales will start after 2030 and till 2040 almost all the mobility will be done by driverless cars or human drivers will be completely phased out. In the case of the developing countries the same exponential growth starts at around 2040 and extend it to 2050 when it finally matures. In terms of market penetration in the developed world by 2050 it will be most likely be reaching 100% while in the developing world due to income inequality and other factors this saturation is likely to be around 75% may be even less since we are talking about an array of different countries with different political and economic systems.

Japan and Singapore have been quick to implement measures to facilitate the transition to automated vehicle technology in the Asian market. Japan has been issuing special license plates to allow testing of automated vehicles on public roads since 2013. In 2016, the Japanese Prime Minister committed US$16.3 million per year to develop maps and other technologies necessary for automated vehicles, with the view

Figure 6: Responsiveness of local governments for AVs in local plans (DuPuis, et al., 2015)

of having automated vehicles on public roads by 2020. Singapore has recognized automated vehicles as the “future of transport� and anticipates fully automated vehicles becoming viable by 2030. Testing of fully automated vehicles on public roads in Singapore has commenced in 2016. Toyota has recently advertised its interest in fully automated technologies after almost half a decade of remaining silent on the issue. In November 2015, Toyota publicly announced that it would invest $1.8 billion to study fully automated vehicle technologies. Further, in April 2016 Toyota released a statement asserting its long-term commitment to fully automated vehicles. (Hayford, et al., 2016) 7

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With this being said it is near to impossible to predict the future trends with absolute accuracy but it is possible to predict it to an extend that some fundamental changes can be made in the ways in which we organize our mobility needs and cities.

One of the important aspect in the adaptation of AVs is that modern vehicles are durable, resulting in slow fleet turnover. Median operating lives increased from 11.5 years for the 1970 model year, to 12.5 years for the 1980 model year, and 16.9 years for the 1990 model year (ORNL 2012, Table 3.12), suggesting that current vehicles may have 20 year or longer average lifespans. As a result, new vehicle technologies normally require three to five decades to be implemented in 90% of operating vehicles. Deployment may be faster in developing countries where fleets are expanding, and in areas with strict vehicle inspection requirements, such as Japan’s shaken system. Annual mileage tends to decline as vehicles age. For example, 2001 vehicles averaged approximately 15,000 miles their first year, 10,000 miles their 10th year and 5,000 miles their 15th year, so vehicles older than ten years represent about 50% of the vehicle fleet but only about 20% of vehicle mileage. (Litman, 2014) So the next generation of AVs should have a shorter life span for a better and faster adaptation curve. This raises the question regarding the environmental effects of having AVs, more cost of materials lower life span leading to faster production all over the world.

Depending on the market readiness and adoption rates of AVs, the transition period, which is likely to begin in the next decade, may last until 2040-2050. Full saturation may occur sooner if the safety and mobility benefits of AVs inspire governments to mandate their use or consumer take-up is faster than expected.

Pressure is likely to increase over the next 15 to 20 years to restrict the operation of current generation vehicles to realise the full benefits of vehicle automation. Managing the transition period with a mix of AVs, or high-level AVs and current generation vehicles with no AV capabilities, will create challenges for road agencies. (Sommers & Weertunga, 2015) In a report published by National league of cities where they surveyed Government Plans across USA and its readiness to technology and autonomous vehicles, the results are interesting (DuPuis, et al., 2015).

The results from this study shows that not many states are what can be termed as consumer ready for the self-driving cars. There needs to be infrastructure that needs to be put in place along with the legislation that needs to be improved in order to improve the readiness of consumers in different markets.

There is a comprehensive study done by KPMG which rates the consumer readiness of different countries for the advent of connected and autonomous vehicles. There are many implications that comes with the same fact that a change in our socio-economic composition and the advent of driverless cars are something that is inevitable.

The Netherlands is the clear leader in this first Autonomous Vehicles Readiness Index. It is within the top four of each of the four pillars and ranked number one on infrastructure, most likely due to its heavily-used, well-maintained road network, rated as being among the world’s best by the World Economic Forum and the World Bank.

It also has by far the highest density of electrical vehicle charging points, with 26,789 publicly-available points in 2016 according to the International Energy Agency’s Global EV Outlook — more than Japan has for a road network more than eight times the length. The Netherlands also has high-quality wireless networks too. On technology and innovation, the country has by far the highest percentage usage of electric vehicles of the 20 countries in this index at present — 6.39 percent in 2016 according to the International Energy Agency, nearly double second-placed Sweden — and has a high number of AV companies based in the country on a population-adjusted basis.

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The Dutch ecosystem for AVs is ready. The intensively-used Dutch roads are very well developed and maintained and other indicators like telecoms infrastructure are also very strong. In addition, the Dutch government Ministry of Infrastructure has opened the public roads to large- scale tests with self- driving passenger cars and Lorries. (Threlfall, 2018)

Figure 7: Readiness index for countries for Driverless cars (Threlfall, 2018)

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3 important revolution in Mobility

2 3 important revolution in Mobility In order to better understand the impact of AVs it is important to capture and understand what is happening in general in the field of transportation and where are we headed towards. Many experts believe that there are three important revolutions which will impact the transportation of the future, these are Automation, Electrification and Sharing. We will discuss about them in detail in the later sections.

‘The world is on the cusp of three revolutions in transportation: vehicle electrification, automation, and widespread shared mobility (sharing of vehicle trips). Separately or together, these revolutions will fundamentally change urban transportation around the world over the next three decades.’ (Lew Fulton UC Davis, 2017) These revolutions in different forms and proportion can give rise to multiple scenarios which are likely to take place in future and will drive the mobility of future. The interesting fact about these revolutions is that each of these can occur in part and independently as well as can occur in conjunction with different prospects and scenarios.

2.1 Electrification Electric cars have been around in this world since 1970s but there was always limitation in terms of technology and performance, which did not lead to a mass revolution. In addition, there was pressure from combustible engine companies and practically no concerns for the environment. Now we have better technology and performance, which might even surpass that of some Petrol cars. One of the pioneers in this field is Tesla who have been able to revolutionize the market of electric cars with putting in the market model S which is a sports car and one of the best in the performance in its price range and hence the stigma (electric cars are not attractive and cannot achieve the same speed and comfort as a normal car) around the electric has been broken. Now many conventional car companies are getting themselves to invent better technology in order to run the electric cars and bring them in the market.

Global electric vehicle (EV) sales have risen quickly over the past five years, fuelled by generous purchase subsidies, falling battery costs, fuel economy regulations, growing commitments from car companies, and rising interest from consumers. Sales rose 60 percent in 2015 alone to nearly 450,000, up from 50,000 in 2011. The average price of lithium-ion battery packs used in EVs fell 65 percent over the period 2010–15 – from $1,000/kWh to $350/kWh – and continues to drop, driven by scale, improvements in battery chemistry and better battery management systems. (MC Kinsy & Company, 2016)

Figure 8:EV sales for different countries & average cost of batteries (MC Kinsy & Company, 2016) page 16

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Today there are more than 100 models of electric car available all across the world in different markets and the number is increasing more than ever. Every day there are new announcements by many major players of the market for a new electric car. In 2018, there are many 300+ km range EVs are introduced by many companies, opening a complete new market for long range EVs and hence the network of charging stations and battery replacement stations that follow it. (Lew Fulton UC Davis, 2017) Also with the introduction of fast charging DC stations the recharge time has significantly reduced, now typical recharge might only take 20-30min and would run for more than 200km .But still the global share of EV is less than 1% currently so it might not be as optimistic scenario as it sounds like in first place but nonetheless, the shift away from the traditional cars seems evident in the near future.

Global and national fuel economy regulations are playing a major role in pushing hybridization and electrification of the vehicle fleet: The United States, the European Union and China in particular have set aggressive targets for automakers to meet. Tesla’s Model 3 launch in 2016 attracted over 400,000 reservations and deposits for a vehicle that will not be available until late 2017 at the earliest. (MC Kinsy & Company, 2016).

Figure 9: Electricity demand for EV Vs. total demand (MC Kinsy & Company, 2016) page 17

Charging Infrastructure The number of charging points and stations are increasing day by day. There will also be a need to upgrade the grids of this charging infrastructure once the demand rises above a certain threshold. Another major invention in charging the EVs are the solid-state batteries which currently Toyota is working on to put it out in the market in 2020. The research aims to provide successful ways to mass-produce these solid-state batteries which will be more efficient then lithium ion which are currently used.

‘Solid state batteries are no fire hazard, they promise to recharge faster, store more power in a given volume, and, especially interesting for automobile engineers, they can be moulded into many shapes’.

(Schmitt, 2017) ‘Among the many examples, the 2017 BMW i3 EV with a 94 Amp-hour battery offers 114 miles of electric range, up from 81 miles in the 2016 model. Notably, the 2017 Chevrolet Bolt EV offers 238 miles of range, a substantial improvement over the 82-mile range of the smaller 2016 Chevrolet Spark EV’. (Lew Fulton UC Davis, 2017) 11

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In the past decade, the cost of renewable power generation has decreased significantly. If costs continue to decline rapidly, intermittent distributed generation, as well as batteries and demand response assets, will play an important role in global power-generation over the next 15 years. This trend could accelerate electric vehicle uptake by lowering power prices at peak times and resolving specific grid issues that currently inhibit electric vehicles locally. In some cases, it may establish a maximum price that consumers will pay to charge their vehicles. Managed EV charging can also prevent curtailment of renewable generation during peak hours. (MC Kinsy & Company, 2016) Since we are talking about the technological progress it is important we understand it is not linear but rather an exponential. According to the Moore Law when technology becomes widely used it becomes cheaper and it has been true in many historical examples. Just to give an example it took 50 years for the car to reach 50million users and it took Uber some months to reach the same numbers of users. There are many success stories for car sharing companies using EVs like Drive now, and Daimler’s car to go that have started using EV fleet for their car sharing program. EV combined with car sharing has multiple benefits as well as challenges. Benefit being low maintenance, environmentally friendly and challenges as to need of a dense charging infrastructure available at all times and also user willingness which has yet to be studied and quantified. Typically, a shared car has the potential to replace somewhere between 5-15 cars (American context) in a neighbourhood but there has not been a considerable reduction of number of conventional vehicles. A lot of people have given up their second cars and also young people are refraining from buying new cars as it is huge financial commitment. (Bondorovå & Archer, 2017) There are many challenges in increasing the charging capacity which is technical problem. One charger DC (Super-fast) needs somewhere around 100kwh to charge a car with 300 km capacity in under 40min. There are more advanced and fast charging infrastructures that are coming up in order to provide better service to people for long range driving. One of the examples of the infrastructure crunch for EV charging was when there were 5 Teslas queued up for one charging spot in Switzerland (Wenger, 2017). So, the wait time in dense areas especially in Europe is one of the challenges for EV charging. Also Tesla recently opened a charging station with 50 super chargers, this has a great impact on grid and this is in response to many of this lone incidents of low LOS for charging infrastructure when it is crowded especially in high density EV ownership cities and regions. (Lambert, 2017) There are many partnership that are developing between competitors in automobile market in order to provide for the EV infrastructure service e.g. joint venture, named IONITY, draws on expertise from BMW, Daimler, Ford, Volkswagen, Audi and Porsche, and aims to launch 400 HPC stations by 2020 (England, 2017). Traditionally all these automakers would not partner for a common market but the EV charging infrastructure is such a herculean task that in order to provide customers satisfaction and service of highest level there are many unconventional partnerships developing in the wake of losing the market to new players like Tesla. Another such partnership to be noted is Shell (Oil corporation) buying the Newmotion (EV charging infrastructure providers in Europe) and Shell has attempted to go for complete and all electric stations in UK understanding the market potential and threat to oil becoming less relevant fuel of future. (Hanley, 2017) To conclude, it can be said that apart from EV car sales and technologies there are many hidden not yet explore challenges revolving around the adaptation of electric cars. One of the most notable concern is decarbonisation of grids. If the electricity produced to charge and use electric cars comes from nonrenewable sources than the environment benefit that electric cars promises to offers might not hold true entirely. There are many researches that draw the concern of lithium and cobalt poisoning in the environment as a direct result of increased EV sales. The future about EV adaptation is still uncertain but once we reach a threshold it is important to gauge the effect of these cars in the wider context of urban planning.

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2.2 Automation "The creation and application of technology to monitor and control the production and delivery of products and services.” (International society of automation, s.d.) is termed as automation.

Since industrial revolution, the word “Automation” has been around and has affected our lives in many ways. Human being as species has two major abilities, which help us to find employment. The first being the physical abilities (to work, lift) and second is the cognitive abilities which help us take decision and analyse any situation with given facts etc. Since industrial revolution of the 19th century was focussed on automation of our physical skills as machines were able to do things much faster and with better accuracy then humans ever could. But when we are referring to the automation in this context it is related to autonomous vehicle, it is not just the physical ability of moving from one place to another that is being automated but also cognitive and decision-making ability, when to turn, when to accelerate, when to drive fast and when to drive slow and when to stop etc. The machine can understand the situation better than humans and it consistent with results without any errors caused due to external factors. In the past decade there has been a huge hue and cry about the automation in the popular literature but full automation that is reliable and tested is yet to be seen. But this is not to say that automation is at its primitive stages.

Figure 10: Different levels of automations NHTSA

There are many players in the market that have already started testing AVs on the roads and there are many success stories one of which id Google AV project having travelled more than 1.6million miles, it is just matter of time before which this technology will be in the market for customers.

There is also a big difference between automated and autonomous. As discussed before Automated means that there is a certain level of autonomy in the vehicle from the traditional OEMS which can include Piloted parking, lane assist etc. But it will largely need a human driver in all the complete senses and ready to take over in an event of failure or emergency. On the other hand, autonomous refers to a complete autonomy and hence does not need human intervention at any stage of driving. Crucial part of this automation revolution in transport will not just lead to self-driving cars but also Trucks, buses and metros (some of which are already in place in some cities).

The U.S. Society of Automotive Engineers (SAE) (U.S. Department of Transportation, 2016) defines a full range of automation levels (SAE, 2016). Level 1 is widespread and level 2 is rapidly being introduced in many models. Level 3, including hands-free driving, is just emerging and only legal in some areas. Levels 4 and 5, with true full-time driverless operation, is not known to be fully legal anywhere in the world as of early 2017, except for operation by test fleets. (Lew Fulton UC Davis, 2017) 13

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Currently AVs are in the testing phases and are implemented in various stages to provide mobility options.

In the present times AVs can function very well in controlled environments. By that it means routes are fixed, lanes are reserved, external traffic is much more prediticable.eg fixed routes of College campuses National University of Singapore have implemented driverless vans with low speed as 10-20km/hr within their college campuses. Currently a company based in France called Easy Mile(EZ10) is already manufacturing Driverless shuttles in various parts of the of the world in Pilot stages (Mile, 2017). They have successfully piloted more than 10 projects all around the world with different contexts and have been quite successful in making the concept popular for public. The shuttles operate in fixed route and in a controlled environment where traffic is predictable but recently a project is launched in Taipai China that will bring them to roads in a more complicated driving environment (Today Weekend, 2017).

Figure 12: Cost of introducing automation by different companies

Figure 11: Global market for automated and autonomous driving including related services in( $billions) (RĂśmer, et al., 2016) page 9

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To further the discussion about AVs making to the roads is whether our roads of today are ready for the having AVs in near future or not. According to the report published by National league of cities (DuPuis, et al., 2015), there are only 6% of surveyed cities (mostly American cities) have included AVs in their future plans. There are majority two lines of thinking in this regard. First being that AVs will need special sensors and infrastructure on the roads which will help them sense the environment and take decisions. Second is that AVs will have all the sensory abilities within the car (advanced LIDAR and Artificial intelligence required to take decisions independently) and hence will not require any other additionally infrastructure but still the discussion regarding the traffic lights and pedestrian crossings is still open to debate. It is safe to assume that automated cars and manually driven cars will share the same road space in the next 2-3 decades. Crucial part of this automation revolution in transport will not just lead to self-driving cars but also Trucks, buses and metros (some of which are already in place in some cities).

Autonomous market is a lucrative market. All told, the apps, equipment, and vehicles related to autonomous driving will pull in $282 billion in revenues by 2030, which represents about 7 percent of the total automotive market (see figure 3 on page 9). The numbers get much bigger from there. We expect the market to almost double to around $560 billion between 2030 and 2035 and represent 17 percent of the global automotive market. And this estimate does not even consider ancillary revenues from mobile apps, which will form the basis of many in-car services. By 2030, traffic management systems will free up a mindblowing 1.9 trillion minutes for passengers, most of whom already have a smartphone within arm’s reach at all times (see figure 4 on page 9). The competition for drivers and passengers will be fierce. (Römer, et al., 2016)

Figure 13: Global self-driving minutes saved (Römer, et al., 2016)

Automation coupled with Electrification will have a major impact on how the revolution will come out to be. It is safe to say that development of one will affect the progress of other and hence it is important to take a close look at the trends. Vehicle automation is certain to radically transform how people and goods move in different parts of the world. The list of expected changes is long: traffic volume, average speed, 15

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travel time reliability, safety, mode mix, congestion, parking, land use, travel cost, emissions, energy consumption, and other environmental and social effects. There will be some we have not yet imagined.

(Grush, et al., 2016) Nevertheless, it can be said that the hype in the market at least for Western countries is mainly fostered by a couple of companies such as Google, Tesla and Uber. In China, ride-hailing service did just pushed Uber out of the competition and received an investment of $1B from Apple (Wakabayashi and MacMillan, 2016).

Google’s competitor Baidu plans to release autonomous cars on Chinese roads by 2021 with tests already taking place in Beijing (Korosec, 2016b) and soon in California (Hawkins, 2016b). Furthermore, Google’s Russian competitor Yandex just announced a cooperation with Daimler and truck manufacturer KAMAZ to develop a self-driving. (Hörl, et al., 2016) The shift and the market vibe seem to say that autonomous vehicles will have some footing in the next years and it is just a matter of time that AVs will be available for customers ready to pay for it

2.3 Shared Mobility Shared mobility is a very hot topic among Mobility and transport experts in the past decade. There has been considerable growth in terms of companies offering services for shared mobility and there are many different ways in which it works and is marketed. But it is very important to differentiate between two different kinds of car sharing services, Ride Hailing and Ride sharing. (Lew Fulton UC Davis, 2017) Ride sharing (or trip sharing or shared mobility) – This refers to rides or trips that are actually shared between different individuals or different parties and paid separately. It can also more broadly include public transit services.

Ride hailing (or ride booking) – This refers to any app-based system to secure a ride from a taxi or other “on-demand” ride service provider such as Grab Taxi, Uber, Lyft, Ola, Easy Taxi or other TNCs. These rides may or may not be shared. Vehicles used for ride-hailing accumulate a similar or even higher annual mileage than taxis – typically around 70,000 miles – versus 13,500 miles for an average private vehicle in the United States (Mckinsy & Company, 2016). At the same time, the higher mileage experienced by a typical ride-hailing vehicle will also reduce its lifespan, accelerating the transition to next-generation vehicles that are both electric and autonomous. For every 10 percent increase in shared mobility as a proportion of the total, the cumulative number of electric vehicles sold in 2015–30 could increase by up to 5 percent.

Sharing the vehicles is an efficient way to use the vehicles, which have been typically privately owned and not used to its maximum potential. In a typical USA neighbourhood scenario, a shared car is able to replace anywhere between 5-11 cars. By far not a lot of success has been achieved in terms of actually reducing the cars from the people one of the reasons being that people resort not to buy second car which is in a way stopping the numbers of cars to grow in many western and European countries. At the same time its effect on eastern markets are very limited and car ownership is increasing in cities of many countries of Malaysia, Thailand and India. Also, car sharing has become more comfortable with better mobile and internet technology so, it in only in recent times then modal has been introduced so it will take some time before we can really see decli9ning number of cars as typically a normal cars life is 10 years, so once that time passes it is more hopefully to see considerable positive effects in terms of reducing numbers.

Sharing of car enables the distribution of fixed costs on many heads and hence it turns out to be a costeffective phenomenon. But unconsciously people are not aware about costs that includes the depreciation incurred in first 4 years and then eventually maintenance cost and since the comparison of a car sharing with only fuel cost seems uneven and hence a lot of people are discouraged to use it. With AVs and MaaS these costs can be made to seem more obvious and hence well-informed people will be able to take decisions consciously. (Anderson, et al., 2016) 16

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Figure 14: Use of car vehicles by time (hours oer day) (BCS car sharing, 2009) page 29

Car sharing provides the potential to reduce the costs of vehicle travel for the individual as well as for society. When a person owns a car, much of the cost of owning and operating the vehicle is fixed. The variable cost of using the owned vehicle is relatively low, and thus the driver has an incentive to drive more than is economically rational. In contrast, payments by car sharing participants are closely tied to actual vehicle usage. A car sharing system in effect transforms the fixed costs of vehicle ownership into variable costs. (Shaheen, et al., 1999) Car-Sharing vehicles are, on average, reserved by customers for one quarter of each day. Here too, the vehicles of larger providers are exploited to better advantage (28.8% of the day) than those of smaller providers (22.6%). Nonetheless, the difference between the two is significantly smaller than that between Car-Sharing vehicles and the average car (MOMO, Intelligent energy Europe, 2009).

As discussed before the mobility behaviour of individual is something that is also changing with the change in technology and ease of accessing services via internet. Before the car was used as an all-purpose vehicle but efficient sharing strategies allows for vehicle for special purposes, to go to office and to go for shopping might not need the same car. Car being a symbol of individualism is now changing since a lot of people have access to this individualism which gives rise to collective problems which can only be solved by collective strategy that is sharing. (MC Kinsy & Company, 2016).

On demand Dynamic shuttle has changed the face of traditional car sharing in multiple ways and it can be accelerated in multiple ways. Taxi services and carpooling have allowed people to share trips for decades, new technology creates the potential for nearly all trips to be easily shared among multiple riders. A dynamic shuttle is a smaller shuttle bus that can serve more passengers than a taxi and offers a more flexible transportation solution than traditional fixed-route public transport buses. This development could revolutionize transportation. Cutting the cost of ride hailing in half or more, ride sharing has the potential to attract large numbers of travellers and dramatically cut the numbers of vehicles on our roads. However, these benefits are only significant if they reduce the number of trips taken.

Car sharing has a huge impact parking demand in the cities as well as ownership per capita. Increasing sharing or vehicles has now reduced the need for minimum parking in master plans and increasing master plan are adopting the strategies to share the parking space in a mixed-use setup and trying to save space and cost when it comes to underground parking. Millennial are owning less and less cars than the generation before them. Even when they are owning the vehicles it’s much later than the previous generation since it’s a huge financial investment and there are other options available especially in dense cities with good public transport network. Some of these impacts will be discussed in detail in later sections.

In a study published by MIT sensible City lab nearly 80% of the trips made by the Yellow taxis in New York could have been shared. (Badger, 2014) This study takes the data which was published by the city government regarding the pick-up and drop off points in the city. So, if people were to wait for 10min for their ride almost 95% of the trips could have been shared. These numbers are consistent in some other 17

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cities like Singapore, San Francisco and Vienna over 95% of the trips could have been shared. So, there is a huge potential in sharing economy of travelling which is growing day by day (Cohen, 2017). Similarly, the International Transport Forum developed a simulation of Lisbon showing that the city could serve its typical daily travel patterns with only 10% of the vehicles currently used, with a combination of 8 and 16 passenger vehicles. Thus, the success of ride sharing as an energy-efficient and space-efficient mode will depend both on the average number of riders per trip, which must be significantly higher than modes like private automobiles, and on its ability to draw riders from these less-efficient modes, rather than from public transport services. (Lew Fulton UC Davis, 2017) Apart from Car sharing Bike sharing has also been on top of most city’s agenda. It started with Bike sharing stations which were sponsored from Local government. It depended on stations which were mostly located in famous public areas and spreading the network all over the city has always been a challenge since it takes up space and needs an infrastructure to support. Consequently, dock less bike sharing has been introduced in recent times where all the bikes are GPS enabled and people pay online and scan the bike with Bar code. It’s a completely free-floating system. This system gives seamless options but recently it has been realized that these systems create an oversupply of bikes in the city centre and sometimes sidewalks are filled with them leaving not enough space for pedestrian. Since we know that demand and supply don’t add up to a perfect match based on geography. Originating trips might not return at the end of the day.

Hence this one of the challenge that needs to be dealt with better modelling exercises and experience.

Sharing economy has flourished with internet and Smart phones easily accessible to huge population of people and hence it has become easy and efficient but the same comfort leads to oversupply and now there is a need to make sharing services efficient, in this context AVs have a huge role to play.

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3 3 Scenarios of Implementation of AVs Scenarios are very important in order to understand how our cities will change and the possibilities that we have to be prepared for in case one of the following thing happens. So, scenario ranges from a more pessimistic one to an optimistic one. This approach lets us gouge the impact of AVs on our build environments effectively. In defining scenarios there are some assumptions involved in the process and then the logic is extended to a possible future that would arise from those assumptions given that assumptions are as realistic as possible and also as imaginative as it needs to be. In all the scenarios we are imagining at city scale, a city that is large enough to host multiple functions and has extended suburbs and rural areas that depend on it. It is a metropolitan city scale. Since this is the scale at which most of the planning happens and even within the larger framework of urban planning and transport at central level most of the actions are directed to the immediate local government and hence this scale is important to consider while assuming how the AVs will impact the cities.

3.1.1 Limited CAV penetration 3.1.1.1 Assumptions Let us imagine a future in which AVs are still as a minority and most of the commute still happens on traditional cars. This scenario assumes that it will take more than perhaps 30 years for AVs to evolve and even when they come public acceptance is low and safety is not ensured and hence people still opt for the normal cars with some assist features as of today which will get better with time. Therefore, there is a certain level of automation in the market but they are not automated.

One of the other most important factor to consider is that the ownership patterns of the cars do not change. Services like car sharing has very limited market penetration so apart from some dense areas in the city centre people mostly own their cars and hence they need to park their cars at least 3 places in a day. If the vehicles are owned personally than parking associated with it also comes in play which has traditionally relied on land use regulation. So, if the ownership patterns do not change and the way people use their cars do not change than the trends that are seen today are likely to aggravate in future.

Another aspect, which will remain constant, is the situation of Public transport. In metropolitan cities of today even after a certain number of people have been using PT there are many cars, which are still a problem. The car ownership will not fall but rise as the income increases and affordability increases. So public transport in this scenario caters less than 20% of trips. Therefore, the modal split remains more than 50% by cars. The future that it assumes includes the metros and buses lags behind to provide for effective service to masses.

3.1.1.2 Effects As we know the Urbanisation is reaching at its peak in the human history and in next 3-4 decades the population is going to increase many folds in urban areas. As projected by United nations to increase by 60% from about 4 billion people in 2015 to 6.5 billion in 2050; these urbanites are projected to collectively become more than twice as wealthy as the average urban dweller worldwide is today (with poorer countries such as India seeing a fivefold or greater increase in incomes, though the levels remain far below OECD countries, and many or even most people in poorer countries won’t have access to private cars in 2050). Because of this city growth and income growth, mobility levels will skyrocket. For example, the IEA projects a nearly tenfold increase in car travel in India between 2010 and 2050. (Lew Fulton UC Davis, 2017) Since the scenario assumes that car usage is going to increase in a way, so in absolute terms the number of deaths caused by accidents is increase many folds. In 2015, the world was at 1.2 million in terms of accidents and increased dependency on OEMs is likely to increase this number many times over. Even

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when we are looking at cleaner air due to better technology and hence safer environment but the sheer numbers will be able to nullify the leap we are able to gain in terms of technology.

In terms of urbanisation most of the cities might not achieve the dense structure that supports public transportation since we have already assumed that investment in public transport is not able to maintain even the same kind of modal splits as of today. Hence people rely a lot on private vehicles and sub urbanisation become worse. The peak hours in most of the metropolitan cities of today like London, New York or Melbourne is already 3-4 hours in morning and the same in evening. This situation is likely to worsen and hence providing access to suburbs for alternative transport system will remain a far-fetched dream.

It has already been stated that the parking in the cities take as much as 30% of spaces and each car needs somewhere around three parking spaces. In terms of innovation and space utilisation since the AVs will provide innovative parking solutions to in the city will remain limited. So even if there are some intervention at decentralised level overall demand will likely to increase. Even though strategies like sharing the car, parking places in mixed-use neighbourhood is a very common strategy to optimise the parking requirement the sheer numbers might as well outnumber the gains from strategies like these. The bestcase scenario we can assume that parking requirements will remain constant and still it doesn’t’ solve any of the existing problems that we are facing. Only a drastic change in the way in which we deal with ownership and parking of cars as the potential to change the situation and possibly solve some of the urban parking problems.

3.1.2 Private CAV as a new mode of transportation (Private CAVS, more convenience etc.

3.1.2.1 Assumptions This scenario assumes that automakers take the lead in terms of market penetration and private AVs will be available to people as they are presently. There is a revolution in terms of AVs and EVs and they become widely available in the market and slowly the traditionally cars will be phased out in 30-40 years’ time gap.

But we assume that the sharing component of these do not come in play as much as other become important. People prefer to own their CAVs than share them with strangers.

As the estimates made by a study of Boston consulting group a small percentage of the new SDVs are shared, there is a very small (about 1%) reduction in the number of vehicles in the city. Emissions drop by 9%, thanks to a higher share of zero-emission EVs. In addition, those SDVs that have internal- combustion engines are assumed to consume 20% less fuel than traditional cars with drivers. (Lang, 2016) Connected AVs will make it possible for vehicles to communicate and thus decrease the necessary distance for safe driving. Likewise, having information about other vehicles intentions, accelerations and braking makes it possible to avoid the emergence of traffic jams and suggest a much better traffic flow than is present today. One example being the intelligent control of intersections.

Autonomous vehicles will become highly attractive compared to established travel modes at some point in time and are regarded as a disruptive force in the transport market. (Hörl, et al., 2016) Since AVs will become comfortable, more and more people will be able to drive it. People of young age, disabled and old age people will be able to use the AVs. There is a likely scenario in which AVs will push for people owning just one car as it might drive itself and hence can be used by the whole family, in this scenario the likelihood of a family buying a second car is going to be less than that of today.

Using an AV that is electric will be more cost effective and might even be cheaper than public transportation. In that case, PT ridership is likely to suffer great losses. Since one of most important factor, that people use PT is the cost the convenience of not worrying a parking spot which can also happen in case 20

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of a private AV and hence there will be many people that would like to shift to AV. This scenario in a way pushes for a decrease in the ridership of PT and AVs become increasingly attractive option in the short run before the wide problems are realised in the long terms.

3.1.2.2 Effects With the assumptions made in the scenario, it is more likely that the urban sprawl is going to increase many folds. Since these AVs are owned privately it more likely to be equipped with personal adjustments like a television or a computer and people will be willing to travel more distances in order to reach their work position. People would like to stay away from the city environment as they can also work when they are in their car. The more the suburbanisation the less dense will be the cities and we will be moving towards what was seen as the suburbanisation era in America which might give rise to its own problems. The city and its centres might return to being not an ideal place to live and work. Places like Manhattan shall seldom exist and hence it has far-reaching consequences.

There will be something called induced demand, though the numbers of cars might not grow to an extend that might create traffic congestion yet the same number of car might spend more time on road and hence the vehicle kilometres travelled is likely to increase. Some of the estimates are ranging from 7%-20% with the driverless car being the harbinger of increased mobility options for more amount of population.

The assumption made by ITDP report are as follows, in all regions of the world, we assume a 10-15% increase in driving per capita (and per vehicle) in personal AVs relative to Business as usual. This could also include increases in zero-occupant vehicle travel, as people assign vehicles to conduct tasks such as retrieving family members or even packages. We assume another 5% increase in vehicle travel from this in our scenarios, resulting in an overall 15-20% increase in vehicle travel, though we acknowledge the effect could be more significant (Lew Fulton UC Davis, 2017).

As stated in a report by ETH Zurich the prediction for induced demand by different authors are as follows: (Hörl, et al., 2016) • • • •

Bierstedt et al. (2014) expect an increase of VKT between 5% and 20% with a 50% market share of AVs, later up to 35% Chen (2015) estimates an increase in a similar range from 7% to 14%.

Fagnant et al. (2015) estimate an increase of 8% and Fagnant and Kockelman (2015) systemwide up to 37% at 90% market penetration.

Increases in VKT are also observed by Hörl et al. (2016) and Gruel and Stanford (2016)

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3.1.3 MaaS (Mobility as a service, Fleet CAVs, shared CAVs, on demand CAVs) 3.1.3.1 Assumptions This scenario is most optimistic of all the scenarios. In this scenario, it is assumed that there is revolution in automation, electrification and sharing. AVs will become a very important component of overall MaaS strategy, which will be composed of Car sharing, rode hailing using AVs, on demand shuttles becoming more prevalent rather than fixed route public transportation. The autonomous technology will be deployed in pilots as early as 2020 and hence will start to take shape based on encouragement of local government and Policies and how cities will implement them. The government are more likely to go for improved infrastructure for Pedestrian and cyclists in and around the city.

MaaS as defined in https://en.wikipedia.org/wiki/Mobility_as_a_service_(transport), ‘combines transport services from public and private transport providers through a unified gateway that creates and manages the trip, which users can pay for with a single account’. Users can pay per trip or a monthly fee for a limited distance. The key concept behind MaaS is to offer travellers mobility solutions based on their travel needs.

(Hensher, 2017) The three Pillar of the MaaS strategy are the Customers, Mobility service providers and Local authorities as it was mentioned in the report KPMG. In this scenario even if we can assume that traditional automakers will be able to maintain their stakes in the market of producing and selling the AVs most of it will be run by technology providers of managers of this sharing like Google, Uber or Apple. This scenario is likely to support the big tech companies as there will a fleet of CAVs used for providing people day-to-day service.

Currently todays car is an all-purpose vehicle since it in ownership of an individual e.g. an individual uses the same car to go to office, shopping or on a holiday, we might have 2 different cars one big one small but that adds more to inefficiency in an overall city or neighbourhood scale. But in the future the mobility

Figure 15:Pillar of MaaS strategy KPMG 2018

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behaviour of individuals is likely to change and there will be special mode of transportation for the specific trip. The vehicle needed for going to work might be a shared shuttle in the neighbourhood and then a high capacity transit and a privatised CAV for going on a holiday with the family. In generic term, individuals will be free to move around as they wish but there will be a wide range of offers to meet the mobility demand and trip type it would be possible for the people to buy mobility needs instead of vehicles or ticket.

Figure 16: Components of Mobility as a service (MaaS strategy)

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MaaS will allow for the coexistence of different modes of transportation which are tailor made for the specific journey purpose. How the costs are distributed along the different mobility choices will be something that city governments have to take into consideration. The case of Whims in Finland is one of its own where people pay in terms of packages that will allow for the mobility for them in the course of the month. MaaS Residents of the city use the travel app, Whim, to select their favoured mode of transport from a list including trains, buses, bikes, trams, or taxis. In cases where they need to switch mode midjourney, the app enables them to plan and where necessary adjust their route in real time to minimize hassle and delay.

MaaS is big pie whose benefits can be widespread and OEMs and new service providers can both have their own place in it. By ARK’s estimates, in San Francisco the miles travelled across all point to-point MaaS offerings has the total vehicle miles travelled increased from 0.4% in 2013 to 0.6% in 2014. As autonomous technology drives ride sharing prices down further, it is safe to predict that MaaS will take a much larger share of all miles.

As it has been said before, AVs will become the main components of the transit options of the future. Since Cavs will be used in multiple ways e.g. on demand services, Shuttle service and Sharing services. Whether its traditional OEMs or tech giants the CAVs have better stake and dependency for the people’s mobility options, hence becomes the central piece of MaaS strategy.

MaaS initiatives are growing very fast throughout the world, especially in Europe. Kamargianni et al. (2015) identified a number of existing travel services/initiatives where citizens are offered a form of monthly subscription payment (MOBIB5 in Brussels, HANNOVERmobil,6 EMMA in Montpellier, SMILE7 in Vienna, and Moovel8 in Germany). These typically include a fixed monthly subscription for unlimited public transport use (costing slightly more than a PT monthly pass) and discounted pay-as-you-go rates on usage of all other modes such as car and bike sharing and taxi. Customers receive an integrated mobility bill at the end of each month that includes the basic cost as well as taxi and car/bike sharing usage fees. These are typically provided through an app or through purchase of a smartcard ticket. (Hensher, 2017) Furthermore, in this scenario the local government will be very active and keen on changing the mobility options available in the city and hence an overall approach by the local government is taken in order to improve the living environment and mobility options. In this scenario the likelihood of CAVs integrating with existing Public transport and Pedestrians is higher. In terms of importance for the city government the top priority is improving the urban environment and experience of the people in the city by providing seamless mobility options which will be facilitated by CAVs.

3.1.3.2 Effects As the immediate effect of the strategies in this scenario the urban sprawl will likely to decrease and hence the cities will try to move towards a more compact option and hence the fast-growing cities have the opportunity to limit the land usage on the periphery and for the cities that are already expanded to a certain limit the densification of the central core is something that is likely going to happen. So MaaS strategy in the favour of city government and hence in the favour of people s carried need will make the cities dense and connected.

The parking requirement in the cities and especially the downtown areas will decrease by a huge percentage. Since most of the mobility needs are met by CAVs in form of on demand, sharing and shuttle service in conjunction with Mass transit the need of the parking for individual building and complexes will change. This scenario offers parking to be completely detached from land use which will be discussed in the further sections. There are various ways in which parking can be managed in the cities. On one hand there is on- street CAV parking and other hand there is parking in structures that makes it more compact. Some estimated suggest that CAVs can be 3 times as efficient in stacking the parking then a human driven car 24

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hence more storage in less space, this aspect will be discussed in the later sections. One thing can be noted is that big parking lots which facilitate the office and commercial building is going to be phased out.

There is another benefit of MaaS strategy can provide is the concept based on dynamic pricing. The peaks will not be as congested as it is now, the difference between peak and off-peak fleet requirements will reduce. The concept of dynamic pricing will allow for a higher occupancy in peak hours. A shuttle service from an office complex will allow for people to reach the nearest transport hub where they can choose to take a shared taxi, private CAV or Public transport. More and connected options allow for the cost to dictate the mobility choices for some and for other it’s a matter of convenience & time. So, it is safe to assume that traffic on the streets in the peak hours will be managed more effectively with many options to spare.

In the next chapter all the effects and challenges for different scenarios will be discussed in detail based on the respective topics. It is important to note that in some way scenario 2 and scenario 3 will become closer to each other as the time passes.

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4 Impact on Traffic 4.1 Induced Demand An introduction of AVs will facilitate a large part of population to take part in an individual mobility behaviour. Currently one of the biggest challenge in the field of transport access are individuals with disability, old age and children. Their access is limited and security concerns are of prime importance. But with AVs and Shared AVs the negative effect of travelling alone will be curbed to an extent with apt passenger information system and reliability.

“AVs could eventually provide ‘mobility freedom’ for the elderly, children, people with disabilities and others who don’t drive or are unlicensed to drive” (Sommers & Weertunga, 2015) Such freedom will be very beneficial when the population is aging and there is a need of increased security and providing them independent mode of transportation. Now since we know that with the convenience of driving and also other options available on the market more and more people will be wanting to travel across different places and people who want to make trips and who actually make those trips increases.

This in terms of traffic engineering is called the induced demand. Some estimate suggests as much as 15% increase in the number of trips made by people. But there are other factors that come in play like Vehicle miles travelled and congestion, which in current scenario are joined to trips that people make. But what happens when more trips might actually lead to less congestion?

4.2 Vehicle Miles travelled This is the measure of kilometres a vehicle on an average travel which also gives the idea of how much it is used and how much time it’s free. VMT will surely increase for CAVs or Cars that are shared. But even in the case of private CAVs the trips will be shared within a family or a small circle so we can assume that CAVs even the most private one which will be probably be made by OEMs will be much better than traditional cars. This will also be due the fact that there will be many dead kilometres that these cars will travel. In the research produced by International transport Forum in partnership with CPB, there is a simulation done based on the various factors and composition and hence the VMT and other technical details.

As a result of TaaS and ride-sharing, it is predicted that the number of vehicles on the roads will decrease although the vehicle kilometres travelled (VKT) will likely increase. (Godsmark, et al., 2015) “Overall distance travelled increased by 11% compared to a traditional human-driven self-owned fleet. This increase in travel distance was largely due to the relocation of the SAVs and the distance travelled to collect the next passenger. However, environmental impacts of the implementation of such a fleet are positive, with 5.6% less greenhouse gas emissions, 34% less carbon monoxide emitted, as well as a 49% reduction in volatile organic compound emissions, among others, compared to the traditional US light duty fleet.”

(International transport Forum, 2015) A similar example of New York taxi cabs where a shared fleet is constructed in such a way that every real trip taken occurs in the model with no more than a five-minute delay to the real arrival time. Results suggest that the total number of kilometres driven by a taxi in New York City could be reduced by 40% with such a shared taxi system.

4.3 Congestion Congestion is referred to the Volume by capacity ratio and if this ratio goes more than 1.00 it can be said that its congestion. It is usually relevant in peak hours. In some literature it is also has level of service and there are thresholds to be achieved to be optimum. 80% V/C capacity is considered optimum while less than 50% will be considered underutilisation and overdesigned. To start off the AVs will make much more impact on the highways and large urban roads that carry a lot of traffic. We are focussing our attention on large urban roads and highway. There are many studies done in order to determine the impact of AVs on 26

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the traffic when a certain percentage of CAVs are added and some of the results are very interesting. Even a certain threshold of CAVs in the mix traffic can impact the congestion scenario.

A typical highway with all human-driven vehicles provides a maximum throughput of about 2,200 vehicles per hour per lane, which is also called the roadway capacity. This reflects only 5 percent utilization of the roadway space. On the other hand, AVs can allow a much better utilization of roadway space. This is because AVs can better sense and anticipate the lead vehicle’s braking actions and acceleration/deceleration decisions than human drivers. The technology allows much smaller perception and reaction times (than that needed for humans), smoother braking, and shortening of vehicle-following gaps even at high speeds. Further, unlike human-driven vehicles, the speed and traffic flow performance of AVs does not degrade in narrow lanes due to more accurate steering.

In today’s highway capacity analysis, each truck is considered equivalent to about 2.5 cars in terms of roadway capacity consumption, partly because of the large spacing needed between human-driven trucks and human-driven passenger cars. AV technology can significantly help in reducing inter-vehicle spacing, even in the presence of buses and trucks. So, in a way the capacity of current roads might increase in a certain way but at the same time we are seeing increase the VMT which will result from an easy access to mobility options.

Bose and Ioannou used simulations to demonstrate that 10 percent semi-autonomous vehicles in the traffic mix (with mixed traffic) can help smooth the traffic from rapid accelerations of human-driven vehicles.

(Pinjari, et al., 2013) It is very important that the vehicles on the roads are connected and are able to interact with the other vehicles and environment /infrastructure this will decrease the spacing between them and hence allow for a compact mix of traffic. Different penetration rates show different scenarios. There are two opposite results that studies have achieved. On one hand lower penetration rates will improve the traffic overall and hence we will be better off in terms of congestion. But on the other if we take into account other factors such as VMT, no of cars in traffic and induced demand than we can say that in the lower penetration percentage the traffic will overall become worse with more cars. Things to explore is whether the added benefits of compact traffic are nullified by the induced demand or not. At 50 percent market penetration, they estimate a maximum capacity of 2,685 vehicles per hour per lane (vphpl), which is 22 percent higher than the today’s typical highway capacity (of 2,200 vphpl). At 80 percent and 100 percent penetration rates, they estimate a maximum capacity increase of 50 percent and 80 percent, respectively.

4.4 Vehicle Fleet and Occupancy Currently each and every vehicle is considered as an individual entity but the problem arises when these individual vehicles interact in a manner that is inefficient in the overall context. To draw the parallels from Tragedy of Commons when individuals try to maximise their individual benefit (driving faster when got the opportunity and congest the cars) then the overall resource will suffer (time lost in congestion along with inconvenience). So when vehicles are treated as a singular entity whose performance is evaluated on collective basis then there is a possibility to achieve efficiency in the system.

In a case study modelled in Lisbon regarding the shared fleet and CAVs it was found that “Under a ridesharing TaxiBot configuration supported by high-capacity public transport and modelled over a 24-hour weekday, 90% of vehicles could be removed from the streets while still delivering nearly the same level of mobility as before in terms of travel origins, destinations and length of trip”.

TaxiBots are self-driving cars that can be shared simultaneously by several passengers. AutoVots pick-up and drop-off single passengers sequentially. These are the terminology used in the study.

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There are other studies that indicate the same premise, the study finds that for the 120 000 residents of Ann Arbor who travel less than 70 miles a day, the shared fleet could provide near instantaneous access to a vehicle servicing their request, but with only 15% of the vehicles currently needed to carry out these trips.

However, overall travel would increase due to the need for repositioning vehicles. Similar findings emerge from the Babcock Ranch case study (3-4 000 vehicles for a projected population of 50 000 people). In the case of Manhattan, the study finds that a fleet of 9 000 taxis could replace all of the trips taken today by over 13 000 taxis with average waiting times of less than one minute, much lower than today.

(International transport Forum, 2015) A study by the OECD (OECD/ITF, 2015b) comes to the conclusion that 10% of today’s car fleet is needed to cover the existing demand in Lisboa, Portugal. Likewise, Bischoff and Maciejewski (2016b) estimate that 10 cars in Berlin can be replaced by one AV, and Fagnant et al. (2015) estimate a ratio of 9:1 for Austin, Texas.

A more recent study in the same city with added simulation components such as a recharging infrastructure estimates that 6.8 private cars can be replaced by one AV (Chen, 2015). For Singapore, a study came to the result that 30% of the available fleet size would be needed (Spieser et al., 2014) and a possible reduction of up to 90% of fleet size in the Zurich region has been found in (Boesch et al., 2015). (Hörl, et al., 2016) In a study done by Columbia University the impact of having a driverless shared-mobility system over privately-owned vehicles in An Arbour, Michigan (population-285,000: area-130miles). In 2009, there were a total of 200,000 passenger vehicles, an average of 740,000 trips daily, and vehicles were in use about 5 per cent per day. The study showed that with a fleet of 18,000 AVs, consumers could expect to wait under one minute for a vehicle to arrive, and the vehicle would be used 70 per cent of the time on average between 7am-7pm. It is estimated that ownership and use of a medium sized car driven 15,000 miles peryear-per-mile costs $0.59 (car, insurance, fuel, repair etc.) compared to $0.41 with a shared fleet of 18,000 in Ann arbour, a 31 per cent decrease. (RUSSELL-CARROLL, s.d.)

In a recent study done by Carlo Ratti & MIT sensible city lab (Ratti, et al., 2018) have possibly solved the minimum fleet problem. Which means we know how many cars we need in order to meet a particular demand on a given day, this will ensure the distribution and planning of parking which is done in this research further on. The Minimum Fleet Network model developed by the Sensible City Lab could reduce the taxi fleet size by 40%. A lot of taxis are encircling in the city in order to find passengers but if there is a system algorithm that helps passengers meet drivers than this feet size can be reduced considerably and efficiency in terms of earning can be more.

Figure 17: Increased efficiency of a taxi system with algorithm. Carlo Ratti ,2018

Currently, active taxis are circling to look for someone to pick up when they are not driving a passenger to destination. While each driver seeks to reduce this empty time individually, taxis still travel without serving passengers for about 40% of their time the working hours with passengers on board are highly optimised when this system is put in place. AVs are more likely to be able to respond to such demands and algorithms.

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Figure 18: Trip generation, fleet distribution and parking demand for a Master Plan in RIyadh (work done with Systematica S.r.l, 2017)

Currently all the cars have more of less occupancy of 1.2 meaning 10 cars will have 12 people travelling and 29

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hence when we look in absolute numbers there are lot of cars and a lot many trips that could have shared.

For instance, if the average could be increased from 1.2 just to 1.5 passenger in each car, car traffic would be immediately reduced by 20% (Galizzi, s.d.).In another simulation done in Riyadh in a Master plan scenario it was found that the fleet size or the parking requirement for the whole master will reduce from 25000 to just 9000 and hence this different CAVs which includes on demand CAVs, Shared CAVs and Shuttle CAVs. In the peak hours the occupancy will likely reach 3 which is 1.2 in the current scenario of the city. The assumptions took into consideration the context of the city the family structure and hence the rates were assumed, the simulation might not actually represent the present context or travelling exercise In the same simulation exercise, it was found that the peaks have become less steep and this can be attributed to higher occupancy in the peak hour which can be regulated with varying costs as it is done in Uber share. Cost has a very pivotal role to play in terms of mobility choices of different people and hence it is a policy decision that will impact how our cities will change in the era of self-driving cars.

In a study done in the city of Lisbon, Portugal shows that shared cars can have a huge impact on the traffic and the fleet required to make a certain number of trips. In a simulation study in (Forbes, et al., 2015) Transport simulation comparing the end-state of only shared cars on city roads to the current state of urban transportation provided a showcase of benefits and consequences of the proposed solutions.

If “taxi-bots” (i.e. ride sharing) were to replace all conventional cars and buses, the number of vehicles on the roads each day would be reduced by 90% (or 65% during peak hours) – with huge impacts not only on mobility per se, but also on city form.

• • • •

10% of vehicles required to deliver transport needs for a 24hr period 35% of vehicles required to deliver transport needs during peak periods 80% reduction of off-street parking thus creating new challenges in terms of the management of the freed urban spaces in order to lock-in the benefits for the society for the long-term.

30-90% increase in vehicle kilometres travelled due to diversions and repositioning

Figure 19:Infographic on a study done in Lisbon. (Urban Land institute, 2015)

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Solution–scenario: shared “on demand” buses with a capacity of 6-18 people, bookable 30 minutes in advance via a mobile phone application and boarding taking place within +/- 10 minutes with “pop-up” stops subject to a traveller’s current location.

• • • •

5% of vehicles required to deliver transport needs for a 24hr period 230% occupancy capacity when compared to 80-person bus 22% reduction in kilometers travelled 27% CO2 reduction

The role of policy-makers for the future developments in the field of autonomous vehicles and car sharing is essential. So, all in all from various different case stories and simulations it can be seen that the fleet size that is required in order to meet the demand of all the trips is going to reduce drastically and one of the reason for this huge change is turnover of passengers per vehicle in different contexts. Hence it impacts the car occupancy.

4.4.1 Conclusion of the effects The general conclusion on the impact of traffic as here as follows Road capacity will increase even after the induced demand since the traffic mixed with AV or even partial automation have demonstrated to regulate the traffic better then all human driver.

Vehicle miles travelled are likely going to increase since more people will be able to travel for longer distances. In case of private mode of vehicle increased VMT might not add any additional cost to the trip but time will still be an important factor for commercial and logistic industry. There will be a lot of dead kilometres which will be one of the challenges for planners, policy makers and technology providers.

Looking at Scenarios that are defined Scenario 1 will experience more dead kilometres since sharing is limited and hence will be more inefficient than scenario 3 which will have a component of sharing and smarter parking policies.

Congestion is going to get worse in Scenario 1 with increasing rate of urbanization and limited space for expansion in terms of road capacity. In Scenario 2 & 3 the traffic might improve to a certain extend in the short run but more perceivable effect on a city scale will be there with increasing penetration of level 4& level5 automated vehicles.

In scenario 2 the optimization will be more prominent in the highways around the cities which will take the benefit of autopilot mode and hence better platooning and cruising but the urban areas will be more congested since in case of gradual automation urban roads will be driven by human drivers and induced demand to a certain extend will make the problems worse.

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5 Social impact of AVs 5.1 Mobility for all The impact of AVs on many social aspects in our rural and urban areas is pivotal. It should also be noted that with the advent of AV the mobility scenario of different people and age groups is going to increase.

People who have a limited access and depended on other will have access to AV .This will provide independent mobility and practical solutions to problems of different user groups.

Autonomous vehicle technology on its own is not enough to help these people become more independent, but simultaneous advances in machine learning and artificial intelligence can enable these vehicles to understand spoken instructions, observe nearby surroundings and communicate with people. (Saripalli, 2017).

In most communities, this type of transport, typically called “paratransit,” is sort of like an extra-helpful taxi service run by public transit service providers. Riders make reservations in advance for rides to, say, grocery stores and medical appointments. The vehicles are usually wheelchair-accessible and are driven by trained operators who can help riders board, find seats and get off at the right stop.

The category of people who will be able to get to move in different ways with a lot more freedom than they enjoy today are Old age people, people with physical stability, people with down syndrome and other nonpermanent problems. There is an opportunity for a wide section of society to feel safe with the advent of autonomous driving.

In Nishaka Japan a small rural community 71km away from Tokyo, the local government has started testing autonomous vehicles in rural areas. As the annual rice harvest begins in the Japanese town of Nishikata, the combines that usually putter along the sleepy roads lining its rice fields are giving way to a vehicle resident have never before seen, a driverless shuttle bus. The town mirrors Japan’s population profile, with roughly

Figure 20: Robotic shuttle in Nishaka town, Japan, Photo taken on 8 September, Reuters /Issei Kateo

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a third of its 6,300 residents aged 65 or more, up from about a quarter four years ago, while the population overall has shrunk 4.5 percent. (Tajitsu, 2017) James Welling who was born with a permanent movement disorder, this disease effects 2/1000 people. In his interview 2025AD Driverless communities he mentioned that driverless cars will give wings to people from his community and all other diabled people. On the other hand sharing his experience with current modes of transportation and how AVs can emerge as a winner if design are done carefully. But at the same time it is said that it can also be overpromise and companies need to keep making profits and hence a limited comfort can be an issue in the driverless cars of future. (Welling, s.d.)

During a few days in August, the parking lot at Perkins School for the Blind morphed into a test zone where a golf-cart-like vehicle transported students and staff members, guided by a laptop. It was a prototype from Optimus Ride, a startup in Cambridge, Massachusetts, that is developing self-driving technologies for electric vehicles. Autonomous vehicles will be transformative for people who are blind,” says Dave Power, Perkins’s president and CEO. “For the first time, they will be able to get to school, work, and community activities independently, regardless of distance. There is tremendous enthusiasm about it, both here and nationally, among the blind.” (Woyke, 2016) Another interesting design concept is having driverless cars for school going kids . Since in the realm of driverless cars integrate school going children in neigbourhoods is very important. So in an article published

by WIRED they have highlighted that the driverless cars of future will be smaller in size and will be more flexible and durable and will add a lot of flexibility to students and parents in order to access the service.

(MARSHAL, 2017) Since it is already mentioned that these new group of people will likely be able to access modes of transportation which were not present. Hence the induced demand which has been discussed before will come in play.

Figure 21: Driverless school buses for young children in the neighbourhood areas

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5.2 Car Ownership Car ownership is one of the most important factors that will impact how our society is organised and how the mobility options will change in the long run. This section tries to address the changed ownership patterns with the advent of driverless cars and behaviour of the current generation in respect to owning a car vs. renting it; for the purpose of using it as a mobility vehicle rather than a permanent asset.

For decades, young people’s mobility development in industrialized countries has been in line with the growth of per capita travel associated with increasing motorization and car use in these countries. More recently, there has been a strong indication of profound changes in the travel behaviour among young adults in industrialized countries with signs of decreasing car ownership and reduced overall travel. In order to look at the mobility trend in a context it is important to look at people of young age buying cars or are they using other means of transportation for the purpose of their travel. (KUHNIMHOF, et al., 2012) In the developed world with high density and where alternative modes of transportation are present the car ownership has been dropping since the last decades. Between 1970 and 2005, the number of cars per 1,000 persons increased in Germany from 230 to 550 and from 210 to 500 in Great Britain. Per capita travel demand specifically automobile travel - of elderly travellers is still on the rise. The generation born after 1990s are owning less cars and are travelling lesser and lesser in their private vehicles. Percentage of young people holding a driving license is decreasing in these countries as well at a steady rate. This largely conforms to expectation as the last representatives of generations who had lived a life without car are being replaced by more auto-oriented generations. Even the number of people in the eligible group having a driving has decreased from 75% in 1993 to 64% in 2008 in Britain rates. Also a study by the German ministry of education shows that in 2009 only 34% of students had expenses for a car compared to 54% in 1991. This observation might be misleading if parents finance auto related expenditures. (Kuhnimhof, et al., 2011) Similarly, a study in USA, California state found out that the Millennial generation are far less likely to own a car and more likely to share the car with their peers and make use of technology in the same aspect.

(Circella, 2013). AVs have the potential to profoundly change the current vehicle ownership model and expand opportunities for vehicle sharing. If vehicles can drive themselves, they can be summoned when needed and returned to other duties when the trip is over.

In the scenarios that are indicated in this document it can be seen that the decreasing car ownership trend works in conjunction with scenario 3 where Mobility is like any other service and owning a car is not very important for having a high degree of mobility freedom. On the other hand, in scenario 2 the ownership of cars and especially the private ownership of car is something that is favoured because still the mobility is not detached from the vehicle itself that an individual owns.

5.3 Productivity benefits of the population There has been a lot of literature regarding congestion in the cities and in the paragraphs above it was discussed the importance of AVs and possibility reducing in the congestion in the cities. Individuals who are travelling in AV will have access to internet and therefore, possibly to work use the travel time more efficiently.

AVs will make travelling more reliable and time cost of travelling will move towards 0. But this has no relation between how much people will travel to reach their work places or will they travel at all if working in an AV is possible. On the contrary travel times and miles have a possibility to increase with increasing comfort of travelling; this will have an impact on densities in cities.

Improved travel-time reliability with AVs will decrease transport costs and improve supply-chain productivity. Industries dependent on just-in-time delivery will be able to reduce inventories even further through connected services. Former drivers will now be capable, at no loss of safety or risk of violation, of 34

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undertaking work or having social time with their family or friends while in transit. (Mckinsy & Company, 2016) Moreover, due to their efficient real-time control algorithms, driverless vehicles have the added benefit of not requiring the two-second human reaction time between vehicles, therefore saving additional road space while driving. The saved time and increased environmental benefits are probably most apparent in autonomous intersection controllers that seamlessly merge together traffic streams of autonomous vehicles, allowing traffic to flow smoothly across intersections. (SZELL, et al., 2015) People imagine performing a variety of different tasks in the car. One blogger, for instance, demonstrates that Google’s self-driving car is not only beneficial in terms of productivity and efficiency but it also frees up time to spend with family and friends. So: „I myself would love to have a car that will take me where I want to go without having to do anything. I would like to take an 8-hour drive from Arizona to California with my family and spend the whole time interacting with them instead of staring at endless miles of asphalt.

(Nordoff, 2014)

5.4 Consumer readiness to automated driving Consumer readiness to autonomous driving is one of the important pillars to make autonomous driving a reality. It is important to understand how the consumers or now an future perceive AVs for their future mobility needs. Companies that includes traditional OEMs, technology giants and specialist start-ups have invested more than 50billion dollars in development of autonomous driving.

In a study conducted by the Boston consulting group Overall, 58% of respondents said they would take a ride in an SDV (Self driving vehicle), and 69% said that they would take a ride in a partially self-driving car.

Willingness is highest among younger consumers 63% of those aged 29 or younger are willing to ride in an SDV compared with 46% of consumers aged 51 or older. This is one reason why we expect acceptance of SDVs to increase over time. Consumers in Asia home to half of the world’s 100 largest cities are among the

Figure 22: Consumer readiness to driverless cars (Boston consulting group) (Lang, et al., 2016)

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most ready. Willingness among Indian and Chinese consumers, for example, is high: 85% and 75%, respectively, are prepared to ride in SDVs. (Lang, et al., 2016) A lot of consumers believe that SDVs will solve the existing problems of having to park cars whenever they go out and the difficulty they have to face in order to park them .There are always security concerns regarding the location of parking which needs to be addressed as well. All these problems can be dealt with and mitigated with the use of self-driving cars.

In another study conducted by KPMG (Threlfall, 2018) there is an index of readiness that is given to different countries on how the country along with its policies and current level of infrastructure is ready for Autonomous vehicles of future.

It can be seen that the countries that have high level of development have the highest scores which is following the narrative that they have more flexible systems to put in place.Their insitutional framewok is robust to make the necessary changes and prepare for future of AVs.As mentioned in the report the important factors that makes a country score higher are governments willing to regulate AV support through laws for testing, network and infrastrcture both physical and technological. (Threlfall, 2018) In a research done by (Nordoff, 2014) has shown that 58% of respondents are willing to adopt Google’s self-driving car instead of a conventional vehicle. In another study conducted by Mc Kinsy & CO it was found that globally, customer demand for car connectivity is increasing at a very high speed: over the past year, the share of customers willing to switch their car brand for better connectivity has almost doubled from 20 percent in 2014 to 37 percent in 2015. The willingness to pay a subscription fee for connected services went from 21 percent in 2014 to 32 percent in 2015. Chinese customers are particularly enthusiastic about connected cars 60 percent of respondents are willing to switch their car brand for improved connectivity. (Mohr, et al., 2015) Another study conducted by University of Michigan study in 2014, 68 per cent of Australian consumers surveyed were at least slightly interested in owning a driverless car; however, 57 per cent of respondents had concerns about safety, system failures and reliability.

In addition, studies have shown that up to two-thirds of motorists enjoy driving so much that they are reluctant to give up control to autonomous vehicles. (Maunsell, et al., 2014) All in all it can be said that there are variety of reactions in the market and among the consumers. But one thing can be sure that there is a certain amount of willingness which is increasing day by day as the technology is developing and people are becoming accustom to the concept of self-driving cars. The kind of scenario that will prevail is something that is very local in nature? Countries like Singapore, Sweden and the Netherlands are the forefront runners in facilitating Autonomous car usage and they are likely going to push towards a MaaS approach; focussing on the collective instead of individuals. On the other hand there are developed countries like America that are market driven and hence scenario 2 will be more applicable in the cities of USA. Nothing can be said for sure how communities and cities will integrate self-driving cars in their neighbourhoods and cities. But there are trends that indicate the transformation that will come about in the immediate phase, there are some clear sign of some countries leaning towards one approach while others giving the responsibility to the market. Cities and local governments that are proactive in trying to discuss and debate the consequences of AVs in their urban planning will be able to benefit from this revolution.

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5.5 Change in employment patterns Employment concerns after having autonomous vehicles have been the centre of discussion in many debate. Not just automation in cars and vehicles but other field with the help of a learning AI has been around for a while. It is believed that any job that requires repetition of a set of steps can be automated even complex jobs are at risk of automation.

In order to focus the attention on autonomous vehicles the major stakes are truck drivers (logistic industry) and taxi drivers. It is also to be noted that these two employment sectors will have a more direct impact than others but at the same time there are other jobs which require the use of internet and not necessarily to be present in the physical space at all times. This will impact the overall travel patterns in the city. In terms of direct employment of driving taxi drivers and truck drivers are at a huge risk of losing their jobs at a much accelerated rate. Furthermore, these jobs are very specific and it is difficult to position these jobs somewhere else. In the next paragraph there will be a deeper insight in the phenomena of truck drivers especially in American context.

In this line of thinking there are two contrasting ideas that ar e put up in the discussion. One that deals with the fact that many truck drivers are going to lose their jobs while other are claiming that the employment will increase since we will still need human supervision in order to make the deliveries and since the included demand of e commerce is likely to have an induced demand for the logistic industry. Apart from long distance travels among the cities there is likely to be more demand of deliveries within the urban environments and a huge chunk of those deliveries are likely to be done with humans involved at some point of other.

5.5.1 Uber Freight It is important to highlight the Uber trucking example which claims that the he average age of a truck driver today is 49, compared to 42 for the average US worker. 55% of truckers are over the age of 45 while less than 25% are younger than 35 years old. The American Transportation Research Institute analysed the data and found that the average age has been steadily increasing for decades. One of the reason being one of the toughest jobs away from home and the pay is not handsome as other jobs and hence in a decade a large amount of drivers in America are going to retire and there will be a vacuum in the trucking industry.

(ATG, 2018)

Figure 23:Average age oftruck drivers in USA, ATG, 2018

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The argument that Uber is putting forward is that the nature of trucking industry should change and there will be more demand for logistic jobs and hence the drivers.

“In this scenario, we assumed approximately 1 million self-driving trucks on roads in 2028. (This doesn’t mean we think there necessarily will be 1 million self-driving trucks in 10 years — that would be very fast adoption — but this example helps highlight how things might change at scale). We also assumed that each self-driving truck could do the work of two of today’s trucks because they can operate at all hours of day and night. On average, trucks today spend less than a third of a given day on the road, according to the American Transportation Research Institute. Doubling their efficiency would mean self-driving trucks would be in use about the same percentage of time as commercial aircraft”. (ATG, 2018) The attempt is try to innovate in terms of Long haul jobs and local hub jobs which according to the Uber ATG report is going to increase I the next 20 years. On the contrary there are many big consultancies that have estimated just the opposite in terms of jobs for the trucking industry.

Goldman Sachs, for example, predicted trucker job losses of 25,000 per month as self-driving trucks roll out. McKinsey Global Institute put out a report with the possibility of 1.5 million jobs lost in trucking over the next 10 years. The International Transport Forum proposed that 2 million American and European truckers could be directly displaced by 2030. (MADRIGAL, 2018) But all in all it can be said that the recent case made by Uber has ran a debate among the traditional community that believes that trucking will be a lost job. In one of the blogs in the Atlantic author suggests that the deployment of transfer hubs—or what Viscelli has more evocatively called “truck ports”—would mean that most working truckers stay fairly close to home. This would mean a major shift in the geographic structure of the work. Right now, truckers can live in far-flung places where their wages go further. In a world filled with truck ports, the rising number of local trucking jobs would be more geographically concentrated around centres of production and consumption (MADRIGAL, 2018). The problem of staying away from home and not being able to attract the younger generation in the business can be mitigated in some way by the strategy that Uber wants to follow. Nonetheless, it is not very clear how the future in the trucking industry unfolds but one thing is for sure in the long run when AV technology matures and reaches high level of penetration the jobs in the trucking industry are likely to decrease because cost is one of an important factor when it comes to commercial operations in logistic industry and having a human taking the wheel is as economic as one can imagine.

5.5.2 Conclusion of social impacts In Scenario 1 will have limited automation hence the distance travelled, employment structure will remain constant as it is now and the commute times are likely to increase than decrease in major urban centres.

As discussed before Uber has launched the Uber freight which will have autonomous drivers who will be needed in the first and last phase of the journey. There are predictions that in short run the employment is going to increase since more good needs to be delivered as it will become cheaper to operate an autonomous truck but eventually in the long run the humans will be phased out when the technology becomes safe and accessible.

In the scenario 2 car ownership patterns for the car are likely to remain as it now of today. The families are more likely to get rid of 2nd cars if the car is able to drive itself back from its first trip to pick someone up for the second trip but this pattern is likely to unfold much later when level 4-5 automation is widely available in the market. Also, it is safe to assume that car sharing and AV sharing will gradually be promoted once the automations are reached to level 4 -5.

In the scenario 3 the ownership patterns are likely to change and with the revolution in sharing and electrification it will be easy and convenient to rent a vehicle for the trip in the cities rather than owning

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one. But the private ownership of cars even though autonomous will most likely to continue in the rural and sparse suburbs even with high level of automation.

Scenarios 2&3 in their mature phases will move closer to each other and sharing will be promoted in both the scenarios with different kind of impact.

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Impact on Parking

6 Impact on Parking 6.1 Parking and land use Parking in cities has been an integral part of land use planning and development. Parking is often treated a deciding factor when it comes to a property and its values and in a lot of cases it adds a lot of cost. As it has been discussed before parking in cities takes up too much space and there is a need to make this process efficient and autonomous vehicle provide the opportunity to do so.

Since their introduction in 1920 mandatory parking requirements has faced many unforeseen consequences. It was approached in a way to decongest the public streets but if now we look at it both the streets and the parking basements are full. In many cases the basements are empty and the streets are full because of comfort to park the car on the streets rather than the basement. Minimum parking requirement can often be a problem for small scale infill’s or extension in case of detached housing. In dense urban areas with high public transportation these minimum parking requirements keep the space underutilized.

(Chapple & Chatman, 2012) Minimum parking requirement laws put up a lot of cost on the land that is very valuable to the city that in the end is underutilized. AVs will give an opportunity to turn this nightmare of overspending and overdesign in some place to spaces for people. Especially the parking lots in the city centre need to be revaluated. In this section we will look how parking areas and parking space are likely to transform in 3 different scenarios. In recent years new and dynamic master plan strategies have tried to make use of mix development and use the parking from one land use to substitute the other. Like the parking of offices can be used for an event space in the night if they are in close vicinity and hence forth. But the gains in absolute terms are minimal and hence a completely new approach needs to be invented.

Requiring enough parking to meet peak parking demand reduces a projects density levels, increases the cost of development, and perpetuates a car-dependent society, which is highly resource intensive and produces negative environmental and health effects. (Filosa, 2006) It is very important to understand the effect on the total demand of the parking in our cities and how these demand are deemed to be managed.

6.2 Types of Parking In order to understand the parking demand and situation in the scenario with limited CAV penetration there will be mixed effects in terms of parking demand. But the design and strategies that will follow are a part of what might happen. Nonetheless it is important to understand the possible effects in multiple scenarios to have a debate among and hence understand the possibilities.

There are 3 major categories in which parking is divided in the present context On street Parking: Since in Scenario 1 there is a limited innovation and revolution for CAVs we assume that most of the cars are privately owned with a very small number of cars being autonomous and probably used as a car sharing or ride hailing. Private individuals will have cars with limited automation features like lane assist, parking assist etc. and hence overall demand for parking in a city context is going to increase rather than decrease. Overall demand of the car park places will increase or remain constant in this scenario and hence all the problems regarding inefficiency in terms of cost and convenience will continue to remain. There will be some neighbourhoods or in the city centre where free floating car sharing will become part of street parked cars.

A simulation for the International Transport Forum, an OECD agency, tested several scenarios for AV use. It found that in all cases, self-driving fleets eliminated the need for all on-street parking and up to 80% of off street parking. (Ticoll, 2015) 40

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Surplus stand-alone parking lots and structures will be redeveloped or turned into public realm.

Underground lots will present challenges; some may be repurposed for storage, but others will require creativity. AV mobility services may reduce the value of condominium parking spaces, currently priced at $40-60,000. (Harvey, 2013) Private parking lots will face challenges to their business models. For example the Toronto Parking Authority (TPA) operates about 17,500 street parking spaces. It also runs 186 off-street parking lots and garages with 37,500 spaces. In 2013 the TPA generated net income of $65 million on gross revenues of $130 million, of which $45 million was conveyed to the City. In addition, it paid $18 million to the City in property taxes (Toronto Parking Authority 2014). The City will face decisions about what to do with TPA assets, as well as whether and how to sustain this revenue stream. By the same token the City will likely lose growing portions of parking ticket and traffic citation revenues. In 2014 parking ticket revenue was $105 million.

Figure 24: On street parking in a European city

6.3 Parking demand In case of parking spaces in and around the city there is always a huge number of people circling the streets and property in order to access the parking space. In some cities as high as 30% of traffic can be the recirculating traffic in peak hours in city centres. So here we know that information regarding the parking space and its availability is something that needs to be shared on a platform that can be shared by everyone and hence these trips can be minimized. There are attempts by many different companies to solve this problem by introducing dynamic pricing and information system on parking occupancy. An app created by RTA (Dubai () helps the visitors in some areas to access the parking and know the information online. Various different types of parking spaces are highlighted and marked in that app. Which ranges from on street parking to parking in multi storey structures. But the fact of the matter is that is inefficient as the vehicles do not talk with each other of the infrastructure. In case of connected autonomous vehicles the information regarding the parking space availability will be available in the system and hence this inefficiency due to lack of information can be avoided.

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Building up on the study done in the previous chapter in order to determine the fleet size, there is no denying fact that fleet size and parking and interrelated in one way or other. The study aims to understand the demand of different parking spaces and its effective allocation. This exercise makes an attempt to understand the fleet size and how to manage this fleet in term of parking.

In this simulation exercise an attempt was made in order to calculate the active fleet and the passive fleet.

Active fleet will be the fleet that is required all throughout the day and it will be in motion most times of the day. Passive fleet on the other hand will only be active during the peak hours of the day and hence can be stored in the areas which is outside the city and can be procured when required and hence the space consumption in the city centre and the neighbourhood areas will be much lower than what it is now.

This strategy is something that needs to be modelled based on real time data and fleet size but for the academic work a simpler version was chosen in order to highlight the fleet management strategy with parking demand in the city.

Figure 25: Fleet and Parking management strategy bu understanding the demand of CAVs all throughout the day

Figure 26: Active fleet and passive fleet strategy

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So, active fleet needs to be available at a very short notice, simulations can be made based on waiting times and level of service desired. Then there is another type of fleet that will be required during the 2-3 peaks of the demand which means that this fleet can be stored near city of in the suburban areas depending on the space availability and where the fleet needs to be dispatched.

As it can be seen in the diagram that around 1000 CAVs will be active all the times of the day another 1500 are active in all the peak hours and hence can be stored in the nearby areas and other 2500 or more is only required during the peak hour. But it can be seen that some of the cars will make a bigger trip which might range for more than an hour and hence cars which are used in peak hour first part might not be able to make trips in the second part of the hour and hence the CAVs from the entire peak hour which lasts two hours needs to be used in order to make the parking spaces. In this exercise it can be seen that there is a strong relationship between the fleet and the parking and the strategy that should be flowed in order to meet the requirements of trips, fleet and parking spaces.

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6.4 Parking structure As it has been discussed earlier that parking is one of the most important problems that needs to be solved and managed and AVs will provide various opportunities to do in many different ways.

Here is an illustration of what will happen to a typical parking structure with the advent of AVs. There are multiple arguments to be discussed in this sphere. One of them is the Plan of space consumption of these parking structures. Even with limited automation features like piloted parking there is a great level of optimization that can be achieved with the same floor space.

Figure 27: Business as usual parking structure with standard parking isle and parking bays (Source: Mobility in chain parking study)

This is a typical layout of a parking structure that has standard 5.4m*2.7m parking stall size (American standards) and the size of the isle are about 6m wide. This allows for a 40cm buffer around the cars which is considered as a safety buffer at the speeds that are allowed inside the parking structures. These buffers are more when cruising speeds are more in this case the parking structure will be able to have minimum speeds. Hence in the plot there also needs to be space for utilities that includes the lifts and service stations it also needs lighting and other basic features and hence somewhere around 20% of the space is consumed in that kind of infrastructure. This is a typical parking layout of today and the exercise is to check how this typical layout will change with the different scenarios in which AVs will be able to intervene.

In this scenario we remove the safety buffer of 40cm from the length and width of the stalls and try to check what will happen to the capacity with limited assistance in parking with automated features and there no need to have such big parking stalls. We can see that the capacity of the same parking structures increases to 20%. Still in this scenario there will be a need of humans to park the cars and hence the

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Figure 28: Assisted parking feature with cameras and easier manueveribility in tight space (level 2,3) automation, (source: Mobility in chain Parking study)

infrastructure that follows will be present. Apart from the stall size even the isle widths are reduced since the cars with limited parking assist features allows for a better level of service to make the necessary manoeuvres. And hence after the geometric optimization the spaces will increase to about 20% of the original capacity. This kind of parking structures are likely to install in the recent times and can also continue in the Scenario 1 (limited automation). So, scenario will still have a better performance in structures of parking then the business as usual.

Figure 29: Fully functional piloted parking , no human assistance required within the property of parking structure ,(source: Mobility in chain, parking study)

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After reaching a complete geometric optimization scenario this scenario allows for the fact that piloted parking has been introduced and cars can park themselves without the need of human help. Hence all the infrastructure that needs to be installed on the parking floor can be used for accommodating more cars .In this scenario we can see that there is 62% increase in the capacity of cars. In this scenario something called stacking is introduced and hence cars can be stacked in 3 rows without the need an isle after every car parked. The cars in this scenario can reshuffle themselves to adjust with the given space and saves a lot space. But the cars are assumed to be owned individually and hence each car is a different unit and hence the reshuffle should not make the functioning of the structures with a lot of delays and hence the stacking is kept to just 3 rows. Since in this scenario the cars are assumed to be owned by individuals and there is a need a car at particular position to come out rather than any other car, this is the most optimized scenario.

This scenario corresponds to Scenario 2 (Private CAVs) in which features of limited automation and piloted parking have arrived in the short run and cars are individual vehicles and hence stacking has to be kept minimal in order to avoid a lot of useless reshuffling and hence a lot of dead kms. Even when level 4-5 automation is achieved in this scenario if the ownership does not change then optimization in terms of stacking is limited as it is for piloted parking.

Figure 30:Complete autnomous scenario level5 automation, single ownership fleets

This scenario deals with complete automation scenario with shared fleet of vehicles and hence each car is same as the other and hence stacking can be made more as compared to the scenario before. In this we assume that all the cars and identical or at least have a shared ownership and hence the need to shuffle is low. But it is recommended to have a 7.5m isle in the middle in order to avoid congestion and gridlock within the structures in case there is a peak hour that will require a lot of cars to move in and out at one particular point in time. This scenario resembles to the scenario 3 (MaaS) of the study and hence it offers twice amount of space /floor rather than the business as usual scenario and 40% more space from the piloted parking scenario. The extra wide 7.5m isle will help the in and out of AVs from the garage in the peak hours when the demand and volatility of the garage will be at it’s highest. In case of storage which is not used very often this width can be reduced and hence a further optimization can be achieved.

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6.5 Drop offs and street design There can be numerous ways in which the designers and the policy makers of today can prepare for an advent of revolution in autonomous driving and parking. Some of the important aspect include what is going to happen to the on-street parking and how is it going to change. I all the above-mentioned scenarios it is clear that cities will require more drop offs in their neighbourhoods and near their public buildings than of that of today. A lot of guidelines and by laws do have a recommendation about how the current parking space can be converted to drop offs and space for public use in future.

There needs to be strategy in which this conjunction between the function of drop offs and current day street design should play. There will be different kinds of drop offs with different space consumption and level of service will be required.

Categorically speaking there are two different kinds of drop offs that can be used in man formats with different lane and street configurations. One is the simultaneous drop off and other is parallel drop offs.

Two of them have different functions and there are many factors that come in play while designing these drop offs areas of future. These include whether the drop has a dedicated lane is it parallel or perpendicular to the building, what kind of land use is that drop off catering to and henceforth.

Figure 31: Series and parallel drop offs (source : Mobility in Chain, Parking and drop off study)

The neighbourhood in the cities with a lot of residential areas will perhaps require sequential drop-offs and can be made in the driving lane and no extra lane will be required since the amount of traffic on such streets is low. On the other hand, places like business, commercial centres that have an influx of large amount of people in very short amount of time will require something like simultaneous drop offs along with a dedicated lane and space for the complete segregation of pedestrian and car traffic for safety reasons.

There needs to be an attempt that the local design guidelines are able to differentiate between the kinds of drop offs that are required in a particular context. How the on-street parking of today can transform into the drop offs and pickups of tomorrow in the era of autonomous driving.

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In the scenarios that were discussed in this thesis the scenario 2 that deals with private autonomous cars there is a likely possibility that the number of drop offs in total in the city will increase much more than scenario 3. In scenario 3 there is a lot of car sharing and other modes of transportation that will come in play. Scenario 3 allows for very good amalgamation of drop off and pedestrian and cycling space, the assumption is that this balance is achieved because of the MAAS strategy for mobility. Scenario 2 poses to be a bit more brutal in terms of requirements for the drops offs and street design which seems to be more car friendly but overall these two scenarios will be very different from the designs of today and hence there needs to be attempt to explore this option in order to provide citizens with best quality of life and take the benefit of this technology for the betterment of all.

6.6 Adaptation in parking structure design Over time, vast areas of valuable urban land currently occupied by parking lots could be reinvented for a whole new spectrum of social functions. Creative uses are already promoted across the world during Parking Day, a worldwide event held on the third Friday of September, in which artists, designers and citizens transform metered parking spots into temporary public places. The same dynamic re-purposing could happen tomorrow on a much larger scale and with permanent solutions, leading to a reclamation of a large percentage of the urban fabric. Vacant lots could be populated with green areas, a variety of shared public amenities or “maker space” facilities, providing working tools — 3D printers, CNC machines — for design and fabrication. Potential uses for ubiquitous reclaimed parking area are almost unlimited, and their cost could be covered by the community or by private investors — eventually offsetting the city’s lost revenue from traditional metering. (Ratti, 2014) There are many ways in which parking structures can be reconfigured or the design can be made resilient for the adaptation of future uses. One of the important factors to consider is that cities will not be needing so many parking spaces and when parking is separated from the land use and its activities there is a bigger opportunity in terms of saving space in the cities and the outskirts. A lot of parking structures of today will be transformed into something else tomorrow or these parking structures should be able to reconfigure

Figure 32: Autnomous Parking of future (Arrow street architects)

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Figure 33: Piloted parking AUDI future initiaves

themselves to other uses. One of the challenge for the reconfiguration of parking structures is that the ramps are imbedded in the structure and the floor height is usually lower than a normal building that has a commercial or office use. So, in order to make the design more resilient for the future Carlo Ratti and Rem Koolhaas have worked together on a project in which the ramps of a parking structure are external and hence can be demolished in future. The floor height is increased from 2.8m to 3.5m or more in order to accommodate for other uses if the parking use is phased out from the city.

All the structure build in a close vanity of city centre or commercial hubs are likely to be phased out once AVs vehicles reach high penetration. Even with limited automation and piloted parking these changes can be seen. In both the scenario 2 and scenario 3 there will be some or many structures that will need reconfiguration. In order to cut the long story short, the parking structures of today need to have a resilient design in order to prepare for the future that might not need so many parking spaces.

In the below illustration it can be seen how a traditional shopping mall or a commercial complex takes away prime urban land to make parking spaces out of it. Because currently parking spaces and the ease of access to these areas define the business that these commercial centres and malls have to offer. But once we introduce piloted parking and autonomous parking for the AVs there is no need for the parking to be so close to the activity centre. It can be moved to a less visible and inexpensive piece of land. On city scale this shift in behaviour of completely decoupling parking from land use will result in a lot of space saving and hence it something that scenario 2 and scenario 3 will offer as soon as automated or piloted parking feature is introduced.

To conclude, in scenario 2 there are two major ways in which the situation can unfold. One being that the private CAVs will be parked in displaced parking lots or buildings in the neighbourhood therefore, will free up the space on the street which can be used for other purposes. Or the street cars will continue to remain as a way of parking cars in the cities and neighbourhoods. In the city centres the spaces will be freed up to provide for mass drop offs for different customers and since we have piloted parking the places like offices, 49

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Figure 34: Efficient use of land use and property value (AUDI urban initiatives)

malls and shopping areas will not host cars in the lots of on the streets rather than structures and hence in city centres situation will be more pedestrian friendly. But in the residential areas of the outskirts the cars will remain integral part of street design and hence limited space for other activities.

In scenario 3 the cities will have the opportunity to categorically choose the policies that are people centric and hence will also integrate the pedestrians and biking areas. The streets will be well equipped with pedestrian and cycling areas. Mass street parking of the cars will not be present and the streets will be equipped with drop offs and hence people can easily interact with CAV and other modes of transportation.

Small mobility nodes will be an integral part of an everyday residential neighbourhoods. These small neighbourhood nodes will host functions like drop offs for CAVs, pick up spots for shared and on demand CAVs which can either link you to the nearest public transport or will continue to take the journey on the private mode. The city centres will be connected with Mass transit and hence the parking requirement in the close vicinity will be minimum.

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7 Impact on Public transport Self-driving vehicles are poised to bring the next round of disruptive innovation. At first blush, it seems that the key advantage of an autonomous car is freeing the driver’s hands from the steering wheel (and, of course, allowing us to shamelessly text while driving!). The real transformational advantage, however, is that self-driving vehicles might blur the distinction between private and public modes of transportation.

Public transport as we know it now is something that is going to change drastically with the advent of increased AV penetration in the market. The change will be more drastic in the scenario 2 and 3 which will be a bit more radical than scenario 1. It becomes very important to understand the effects of services like on demand services, sharing and shuttle services. There are many drawbacks in the usage of public transport in the modern times and there is a possibility to induce efficiency in the practice of using public transportation.

Public transport is defined as a service in a shared mode of transportation to anyone and everyone who pays the prescribed fee for the service. Usually a public transport network never charges the money it actually spends on the investment and is often a kind of welfare activity to solve the problems of congestion, pollution in the cities. In a typical urban set, up the modes of public transportation are Buses, metro and trams. The main features of public transportation are: (Owczarzaka & Żaka, 2015) • • • •

Transfer of passengers on a massive scale with an application of high efficiency and large capacity means of transportation.

Widespread availability - guaranteed to any person that has the right to use public means of transportation.

Arrangement of passengers’ transportation on fixed, pre-defined routes.

Carrying out transportation based on a predefined time-table which results in fixed intervals/headways on particular routes.

Many scholars assume that that driverless cars can work as on demand public transportation with more flexibility than imagined before. A study by the OECD (OECD/ITF, 2015b) comes to the conclusion that 10% of today’s car fleet is needed to cover the existing demand in Lisboa, Portugal. Likewise, Bischoff and Maciejewski (2016b) estimate that 10 cars in Berlin can be replaced by one AV, and Fagnant et al. (2015) estimate a ratio of 9:1 for Austin, Texas. A more recent study in the same city with added simulation components such as a recharging infrastructure estimates that 6.8 private cars can be replaced by one AV (Chen, 2015). For Singapore, a study came to the result that 30% of the available fleet size would be needed (Spieser et al., 2014) and a possible reduction of up to 90% of fleet size in the Zurich region has been found in (Boesch et al., 2015).

For instance, a bus today with 50 seats could be replaced by 13 AVs with four seats. Given that all travels on that route remain the same, there would be 12 additional vehicles in terms of flow capacity. Looking at the needed storage capacity in the network with one bus being equal to three cars, one arrives at an increase of 400%.

In response to the current modes of transportation CAVs can offer the following: • • • •

Replacing regular fixed routes with more dynamic routing adjusted to local needs (time of the day) Providing more reliable service in terms of space availability and route options Using heterogeneous fleets in order to accommodate different kinds of trips vs standard buses or trams with particular capacity that we see now.

Replacing regular’s stops with more dynamic pick up spots that runs deep in the neighbourhood and build up structure.

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As said by Prof Carlo Ratti the future of transportation is going to change in many ways and the line between what is private and what is public mode of transportation will start to fade and hence we will be able to move towards a collective mode transportation. (CARACCIOLO, 2017), (SZELL, et al., 2015)

7.1 Public bus in an era of AVs It is fundamental to understand the effects of CAV technology on the buses of today to see them came out as AVs PTs of tomorrow. Buses, compared to railway, tram, or metro, present the most flexible modality to address on-demand services, as they don’t drive strictly in fixed routes. (Aloni, Optibus) • • • •

Incentive - Bus operators spend more than 60% of their operating expenses on drivers’, and they will push aggressively to find ways to eliminate this cost Regulation - Public transportation is a limited and professional segment which is much easier to regulate and enforce new rules Education - Passengers are more likely to ride a bus without a driver, as many are already used to such modalities in airport and theme parks Navigation – Public buses use mostly fixed routes in a specific geographical area and do not have to navigate across unpredictable numerous regions

Therefore, there are some very good reasons for buses to be the segment that will spearhead the autonomous driving revolution, and we may see it starting in just a few years from now.

A study by Princeton University showed that autonomous buses on the bus lanes of the Lincoln Tunnel, connecting New York City to New Jersey, could accommodate over 200,000 passengers per hour, more than five times today’s throughput. This shows how public space and budget can be saved (less road construction, etc.) (Lutin & Kornhauser, 2013) Buses are likely to run on long routes and on the routes, which have a high demand. The cities will still need fixed route high frequency transit. In the transition period it will be more common to have buses rather than trams (trams are a more rigid and expensive infrastructure to change). Buses are one of the most flexible modes of transportation and hence they will be able to survive in the era of increased CAV

Figure 35: Autonomous buses by mercedes

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penetration. So, in term of city planning and design strategies public transport buses that are unmanned is something that is desirable and city governments should push for this option. If we look at it in an objective way there will be other modes of transportation that uses CAVs and that is likely to compliment unmanned buses.

Figure 36: Autonomous bus by Japan basd venture capital between SB bank and Yahoo

NTD reports approximately 200,000 bus operating employees for fixed route services and an additional 100,000 employees for demand responsive services.

Part of the motivation for large vehicle sizes is to increase productivity per operator by virtue of being able to accommodate more passengers per operator. When unburdened by operator costs, the optimal vehicle size to best accommodate the volume of passengers and frequency of service that the market finds most attractive might well favour smaller vehicles running at higher frequencies. Such a service concept might substantially increase the overall appeal of public transportation services. (Polzin, 2016) One of the most interesting but unsuccessful trials was in Kutsuplus, a city-run ‘‘mobility on demand” transit service in Helsinki.10 WiFi-equipped minibuses roamed the city’s downtown core and a dispatch system would direct buses to passengers and dynamically update routes on the fly to pick up more passengers.

Pickup points were typically the nearest city bus stop, usually only a few minutes’ walk, and payment was arranged through an app - no fumbling with transit cards or cash needed. On the last day of 2015, Helsinki Regional Transport cancelled the Kutsuplus pilot program. There are a few suggested reasons as to why it failed. First, with a budget of about 3.2 million euros, the service was unable to get more than 15 buses running at a time (this is the scalability requirement). (HSL - Helsinki Regional Transport - planned to have 100 by 2017, and 2000 by the year 2020.) By the end of 2015, the 15 buses were operating for longer hours and serving more people, but it was not enough for Helsinki, which has a metro, 15 tram lines and a large bus system. Combined, Helsinki’s public transport network provides about 1.2 million rides daily. (Hensher, 2017) From the above example we know that even when on paper and in simulation AV buses can be very benefiting the business model around it and readiness of the local agency plays an important role to make such projects successful. Scalability is one of the other challenges that will be faced in implementation of 53

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different models in order to inculcate CAVs in the public transport scene. Nonetheless there is a wide opportunity in this field and there are various ways in which local government can take the benefit of this technology. Most important thing is to understand the local context, demand and practicality of a plan in terms of on ground reality of people behaviour and other important stakeholders.

What is exciting about this future prospect is that there are many smart bus operators in various jurisdictions throughout the world, and they can be part of this journey, make profits and take pressure off of the funds currently provided by government, resulting in a significant improvement in value for money to the tax payer, something that has been somewhat alien for many years in this very fragmented and protected sector in a number of jurisdictions throughout the world. The existing public transport should be able to delegate different roles to different private authorities and hence reduce the cost in terms of initial investment. All of these different users and service providers can be brought under the same platform by using MaaS strategy which includes common payment options and therefore, the citizens can enjoy seamless mobility and cities can have an excellent technology with minimum investments and risks.

A lot of mobility choices are based on the cost factor, this will be discussed in the later sections as to how cost, convenience and time travel savings help people to choose their mobility option.

7.2 Other modes of PT emerge One of the major concerns in terms of having driverless buses is that in future the trip composition will be more individualistic and there will be other modes of transport which might compliment or work against having a driverless vehicle with a capacity of 80-100 passengers.

The reduced cost of private vehicle access that may accompany driverless vehicles may pose a threat to public transport but also provides some possible ways out. Removing labour costs from driverless taxi/Uber type services, for example, may increase demand for those services, some of which may come at the expense of public transport although these services are increasingly becoming part of the ‘public transport’ mix. Similarly, increased personal travel in cheaper driverless shared cars may also reduce demand for PT trips. Such circumstances may reduce demand for some types of bus services as we know them today. Thus reducing mobility options for those at risk of social exclusion. The balance between providing services in driverless cars and small shared passenger vehicles that may be able to operate on a commercial basis and Government funded bus services of today that subsidize passenger fares needs to be seriously considered in the context of social exclusion and access to opportunity to different social groups. (Bus industry confideraton, 2017) On demand CAVs are like on demand ride hailing services that are present now. Uber and Lyft being the forefront runners in this kind of business model. In dense urban neighbourhoods and job centres, especially with the expected growing trend of urbanization, public mass transit will still be much more effective to transport the masses than small autonomous vehicles. However, it will undergo transformation with the advent of autonomous driving technology, which makes it much easier to dynamically change and adjust the original routing and schedule. Other modes that are appealing and are likely to come are shuttle services. For users that needs to go from point A to B in large numbers like offices, malls and entertainment parks shuttle Cavs looks a very attractive option and has a potential.

As the conventional knowledge suggests that modes of transportation with lesser numbers like 8-12 capacity are considered more flexible and can provide a wide range of services in the areas with less demand and still be profitable to run. The most important use of such services can be during the off peaks hours in order to provide the accessibility to areas that do have enough density or demand to successfully run a full-fledged public bus. This model can make cities more connected than ever. Furthermore, in off peak hours like in night time these buses or on demand shuttles can be very helpful to provide much needed connectivity at nominal rates since it is shared and cost is distributed.

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There are many recent studies that indicate that ride hailing services of Uber and Lyft have started replacing the public transport and has been considered as a separate mode of transportation. In 2015 Uber CEO claimed to have a traffic free Boston but the effects are quite different from what was imagined.

One study included surveys of 944 ride-hailing users over four weeks in late 2017 in the Boston area. Nearly six in 10 said they would have used public transportation, walked, biked or skipped the trip if the ride-hailing apps weren't available.

And a survey released in October of more than 4,000 adults in Boston, Chicago, Los Angeles, New York, the San Francisco Bay Area, Seattle and Washington, D.C., also concluded that 49 to 61 percent of ride-hailing trips would have not been made at all — or instead by walking, biking or public transit — if the option didn't exist. (LeBLANC, 2018) If we try to converge on the results of the studies it indicates that other modes of transport that is ride hailing coupled with autonomous driving has a very good chance in terms of market potential. The reasons being it is convenient, cheap and reliable and hence in order to achieve the best mobility options these new services will have to be taken into consideration in our planning process and as a mobility option.

7.3 PT in different scenarios Public transport options can work out to be different in different scenarios. As it has been discussed before role of PT comes in term of defining the scenarios itself. In scenario 1 which is closer to business as usual scenario and we assume that the car ownership doesn’t change and hence the situation does not change much from what it is today. But additionally, we have assumed that Public transport will not be able to meet the additionally needs of the population and its reach remains same. In this scenario of limited CAV penetration public transport ridership remains constant but since there is an increase in number of trips and population it will not be adequate. The modal share of the PT will decrease in terms of percentage.

Figure 37: Autonomous shuttle system EZ10

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In scenario2 it is assumed that private CAVs will take over and no automation occurs in terms of public transport and on the other hand the investments decrease and the traffic congestion raises to a certain extent. With limited options for the city government and of dealing with the existing problems of congestion PT will not be a very used mode of transportation. At the same time limited ride hailing services will be present in the cities which will meet a small portion of demand with sharing vehicles but its effect on the overall traffic is very low.

In scenario 3 which is the favourable scenario the city government and the local service providers take the benefit of the opportunity of providing good profits as well as spaces for people. So public transport like metros and trams will vanish from low density or low demand areas and will be replaced with high frequency low capacity buses or shuttles that are autonomous. These buses can be used in a dynamic environment and their routes can be changed based on the demand. It is more flexible and cheaper than the conventional mode of public transport. In order to compliment this set up there will be a lot of private and shared CAV services coupled with shuttle services in off peak hours which fill in the gaps. More than 70% of trips will be taken by a shared mode of transportation. This scenario provides for an array of services based on need and pricing and hence has a very good market potential as well acceptance from multiple stakeholders. This scenario will be able to benefit the PT network the most and it will be transformed into something different than it is now. The line between public and private transport will blur and something that is termed as shared transport will take over and transform the mobility choices of citizens.

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8 Transport Costs Transport cost is one of the most important factor that determines the mobility choices and is likely to stay the same in the future. The transport cost includes many parameters like traffic, congestion, time value and cost of fuel, operation & maintenance. Some cost are direct costs that are bared by the customers for a particular service other are indirect costs. There are various kinds of cost benefit analysis that make that make the estimates of indirect cost in terms of dollars or euros and hence help agencies as well as private individual to make an informed decision.

When AVs are in the market the finding from the study done by (Morgan & Stanley, 2014) says following: •

• • • •

Savings from collision avoidance will be $488 billion—although we note that since the Morgan Stanley paper, the U.S. Department of Transportation has stated that the 2010 societal cost of road crashes was $871 billion, or the equivalent of 6 per cent of GDP.2 (The direct costs were $277 billion, or 1.9 per cent of GDP.) Productivity gains from regained driver time will be $507 billion—based on average commute times of 25.5 minutes for the U.S., which is similar to the Canadian average of 25.4 minutes.

Fuel savings will be $158 billion—based on the improved efficiency of automated vehicles, reduced time spent driving around urban centres looking for parking spaces, etc.

Productivity gains from congestion avoidance will be $138 billion.

Fuel savings from congestion avoidance will be $11 billion.

On a global economic scale these benefits seem profound but when we dig deep into the question of having AVs on our roads there are many costs that needs to be factored. One other important observation is that even if the costs decrease at manufacturing levels it might not be necessary that the consumers will pay substantially low. Bu for the purpose of the research this question is kept out.

In this section we will be focusing on 4 different types of cost that are associated with motoring and advent of driverless cars.

• • • •

Private cost Cost affecting mobility choices Delivery cost Congestion & accidents cost

8.1 Private cost These are the cost that individual pays per trip that includes the cost of fuel, time and O&M. There are many studies that indicate what the costs that are associated with owning a car is and how these costs can drop multi fold once AVs are in market. While AV technology offers the potential of substantial benefits, there are also important costs. Ironically, many of the costs of AV technology stem in part from its benefits.

In this situation there is this loop in which the cost of driving will reduce and hence the VMT will increase as more people will drive (induced demand) and then it will lead to negative externalities of driving that are associated with congestion. It’s a double-edged sword which is very difficult to predict accurately.

(Anderson, et al., 2016) TCO is the sum of all the costs related to a car purchase and driving it over the period that one owns it.

Lipman and Delucchi (2006) include the following in their TCO analysis: vehicle purchase (as annual depreciation), fuel, insurance, maintenance and repair, engine oil, replacement tire, safety and emissions inspection fee (Ministry of Transport – MOT – test in the UK), parking, tolls, etc. Battery costs are also included when conventional vehicles are compared with electric vehicles. Social costs of emissions and noise are generally not included in TCO analysis because they are often not considered (or, at best, qualitatively considered) in individual vehicle purchase decisions. While TCO analysis may not have been 57

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very popular in the vehicle purchase literature in mainstream transport research, components of the TCO analysis are still used to characterize the vehicle attributes in vehicle choice models, which are more popular in the discipline. (Wadud, 2017) A recent survey in the UK found that fuel economy/running costs, size/practicality and vehicle price were the three most important factors to the consumers while purchasing their most recent private car. (B & N, 2010). In a study conducted on the Canadian economy (Godsmark, et al., 2015) the average Canadian household will save nearly $3,00 or approximately $2,700 annually after considering a 10 per cent rebound effect, in 2012 prices and activity levels as a result of automation. This represents close to 4 per cent of the total household budget, or over 5 per cent of total household consumption. This has not taken into consideration the reduction in the cost associated with reduced freight cost on the goods the particular household buys. But on the contrary, there are studies that shows that automation will increase the household expense. A study in UK suggested that on different income quintiles the cost of automation will not overcome the benefits. In an average low-income UK household, the cost of automation is 30% more than it is now and only the high-income households will be able to take the benefit of automation with reduction in cost up to 6% annually. The same study suggests that Taxis and private services are going to experience 30% reduction in 99% percentile of cases. The benefits in the trucking industry gets close to 14%. The study uses TCO analysis to understand the cost and the benefits of this scenario. (Wadud, 2017) On the contrary to this finding a study done by Victoria Transport Policy Institute (Litman, 2014) suggests that autonomous taxi systems will incur additional costs than they have as of today. These costs include • •

•

Additional VMT for the origin trips (10-20% in a dense urban environment, the numbers are more in case of suburban and rural areas) Cleaning and Vandalism (To minimize these risks self-driving taxis will need hardened surfaces, electronic surveillance, and aggressive enforcement. Assuming such vehicles make 200 weekly trips, 5-15% of passengers leave messes with $10-30 average clean-up costs, and 1-4% vandalize vehicles with $50-100 average repair costs, these costs would average between $200 and $1,700 per vehicle-week) Reduced services. Drivers often help passengers (particularly those with disabilities) in and out of taxies, carry luggage, ensure passengers safely reach destinations, and offer guidance to visitors.

In the same study it suggests personal automobiles typically cost about $4,000 annually in fixed expenses plus 20¢ per mile in operating costs. It is generally cheaper to use conventional taxis ($2.00-3.00 per mile) rather than own a personal vehicle driven less than about 2,500 annual miles or rely on car sharing services ($0.60-1.00 per mile) rather than own a vehicle driven less than about 6,000 annual miles. This suggests that autonomous vehicles will be a cost-effective alternative to owning a vehicle driving less than 2,500 to 6,000 annual miles, depending on cleaning and repair costs.

Although full automation in personal vehicles does offer substantial benefits for households in the wealthiest percentile, these benefits are still small in comparison to the benefits for commercial taxi operations. (Wadud, 2017)

8.2 Cost affecting the Mobility choices The cost incurred by people in order to take a particular service is something that is very important to understand the mobility choices of individuals in future. Despite different scenarios the physical cost and the perceived cost in terms of convenience, privacy and time is something that also dictates the mobility choices of different kind of individuals.

As mentioned in the report by (Albright, et al., 2015) today’s cars cost around 82 cents/mile for an individual this includes the fixed cost incurred by individuals and also the operating costs. But in the report the author suggested that this cost will be brought down to 43 cents/mile for shared CAVs. Since the capital 58

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cost comprises of over 75% of the total cost will be shared among different individuals. There are many authors that have given different definitions and numbers to what will the cost of an AV as compared to present mobility choices. It became imperative to compare these costs in relative and absolute terms to determine how the mobility choices of people will be affected in future.

First definition of cost of AVs for the future was given by (Burns, et al., 2013) that indicates that cost per mile of an AV can be as low as 0.15 cents/mile. This drastic reduction of the cost was given in the light of 3 major reasons. First being that Shared AVs will be able to utilise the cost in a more effective way, second being the better capacity utilisation and the third reason is that these cars will use less energy per trip rather than conventional cars. The case of New York, in which this new mode will be very competitive to the traditional taxis that cost around 5 $/mile. The base cost of the shared AVs in New York is 0.5 cents/mile which is a bit higher than the number of 0.15 cents/mile.

Figure 38: Cost of Private cars (traditional) and Autnomous cars with sharing (Fixed cost vs Operating cost), KPMG white paper 2015, page 14

In this graphic made by (Albright, et al., 2015) it can be seen that AVs will make the fixed cost very less if it shared among different people. Even the operating and the maintenance cost are highly reduced.

In another approach by (Fagnant, et al., 2015) where they also accounted for external cost of private vehicles of today that includes the congestion cost and accidents. In this the author assumes a flat 7000$ for installing the AVs technology which brings the cost per mile for operating an AV to be around a 1$/mile, this value is higher than (Burns, et al., 2013) but it is still very competitive with today’s taxi options which cost somewhere around 2-3$/mile at the very least.

Furthermore, (Litman, 2014) introduced cleaning and management costs which were discussed in the previous sections, these costs can increase the operating cost/mile for AVs to somewhere around 0.60-1.00 $/mile. But this cost will still be competitive to private taxis but somewhere equal to a private car. But it also depends how much the private cars travels in a year if it is less than 13,000 miles/year then it is better to use a conventional taxi. Autonomous taxi is even cheaper than conventional taxi so, it can be said despite the same costs AVs still have a better chance of acceptability and market penetration.

In another estimate done by (Johnson, 2015) the price of shared AVs to be 0.44 US$ per trip-mile (operating cost plus 30% profit margin). For purpose-built shared AVs used as pooled taxis, they estimate the price per trip-mile as only 0.16 US$.

Less rigorous and detailed, but more transparent estimates are provided by Stephens et al. (2016) and Friedrich and Hartl (2016). Stephens et al. (2016) find the lower-bound cost of fully autonomous vehicles

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used with ride-sharing to be less than 0.20 US$ per passenger-mile and the upper bound to be 0.30 US$ per passenger-mile.

From the various research’s it can be seen that AVs will be cost effective in terms of owning and well as operating it. In all these studies it is assumed that AVs will be shared rather than owned privately and hence the range can be same as that of cars sharing fleet but with an additional advantage in terms of cost saving that is driven from being electric, connected, fuel efficient etc.

The age-old question whether public transport will be able to survive in the era of AVs is something that is difficult to quantify. Since Public transport system involves a lot of different costs even when just operation and maintenance is taken into consideration, most of the capital costs are covered by the governments. But in order to simplify the argument, it becomes evident to look into the costs that individuals pay in order to access the public transport and how it can change when AVs come in the market.

A public transport system works on the economy of scales, the more the ridership the better the revenues.

But the increased in the marginal cost is less as the ridership increases (10% increase in ridership will mean less than 10% increase in the revenue). Usually transit costs are highest/ mile for short distances and a system would prefer more people travel small distances in order to have the maximum revenue from a given ridership. In the study done by (Institute of Victoria Transport Policy, 2017) it was found that the total operating cost for Bus and light rail is 0.94 cents/mile and 2.05$/mile respectively. But the revenues from the fare (which the individuals pay) are 0.24 cents/mile and 0.19 cents/mile. The difference is the revenue comes from the subsidy that the government provides for the transit. This range is similar to that of a shared AVs as mentioned in the previous paragraphs. If the public transport system stays business as usual there is a surely a shift from PT to the AVs by just considering the monitory benefits. Other benefits which includes, time saved, convenience and safety as not be considered in this case. Shared AVs clearly have a benefit from the traditional public transport system but what happens if these systems get an upgrade and they also have a revolution in terms of automation? As of now very little information is known to comment on this fact because even though the manpower that is used to run public transport is gone there is other kind of human intervention that is needed behind the scenes in order to maintain the network, which might apparently be more skilled than people who fix and maintain the train tracks currently.

In the recent study done by (Schwieterman & Livingston, 2018) examines when and why consumers choose an on-demand ride—or indeed a carpooled on-demand ride—over public transit in one major American city (Chicago). The group of researchers made a sample of over 3000 trips that were done by Uber and Lyft in the city.

Most of these trips were between 4-11 miles on an average, this is also the range in which Uber and Lyft compete with the transit in most instances, and other ranges have a bit stable ridership and are not affected from each other. The research aims to compare the average fairs for the average distance but in order to make it consistent with the approach of this section some values have been averaged to convert it from average fare per person to cost/mile/person.

So average value of 7.5 miles is divided by the average numbers in order to obtain the cost /mile.

In case of Chicago the local transit costs 0.35 cents/mile while on the other hand Uber and Lyft taxis cost somewhere around 2.35 cents/mile which is consistent with the cost discussed before for Public transport and ride sharing services. But in most cases the TNCs (Uber, Lyft) are faster than public transit in the rush hours (This observation is very particular to case of American cities where Transit is not as effective as it can be). Over 90% of total trips are faster with TNC than with public transit, it can be clearly seen that transit is not able to meet up with the needs of the people.

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Even though when these time values lost are compared with average salary it was found that the riders were paying 4 times more than the value of times that would have been saved. In this sense it can be seen that even when time benefits are converted to money public transport seems to be the better alternative.

The question is why people chose a private/shared mode over the public transit.

In the report the author has given various reasons for the same, time isn’t the only factor under consideration when choosing a travel mode. Maybe it’s raining, or late, or your shoes hurt and you don’t feel like walking. Passengers might overvalue the convenience and perceived safety of one mode or another. Then there’s the cost of uncertainty: Transit trips can be unpredictable, and there are wait times and transfers to consider. (Bliss, 2018)

Figure 39:Personal Transport cost comparison

All in all, it was concluded that the more comfortable and reliable the journey in the bus seems to be the better will be the ridership despite some fluctuations in the cost.

If the same logic is extended to AVs it can be said that in terms of comfort, reliability and time values saved it clearly has an unfair advantage over traditional public transport. But, there are some actions that the local government under the MaaS strategy can do in order to regulate the ridership and resource distribution. Since it is well known fact that if everyone starts to move with cars the urban environments in the cities will degrade and no considerable benefit will come about from the autonomous vehicles.

The city government along with the partnership of different companies can regulate the occupancy for shared AVs in the peak hours. For e.g. in peak hours private taxi is 10 times more expensive than normal rates, public transit that runs on a certain dedicated network with an established demand will be virtually free for the riders and if someone want so share their AV with the maximum occupancy they will not have to pay the surge. In this way the city will still be able to keep a fully functional multi modal environment.

The kind of services the cities will host will depend a lot on how the local governments forms ‘partnerships with different service providers. Technology is always agnostic so it can be used to solve some of the problems or make them worse. The most important role is to be played by the local government and people of the community.

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8.3 Cost from accidents & congestion Another important characteristic of transport cost are the costs that are incurred by collisions which includes the time lost on work days by minor injuries, money spend on medical emergencies and treatment as well as insurance.

“KPMG estimated the current accident expense could increase from almost $14,000 to roughly $35,000 by 2040” (Albright, et al., 2015) In the United States, the time attributed to waiting in traffic in 2014 was 42 hours per person, a loss valued at $160 billion nationwide (Fox, 2016) Globally, 1.2million deaths occur each year resulting from car related accidents and another 50 million are seriously injured. Road accident costs, which encapsulate injuries, property damage, and lost productivity, are estimated at $518 billion globally, costing individual countries from 1-2 per cent of their annual GDP. Significantly, human error is the critical reason for 93 per cent of accidents. (Johnson, 2013) “We estimate that at 90% AV penetration, the benefit to Toronto is 12,000 fewer road accidents, 38 fewer fatalities and many fewer injuries, with cost savings of $1.2 billion”. (Ticoll, 2015) Even though the benefits in terms it is a well-known fact that in term of commercial operation cost matter more than in owning a private car. Private car ownership is more influenced by convenience, role of car as a social symbol and overall connectivity in the local area. Families earning a certain amount of money living in a village or suburbs are more likely to own a car or even more than 1 car than families earning the same in the dense urban environment where an array of mobility options are available.

Congestion cost is one of the most expensive cost that a city bears with increased numbers of vehicles in the city. The congestion cost includes physical capacity bottlenecks, traffic accidents, road construction, poor signal timing, and travel time cost. AVs have the opportunity to reduce these costs. In a study done in Toronto region Board of trade found that in 2011 cost of congestion for 6 billion dollars and this cost if expected to increase to 31 billion dollars till 2031 if measures are not taken to curb this problem. (Ticoll, 2015) Secondly, a lot of cost is associated with accidents in a 2007 study commissioned by Transport Canada, road collisions had a societal cost of $62 billion, or the equivalent of 4.9 per cent of GDP that year. By comparison, the U.S. societal cost estimate is $871 billion6 (2010), or the equivalent of 6 per cent of GDP, and a direct cost estimate of $277 billion, or 1.9 per cent of GDP. In 2011, there were 2,006 fatalities on Canada’s roads.

Current roads congestion in Europe costs 130 billion euro annually and the total external costs of motorized traffic are estimated at 270 billion euro per year, around 4% of Europe's gross national product. Again, the average traffic speed in European cities is estimated only 15kmh. (Galizzi, s.d.)

One study estimated that total congestion costs in Canada’s nine largest cities were between $3.1 billion and $4.6 billion in 2006. The range was based on the congestion threshold, or the speed at which we start “counting” congestion. The higher end of the range assumed that highway congestion “started” when vehicle speed declined to 70 kph. The lower range assumed a value of 50 kph. (Godsmark, et al., 2015) If the AVs in any form are able to reduce this cost to 80% it will be a lot of saving in terms of life, insurance cost and medical expenses in the case of an emergency. (Godsmark, et al., 2015) Thirdly, congestion cost includes the Values of travel times saves (VTTS). In the UK, on average, a driver spends 274 hour a year behind the wheels, which cannot be used for any useful purpose as nowadays driving requires full attention from the driver for the entire time. However, full automation can relieve the 62

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Figure 40: Cost of congestion in different countries (Centre for Economic and business research , 2014) page 60-67

driver of his/her driving duties so that the driving time can now be used for other in-vehicle activities.

Combined with the proliferation of mobile information and communication technologies, this extra time can be used to improve individual productivity, which has been estimated to be ÂŁ20 Billion for the whole of the UK. (Wadud, 2017) 63

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A study done by CEBR (Centre for Economic and business research , 2014)regarding the cost of congestion in 4 developed economies suggests that the cost of congestion will rise from $200.7billion in 2013 to $293.1 billion by 2030 – a 46% increase in the costs imposed by congestion. The total cumulative costs over the 17 years, across all four countries, amounts to $4.4 trillion. This is likely attributable to a combination of an average increase of 19% in total passenger vehicle miles travelled and a 14% increase in freight miles travelled between 2013 and 2030, driving the aforementioned 6% increase in annual hours wasted due to congestion.

The report also suggests that the effects will be worse in capital or large cities within these developed economies. The aggregate costs across the four cities featured in this report on a city economy-wide basis are estimated to increase from $46.6bn in 2013 to $75.9bn by 2030 – a 63% increase in the cost imposed on households in these cities as a consequence of congestion.

AVs provide an oppurtunity to make these costs under reasonable conditons coupled with all the mobility strategies that involves Parking demand assessments, smart car sharing optons, shareways, accessible and reliable public transport networks and also other seamless options of using AVs which includes sharing, ondemand services and shuttle services for effective and efficient management of individual mobility behaviour in future.

8.4 Parking and Land cost Parking has long been debated as something that takes a lot of resources and adds very little value to the urban environments. Till recently in the popular literature the cost of parking in terms of construction, maintenance, space utilisation was considered balanced in terms of convenience it provides for the car that is part of our mobility and hence freedom. But as the society is moving away from a car driven environment with alternative modes of transportation gaining popularity solving the problem of parking has become more important than ever.

More importantly, parking has a huge impact on housing supply and construction of new homes. First is the issue with space in developed world scenario and the other is the high cost people as well as developers have to pay in order to build a parking space. On an average it cost somewhere around 35,000-45,000$ to construct and underground parking space in building. In both UK (Adams & Leishman, 2008) and USA (Chapple & Chatman, 2012) especially in the state of California the parking price has caused a lot of negative impact in the housing supply. Increased parking provision ends to reduce development density by consuming land that could otherwise be used to increase the size of buildings, thereby reducing housing supply.

These are the major burning issues with parking in the current context and hence there is an opportunity to address some of the issues and how it will impact the future of mobility.

As we are aware and has been disccused before the real cost of parking is something that is way beyond what can be conphrihended. But this was supposed to be considered as a convnience cost in terms of attratcting more people or customers to the land use. All the commercial and office buildings would provide parking in the close vinicity in order to give the convinience to the passengers. There has been many attempts to separate parking from land use and most of the attempts have not been effective. But systems like self driving cars , piloted parking will provide for the oppurtunity to decouple the parking from the land use.It will no longer be necessary for the land use to provide for parking.

Parking will be a big cost saving if AVs penetrate markets effectively. Those customers that will choose to have an AV or a mobility on demand will have forgotten the pain of finding a parking spot in the city centres.

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In this context on an average a parking lot on surface costs about $15,000 and around $45,000 for underground parking. These cost do not include the land costs and manitenance costs every months that dfferent parking systems incur.

Private parking lots will face challenges to their business models. The Toronto Parking Authority (TPA) operates about 17,500 street parking spaces. It also runs 186 off-street parking lots and garages with 37,500 spaces. In 2013 the TPA generated net income of $65 million on gross revenues of $130 million, of which $45 million was conveyed to the City. In addition it paid $18 million to the City in property taxes (Toronto Parking Authority 2014). The City will face decisions about what to do with TPA assets, as well as whether and how to sustain this revenue stream. By the same token the City will likely lose growing portions of parking ticket and traffic citation revenues. In 2014 parking ticket revenue was $105 million.

(Ticoll, 2015) In a simulation done for the city of Atlanta it was found that total of around 25,000 parking places will not be required for an inventory of 500,000 parking spots.The study took into accound various scenarios of costs as well as land uses in order to compile the above numbers. The simulation results show that parking land use can be reduced by approximately 4.5%, once the AVs start to serve 5% of the trips within the City of Atlanta in both charged and free parking scenarios. The results also reveal that each AV can emancipate more than 20 parking spaces in the city. (Zhang & Guhathakurta, 2016) There are two ways in which the AVs can affect the cost of land and infrastructure in the city, firstly if AVs reach a certain threshold in terms of numbers in the traffic mix we will be able to isolate some of the effects of AVs on parking and land use. AVs will reduce the need for having a parking lot in the close vicinity of a commercial or residential area saving a lot of space in the downtown areas, these cars will be able to park themselves. Even with level 3 automation which has piloted parking these effects can be noticed.

Hence parking will be concentrated in inexpensive areas in the vicinity of the city not to mention the efficiency in terms of space utilization will increase as well. Secondly, there will an opportunity called Robo taxis which will not need to be parked unless it is required absolutely, the number of taxis can be adjusted according to the demand algorithm. Hence, cars or taxis in general will have more trips per unit of time and hence less need to park them somewhere. (Anderson, et al., 2016)

8.5 Platooning and delivery cost “Platooning refers to the practice of multiple vehicles following one another closely, leading to reductions in aerodynamic drag for all of the vehicles, but particularly for the vehicles in the middle of the pack.

Platooning may also increase roadway capacity, helping to reduce congestion as discussed above, and reducing the need for roadway capacity expansions.” (Wadud, et al., 2016) Drag reductions and energy efficiencies for platooning depends on the shape and size of the vehicles along with how many vehicles form that queue. For two sedans 1m apart the drag reduction can be around 10% (Zhu & Yang, 2011).For platooning containing mixed types, drag reductions between 20-60% is reported (Schito & braghin, 2012). For a long platoon of vans (five or more vehicles) separated by 0.5–1.0 vehicle lengths, average drag reductions between 45% and 55% have been reported while reductions of up to 60% have been reported for the vans in the middle of a platoon with short following distances (less than half a vehicle length.

Heavy vehicles are one of the lifeline of our everyday commerce and the economy and hence self-driving technology is something that will play a very crucial role in management of heavy vehicles and traffic that comes along with it. It is also expected that heavy vehicles will take the precedence in terms of adopting the autonomous driving as it will be beneficial in terms of economy but at the same time trucking industry is likely to suffer a huge loss in terms of employment.

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In the short run the drivers are going to be essential part of the autonomous driving in heavy vehicles but slowly they are going to be phased out when machine has become capable and reliable to do the job from door to door. Currently Uber has launched its trucking facility in which it will provide employment to drivers and only on the highways the trucks will be autonomous but they will need the drivers in the start and end of the city in order to pass through dense and comparatively unpredictable traffic. In short run it was estimated that this will get more employment under the pipeline since the trucking industry is already on shortage and the emergence of e commerce and other activities will generate more flow of goods in the spectrum. But in an objective way Uber is going to phase out all the human drivers once the technology is available and reliable. So, the employment concerns are going to be there for the governments to handle in long term, this is something that is going to happen to a lot of other fields where technology is going to take the human work.

Daimler is already experimenting in Nevada with a new version of its 18-wheeler – the Freightliner inspiration. This test vehicle—which incorporates sensor, radar, and camera technologies—also offers some restricted self-driving highway capabilities. With a potential for more efficient, safer, and profitable use of their fleets, long haul carriers could potentially lead in autonomous technology adoption and be a leading indicator of marketplace feasibility. (Albright, et al., 2015) Owners of truck fleets have economic incentives—higher vehicle utilization being one provided that a human who has hourly caps is no longer behind the wheel—to embrace this technology. And now we are beginning to see significant progress in this area. There might be a situation in which there will be one human driver for a whole convoy of 3-5 trucks. This human is capable of taking decisions on its own and the other machines can follow the lead if the need arises.

For example, in April 2016, a caravan of roughly a dozen autonomous, semitrailer trucks—for the first time—finished a trip across parts of Europe. The project was designed to create a system that allows commercial trucks to follow one another closely, which would reduce drag, improve safety, and potentially create economic growth in the traffic and transport sector. (KPMG, 2017) Another big player in the market which has recently launched its Semi truck is Tesla and it has been seen as one of the pioneer in terms of autonomous driving, electrification and fleet enabled driving. Here are the details of the performance of a fleet of tesla.

There are few reasons why the trucking industry is likely to adopt the technology of autonomous driving before passenger. Human factor of trucking costs a lot of money for the service providers and sometimes it’s half the cost of the overall fare. Second, there is a lot of inconvenience and laws involved which mandates the driver to stop the truck and rest and hence adds the time value to the cost. Once human will be free from this package, the goods can move along the boundaries without any legal constraints and added cost.

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In the following graphs it can be seen that the electrification coupled with autonomous features can add a lot of value in terms of savings of cost which is one of the deciding factors in any transportation format. So, a convoy of tesla will cost somewhere 0.80-.90 $/km which is in range of a convoy of trains and hence the goods travelling through roads might see an increased volume in the decades to come but eventually when the technology matures and automation has reached all the other modes of transportation things are likely to stabilise after the shock.

Figure 41: Cost of an electric truck (Tesla ) vs traditional diesal driven truck

Figure 42: Cost of platooning electric trucks vs Diesal trucks

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9 Ethics safety and insurance policies for driverless cars Driverless vehicles are widely anticipated to represent the future of transport. (BAMONTE, 2013) As it has been discussed in the previous sections that driverless cars have two different ways in which it is likely to appear in the market. One way is the route of partial automation which the traditional OEMs are using and the other is complete autonomy which the tech giants prefer. In both the cases the end result is complete autonomy. By progressively extending the capacities of these technologies, engineers will be able to produce vehicles that can drive in a larger and larger set of environmental and road conditions, as long as a human being is available to step in should conditions exceed the capacities of the vehicle to handle them safely. (Gordon & Lideberg, 2015) While these systems offer promise of significantly improved safety, they bring new kinds of safety challenges that must be managed. The most prominent of these challenges is cyber security and the risk of hacking. This has, in fact, been the primary focus of early legislative efforts in the U.S. on automated vehicles. On the one hand, framing car hacking as an issue of automated vehicles in particular is a bit of a red herring — we have seen that the roughly 200 million internet connected vehicles today are themselves prone to hacking before we introduce any automated functions, and the prospect of actually disrupting a vehicle’s controls would take a complex and sophisticated attack. (Zon & Ditta, 2016) But how will we reach this stage of complete autonomy will follow a gradual path and there will be a period of mixed automation and traditional cars. The major arises is how safe the AVs have to be in order to reach market acceptance and penetration. With the current technological development, a lot of researchers are certain that making the technology better than humans is something that we will be able to achieve in the short run let’s say next 20 years. At the same to make the technology 100% safe in order to make it available in the market is something that might take a lot more years and research in order to achieve it. Should the policy makers deny the customers this right to have technology because it’s not 100% safe?

These are the questions that needs to be discussed among different policy makers in order to achieve the best results and making use of the opportunity the technology has to offer.

9.1 Freeing people from the necessity of driving?

In today’s world a lot of people drive because they have to rather than they want to. There are obviously some people who are addicted to certain kind of cars and driving as an experience to relax themselves and makes them feel better. But for the most part these driving hours are necessary in order to reach places.

One of the major arguments in this favour is that people waste a lot of time in driving every day. As the evidence suggests most of the people most of the time drive alone and they are work home related trips.

Plus, there has been an increasing frustration and concern with being stuck in traffic as well as finding a place to park these cars. One of the study done by Boston consulting group suggests that the most people are willing to opt for a driverless option because of difficulty to park the cars everywhere they go. In the developed world and in the dense city centres the problem is worse. But, the driverless cars provide an opportunity for people not to drive.

It is possible that the types of car crash that could occur from the failure of autonomous technology would be of a more severe and less regular character than those caused by human error. While severe accidents can also be caused by human error, it is rare that drivers do something blatantly in contravention of road safety, such as drive the wrong way down a motorway. A computer miscalculation or a faulty reading from a sensor could lead a car to do something that a human driver would instinctively realize is inappropriate.

This could potentially lead to unusual and more complicated types of accidents which are hard to predict the nature of. (Maynard, et al., 2014) 68

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Partial automation can be a very dangerous situation from a safety point of view. One of the compelling argument against partial automation is that humans are a very lazy species and cannot take control in an emergency situation when we have not paid attention for the rest of the time.

As journalist Dave Roberts asks: “can we trust human drivers who are inattentive 75 per cent of the time to pay attention the right 25 per cent of the time, and to make the right decisions?” (Zon & Ditta, 2016) In this situation the most difficult and challenging aspect is what will happen in the interim state, the concern is graver for human drivers than AVs. Human drivers will respond differently to a situation than automated systems, making it more difficult to design safer systems. In the longer-term, we ultimately face the question of whether allowing people to manually operate vehicles makes sense, given the public health and safety implications when automated systems are demonstrably safer. At higher (but not complete) levels of automation, designers face the challenge of an “uncanny valley” where the human driver might only be controlling the vehicle 25 per cent of the time, leaving a paradox where the safety technology creates the danger of inattentive and inexperienced drivers. The system will be very difficult to design and to make the safe for humans in driverless dominated environments. (Zon & Ditta, 2016) Another issues that arises from partial automation and partial penetration of driverless cars is rise of new and different types of conflicts on the roads. There will be new kinds of accidents that will arise from this situation e.g. an accident between a fully autonomous car & partial AV, a collision between AVs and traditional cars and pedestrians. These are the conflicts that will arise and will be termed as new conflicts.

At present there are many different circumstances that can lead to a driver’s inappropriate situation assessment, inattention or distraction. These have been calculated as contributing to as much as 10-30% of road deaths. The people in favour of AVs have always argued that it will help to reduce the accidents that is caused by human error, this argument is the face of implementing autonomous driving on the cities. The concern for safety is something that will also have a political backing. (Ellen Townsend (ETSC), 2016)

Figure 43: Currents accidents vs Future accidents with driverless cars

In the above diagram it can be seen that apart from taking away the conflicts and the accidents that usually happens now due to poor traffic management and human error will no longer happen but at the same time a new class of accidents and conflict will arise on the roads which needs to be discussed by policy makers in order to make an informed decision. These accidents will arise from the interaction between a machine and human driven car.

The potentially drastic reduction in incidents per vehicle will be somewhat offset by the increased severity incurred in each accident, given the greater likelihood of higher priced vehicles with more costly technology underpinning the autonomous capability. KPMG estimated the current accident expense could increase from almost $14,000 to roughly $35,000 by 2040. (Albright, et al., 2015) 69

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There are many different companies and nations trying to experiment on driverless cars with partial automation. There is an obvious chance that in a future not too distant from now when these cars will become efficient and reliable the human driver is going to stop paying attention and overtime the skill will deteriorate and hence the ability to take the course of action in case of emergency becomes more dangerous than it already is currently. Human drivers will cease to pay attention in this situation. (Jamson & Meret, 2009) In this perspective it can said that in order to test whether an autonomous car is fit for urban roads or not there needs to be a methodology which provides us with accurate results. As suggested by David Stevens in (Moore & Lu, s.d.) In the case of autonomous vehicles, the relevant incident to measure cannot be “fatal events” since driverless cars are not a currently available commercial product, and consequently, there has been no opportunity to collect such data. Instead, the “mean failure distance” is used. This distance is equivalent to the average number of autonomous miles driven per required human intervention. This measure will help us to better understand the readiness and the safety features that the AVs will provide in the long run.

One solution would be to provide the vehicle with the capacity to monitor whether the ‘‘supervisor” was paying attention to the route, traffic, and road conditions. Vehicles could sense whether the driver had their hands on the steering wheel, track their gaze, and monitor signs of physiological arousal. If the human driver ceases to meet some predefined set of conditions, the vehicle could insist on manual control or safely slow to a halt. This is, for instance, the approach taken by the Tesla autopilot feature, especially after the notorious crash in Florida. (Lambert, 2016) But for these conditions to occur the driver should pay attention to this feature and not bypass the security warning that the vehicle provides. (sparrow & Howrd, 2017) In order to conclude on this section, we can see that eventually the driverless cars will become safe enough for driving and at some point, not so far in the distinct future it will be safer than human drivers. The technology will become safe, reliable and robust in 99% of situations will topple the balance of having human drivers out of the equation. But one of the most important question that arises is will humans be able to drive once the cars have become safer than human driving. What are the odds and arguments in favour of this situation is something that needs to be explored.

9.2 Should humans drive in the age of driverless cars?

Individuals have right to risk their lives in the way it does not impede on to the responsibility of any other authority or individual. But as policy makers and law enforcers it has be clear that anything that puts at risk lives of many people altogether s considered as a public health risk and it needs to be cleared out. So, in this argument when we look at from an individual perspective we see that there is no harm in human divers driving when AVs become mass produced, widely accepted and comparatively safer than the human driver.

But alternatively, from a policymaker’s perspective this is something more complicated because of the issues it will give rise to. In the same pretext we accept as a policy that drunk driving puts yourself and other road users at risk so we do not allow such behaviour in our policies.

To draw the parallels from this theory it is seen that human drivers will act as drunk robots and their efficiency will be evaluated in the same context when we evaluate drunk human driver vs. normal human driver, one being safe and other being public health risk. (sparrow & Howrd, 2017) First, and most obviously, a test case might arise in which a human being is in control of a vehicle that is also fitted with a state-of-the-art autonomous driving system when it causes someone other than the driver to be severely injured. The injured party – perhaps an occupant of another vehicle, a pedestrian, or a passenger in the vehicle – will then sue the driver, insisting that the latter was negligent in taking the wheel. If it can be shown either that the autonomous driving system was much less likely to cause a crash 70

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than the human driver, in general, or in this particular situation, then the plaintiff’s case is likely to be granted. In the short term, this will establish a precedent that to take control of a vehicle fitted with autonomous driving software is to open oneself to significant legal jeopardy. In the long-term, it is likely to generate legal and political pressure to make it illegal to take the wheel of a vehicle on a public road. (Zon & Ditta, 2016) Second, an injured party or their relative, or perhaps the government itself, might take the manufacturers of vehicles without autonomous driving capability and/or those that allow optional human control of the vehicle to court for manufacturing an unsafe product. Once autonomous driving systems become safer and more reliable than human drivers, placing a human being at the control of the vehicle will produce casualties that were foreseeable. Indeed, it would be unreasonable to believe that this would not place people’s lives at risk. At some point, then, providing the option of manual control will fail the ‘risk-utility test’ and will be a violation of the standard of care that manufacturers owe consumers. (Marchant & Lindor, 2012)

9.3

The Trolley problem

From a liability stand point it becomes difficult t integrate the human drivers in the scenario when AVs become safe and widely used. Other issues include the fact that insurance for a car with human driving possible will be more than an autonomous car since we know the risks that are associated with it. Insurance scheme can also be included in the service fee in case autonomous vehicle is not used for private reasons.

This scenario leads to other major issues of pedestrians and cyclists and what will these cars do in the case of an emergency. The trolley problem is something which has been around in philosophical debates for centuries and there are many ways it can be dealt with and explained about.

So here is the explanation of a classic case in context of trolley problem. A person is riding in the driverless car and there seems to be a possibility of accident and it has to choose whether to kill the couple of pedestrians on the way or to save the passenger at all cost. It is well known fact that from a utility point of view it will be better to safe more people as much as you can even if that includes killing the passenger of the car itself. On the other hand, it can be based on a libertarian perspective which if his car follows it will save the passenger at all cost and kill people on the way as well as destroy the property that comes in the way.

So, in the utilitarian perspective which will be a preferred option for policy makers will not be a good marketing strategy for the service providers and hence no one will be willing to buy a car that will not protect you in case of an accidents and hence will not work. On the other hand, if the car saves the passengers and kill the pedestrians who has the first liability and responsibility in terms of the fault. Will the technological company sued for or they will escape this punishment by saying that it was said before use and the blame will be shifted to the passengers who were in the car? Imagine the case when the driverless cars still have possibility of human control. But in the case of emergency the driver was not able to take control and autopilot feature took the decision who will be responsible in such cases.

There are many unanswered questions in this realm and the policy makers need to start debating and negotiating with all the stakeholders before it becomes a problem that is more of a mess than a policy in order to avail a service which has as indicated multi facet benefits.

Regardless of the scenarios mentioned in the report we can see that when AVs come in there are too many issues that will arise in the medium term. In most of these analysis in the long term the market is going to adjust and new systems for industries that will be affected by AVs will come in play. But it is important that an informed decision is taken by the policymakers in order to facilitate the smooth transition of this technology in the market and in our cities.

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9.4 Impact on pedestrian and cyclists Cyclists and pedestrians are classified as vulnerable or unprotected road users. Contrary to car occupants, pedestrians and cyclists do not have a protective 'shell' that reduces the impact in case of a collision or a fall. As a consequence, they have a high risk of getting seriously injured, in particular when colliding with much heavier vehicles, even at relatively low speeds. (Vissers, et al., 2016) The (WHO, 2015) reports that of all road fatalities worldwide pedestrians make up circa 22% and cyclists circa 5%, implying that, as a group, they contribute to over one quarter of all road fatalities worldwide.

There are substantial differences between different regions in the world which can be seen in figure below.

In the European region, for example, the share of pedestrian and cyclist road fatalities is somewhat higher.

According to the WHO figures, these averages are circa 27% for pedestrians and circa 4% for cyclists.

There are many ways in which AVs will impact the life in the cities. But one of the most important aspect is what will be the impact on the pedestrians, safety and what kind of infrastructure and practices will fall in place for the people in their neighbourhoods and what will the life that these AVs will offer to people.

One of the major topics of study has been the interaction of humans with AVs and how will this enforce the

Figure 44: Distribution of deaths related to accidents by continents (WHO, 2015)

behavioural change in humans and pedestrians. Other question arises that deals with the fact that what will happen to the signalized junctions and what will be there future. Will we still continue to have a phase for pedestrians to cross or they will be more dynamic based on the demand of people wanting to cross a particular junction.

Pedestrians routinely play the game of chicken. Crossing the street, even at a marked although unsignalized crosswalk, requires an implicit, instantaneous probability calculation: what are the odds of survival? The benefit of crossing the street more quickly, rather than taking a long detour or waiting for a gap in traffic, is traded off against the probability of injury or even death. Pedestrians know that drivers typically have no interest in running them down. So why not simply step out into the street and assert the right of way? In 72

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part, because they also know that there is a small probability that the driver is inattentive, intoxicated or sociopathic, or that the vehicle may be unable to stop in time. Moreover, because local norms typically dictate that pedestrians wait for cars to pass, drivers do not expect them to cross, amplifying the risk. In most cities, this tiny risk of injury or death keeps pedestrians firmly on the sidewalk, children (and dogs) on a short leash, and traffic flowing smoothly. In particular contexts such as college towns, in contrast, the norms are reversed; drivers adjust to the unpredictability of pedestrians and modify their speed and behaviour accordingly. (Millard-Ball, 2017) As mentioned by (LagstrĂśm & Lundgren, 2015) there are major 3 ways in which the AVs will interact with humans. The cusp of this finding is that the pedestrian need a way of communicating intent from the vehicle in order to feel safe.

GESTURES This concept is based on the idea that the vehicle can be equipped with sensors that recognize the pedestrian’s gestures, such as waving. The vehicle then gives the pedestrian feedback by using for example one of the visual concepts

Figure 45: an illustration of for pedestrians in the era of autonomous cars

INFRASTRUCTURE Dedicated infrastructure could be used to display information. Since the vehicle is able to communicate its intention digitally this kind of solution could provide the information to the pedestrian and handle the interaction. For example, a dynamic crossing could light up when it is safe for the pedestrian to pass.

WEARABLE DEVICES By using devices already in use by a large number of pedestrians, such as phones, the vehicle could connect to the device and share its intentions. The device can then inform the pedestrian via an auditory or visual warning.

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Figure 47: An illustration of gesture and hand signals for the driverless cars to stop

Figure 46: An illustration of a communication device for informing driverless cars about the intention of pedestrians

In the popular literature the gestures are understood to be an effective way in which humans will be able to communicate with the CAVs and hence will be able to tell the intent of the car. One of the biggest questions within the vehicle-pedestrian interaction problem is how to successfully communicate the intent of the vehicle with the surrounding pedestrians in a way that is efficient, comfortable, and easy to understand.

A research published by (Matthews, et al., 2017) suggests that there should be a way to solve the deadlock which is defined in a situation where the AV wants to drive ahead and the pedestrian wants to cross the road in an unsignalized junction. So, in order to prevent this situation from a safety and convenience point 74

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of view there needs to be an ICT and communication system that needs to be part of this interaction. In the results from the study it was found that n ICS can help in resolving potentially dangerous and inefficient deadlock situations by 38 percent. Both the real-world testing and simulations were designed to evaluate how trust is affected when a pedestrian encounters and autonomous vehicle. It was seen that trust is dependent on how comfortable a human is around the vehicle, how much prior knowledge they have of the vehicle, and the distance the vehicle is away from the human, among other factors. While this is true for most machinery, this is one of the first tests involving autonomous vehicles that confirms this holds true for this type of vehicle human interaction.

In term of infrastructure and devices there are certain limitations which will not be very ideal for pedestrian safety in this scenario. We can imagine a situation in which a pedestrian who does not have the smart device on it? Will that pedestrian be considered as a pedestrian by the CAVs or not and in this case, there is a risk of conflicts and confusion for the system? These two interventions are at the receiving end of the service and hence will be part of local government’s responsibility to install it and can be met with bureaucratic hurdles.

In the first phase of implementation of CAVs when the transition period is going to arrive it is important that the CAVs are self-sufficient in order to take all the decisions and rely less on the infrastructure that needs to be installed in the local context. Eventually when the CAVs become a common domain such expensive and time-consuming infrastructure can be installed. There are many school of thoughts which includes Tesla CEO Elon Musk that believe that the CAVs should be able to take decisions and drive itself in the urban environments without the need of an extra infrastructure on the roads as of today. So CAVs need to be self-sufficient in the first phase of its implementation.

There are two major line of thinking on how pedestrian interaction in an AV environment will work out to be. One being that AVs will be very sensitive to pedestrian and cyclist activity and will allow the users to seamlessly cross the roads without any issues. There are different ways in which an Av will be able to communicate the intent. Second being that people will become more careless in their behaviour as a pedestrian since CAVs are considered safer than normal drivers and hence it will lead to bad pedestrian behaviour and hence cause more accidents that are result of bad behaviour.

In low-volume settings such as suburban residential neighbourhoods, autonomous vehicles may well reinforce and facilitate these trends – and provide planners with the opportunity to promote shared use designs. It becomes more appealing to stroll along a shared street, or allow one’s children to play outside, safe in the knowledge that autonomous vehicles will obey traffic regulations, and slow down and yield if necessary for safety.

In denser, urban settings, in contrast, where competition for street space is intense, the strategic interactions between road users may limit the benefits of autonomous vehicles. Their greater propensity to follow traffic regulations may put autonomous vehicles at a disadvantage, even before pedestrians respond strategically to their law-abiding behaviour. And once pedestrians, cyclists and human drivers learn to exploit the caution of autonomous vehicles, the speed of driving through a city will be further hampered.

The ultimate impacts of autonomous vehicles on travel and the built environment are still speculative.

(Millard-Ball, 2017) There is currently little research focusing specifically on this problem for pedestrians and bicyclists.

Research is needed to evaluate the types of crashes that AVs are more or less likely to encounter with pedestrians relative to existing driver-controlled vehicles, and ways in which speed modification in these circumstances may improve non-motorised road user safety. For example, it may be advantageous to dynamically moderate speed in areas where pedestrians or bicyclists are more likely to appear; this is currently done in school zones, but a more discrete and dynamic approach could be taken using advanced machine sensors and communication. (Sandt & Owens, 2017) 75

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Ethics safety and insurance policies for driverless cars

Impact on the insurance and regulations The discussion that has been done in previous sections falls in line with what will be the situation of insurance providers and hence the impact on the overall coverage and how these agencies still can stay relevant. Currently, all personal auto insurance products are purchased by vehicle owners, since drivers are held accountable for accidents. With the advent of driverless cars, accountability will shift from the driver to the vehicle manufacturer and the network provider. The vehicle manufacturer will be liable when the accident is due to an issue with the hardware or software of the car; the network provider will be liable when the accident is due to a network fault such as failure to provide the correct direction coordinates.

Driverless cars will be dependent on the network to determine driving aspects such as the location of the car and traffic. The vehicle manufacturer and network provider are expected to buy hybrid auto insurance products that will augment the personal auto insurance that exists today.

One of question has been discussed in this sphere is how far the responsibility goes to the manufacturer and how far will the person inside the car will be responsible for some actions that are to be taken. The situation becomes more complex when it comes to partial automation and how to define the boundaries in terms of responsibility for the safety of people on the roads.

In a survey conducted by KPMG they have found that there is a certain level of conservatism in terms of people’s belief in changing the business models in terms of insurance.

Currently, all personal auto insurance products are purchased by vehicle owners, since drivers are held accountable for accidents. With the advent of driverless cars, accountability will shift from the driver to the vehicle manufacturer and the network provider. The vehicle manufacturer will be liable when the accident is due to an issue with the hardware or software of the car; the network provider will be liable when the accident is due to a network fault such as failure to provide the correct direction coordinates. Driverless cars will be dependent on the network to determine driving aspects such as the location of the car and traffic. The vehicle manufacturer and network provider are expected to buy hybrid auto insurance products that will augment the personal auto insurance that exists today. (Venkatesan, et al., 2016) Different phase in terms of timeline will attract different kinds of customers and service providers. In the Scenario 2 when CAVs become a mode of personal transport and since we have assumed that this model will be led by traditional OEM manufacturers. In case of insurance in the initial trail phase with limited automation like level 2 all the models for the insurance company will remain business as usual. But once limited automation features are kicked in like highway mode etc. than the liability will be on the driver in the manual mode and on the company manufacturers once it is in autonomous mode. The drivers and the manufacturers will take a share responsibility. There are many issues that arises from this kind of liability sharing and will be very difficult and dodgy to determine when and how the responsibilities will be transferred from one party to the party.

But once we are reaching an age of complete automation than the models for insurances will be completely transformed and it will shift gears to the manufacturers and will be a very difficult realm to deal with. There is an array of different agencies that produces the hardware components and the software that deals with the complex situation. One other stakeholder in this equation is network provider and hence these companies will have to come up with a model that is agreed upon and risk factors will also change.

Moreover, in a complete autonomous era like Scenario 2 or scenario 3 in the later stages the human driver insurance will sky rocket as the risk increases with human driver being on the wheel. So, this will be another push towards getting rid of human drivers when technology has proven its worth and it’s safer than human drivers. There will be frictions from different lobbies in favour of not letting go of driving as a hobby and that will be one other major area of conflict between different parties.

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Impact on Urban sprawl and densities

10 Impact on Urban sprawl and densities Driverless cars have a multifaceted impact on our urban environments. Although it has been discussed before that driverless cars combined with electric cars will bring a mass revolution and save lot of money on fuel prices and cleaner air and low emissions. Density matters, even in a world of zero emissions and zero productive time lost to driving, because it is the means by which we control the human footprint on the larger ecosystem and it becomes important to critically understand the urban impact of increasing land usage and low-density ecosystems. (Fox, 2016) Conventional wisdom suggests that high density cities are less impacting in terms of environment factors and it is advisable to be in a walkable high dense neighbourhood rather than suburban areas with sprawl.

Even in terms of per capita energy demand it is advisable to live in a certain threshold of densities which will help to make and efficient use of resources and services that includes public transportation. (Baojun, 2012) (Cheng, s.d.) (Newman, 2014) One of the most important topics to discuss is the Urban sprawl. Sprawl has been part and parcel of modern living, this all began with the era of automobile and over a span of very few years the cities and the urban lives were changed because of automobile. In 1900s New York had around 8000 cars and was reserved for only rich and wealthy. But in 1915 there were 2.3 million registered automobiles in the New York City. Horse carts were made redundant overnight and were taken over by the automobile. And hence began the era of urban sprawl and suburbanization especially in America which was then transferred to many cities in the world in one way or the other.

There are majorly two very distinguished urban forms that will emerge as an outcome of driverless cars.

One is the scenario that says that Urban sprawl will increase and the cities will go far beyond what they are in the current situation. Second scenario is the one in which the cities with effective planning and policy framework will establish a denser urban form and more walkable cities. Both of these scenarios coincide with scenario 2 & 3 of this research respectively. There will be obviously some greys in some sectors and these in-between scenarios from hyper dense to suburban sprawl is something difficult to predict.

Figure 48: Concept structure of a city in the era of driverless vehicles

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In the research developed by (Alessandrini, et al., 2015) the authors are trying to explore different urban forms as a consequence of Autonomous car revolution. This model of city is divided into city centre, inner sub urbs and outer sub urb. This model of the city describes scenario 3 of the research in which the Public transport is still useful and host a lot of trips especially in the city centre. The car ownership is lowest in the city centre and over 90% of trips in the centre that are made on a private vehicle are shared. This shared percentage decreases as we move away from the city centre. The concentration of regional and city level services is the highest in the city centre. In the neighbourhood of the suburban areas local transit or the feeder system connect it to the main trunk network. This makes a connected and integrated network and mobility access across the board.

Rural areas will be decreasing in the future since most of the people will move towards urban areas. But it becomes important to keep the rural areas connected with the upcoming technologies in order to maintain social equity. As mentioned before the differences between the rural and urban areas will reduce and this gives the opportunity to extend the services that are present in urban areas to rural areas and make them more connected to the modern living in the cities. There should be a difference in connecting the rural areas and making them urban and hence a careful policy consideration needs to be made on how these policies will affect the lives of ‘people in rural areas. A hostile takeover will result in loss of rural areas.

A recent study by EPA observed a levelling off of the increase in vehicle miles travelled (VMT) per year in the United States. The report theorized that “travel demand might have reached a saturation point as drivers are unwilling to devote more time to travel, infrastructure improvements no longer allow substantial speed increases, and the marginal benefits of additional trips or travel to additional destinations are not worth the marginal cost. “Economists too have proposed a “reduced tolerance for commuting” as part of the reason for the increase in urban home values. Under this theory, the limitations of current transportation technology have brought us to a point of VMT saturation that has contributed to the renewed urban growth of the past several decades. That growth, changing demographics in the United States, and other factors have led to a decline in traditional suburban development’s popularity. (Kramer, 2013) Historically, new transportation technologies lead to larger metropolitan areas and “time saved from mobility gains is used mostly in additional distance between home and workplace. With the increasing comfort for the driverless cars in this line of thinking the “time cost of driving “tends to reach zero and hence the assumption is that the suburban sprawl is going to increase. The people density will decrease and the road density will increase. This is a way will replicate the advent of suburbanisation in the 20th century and hence it will be making the same mistake twice. (Fox, 2016) Increasing suburbanisation puts a lot of pressure on the infrastructure requirements that include the water supply and also uses a lot of land and it is not sustainable. In the recent times through many means and ways popular urban design concept revolves around the fact that the denser the cities are the better it is in order to meet the infrastructure needs of transport as well as walkability.

This suburban sprawl is likely to take place in scenario 2 where the ownership of the car is still private but the use of the cars has increased to a large extended. But there is one argument that goes against this line of thinking which that even though more families are likely to own a car but the necessity of having a second car is something that is going to decline. There is no surety on how the combined effect of this factors will turn out to be but it can be said for sure that the any extreme assumption might not be as true as many researches are suggesting.

Given the potential for driverless cars to undo progress toward density made in the past several decades, planning for this new technology is critical in non-urban areas. The main factor that will be considered is how we manage our outskirts, suburban areas as well as rural areas in the light of density and land use planning.

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It may be tempting at times to consider simple solutions that address only the driverless car, without actually planning for density. For instance, some may propose to simply restrict driverless cars to urban areas. Such an option, even if legally viable, it is unwise. First, artificially restricting the marketplace for driverless cars to urban areas (however those areas are defined) would deny the benefits of self-driving cars to those in suburban areas. As noted above, those benefits would be considerable.

In a report published by (Mearu, 2017) there is an illustration which says what will happen to the cities in different scenarios? The research also looks into sharing and electrification component. In this illustration it can be clearly seen that when driverless cars are put in the market without careful consideration and policy that support a certain kind of build form over other; the effects are no very healthy. In the First scenario which resembles very close to scenario 1& 2 of this research, driverless cars causes a lot of negative externalities. These includes reduced revenues for government from parking, 0 occupancy dead kilometres, inefficient transit use, congestion on the motorways entering and exiting the cities and sub urban sprawl.

This illustration demonstrates that if driverless cars are owned by people and things remain business as usual we are likely to see an increase in sprawl and increased cost of housing farther away from city centre.

While in the other illustration when a careful consideration is given to these effects parking spaces in the cities are turned into community spaces. On demand CAVs and shared CAVs take up a lot of internal trips and in the close vicinity there will be dense communities that will be developed. This description resembles the scenario 3 where integration of mobility choices makes our cities dense, walkable and affordable.

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11 Environment impact of AVs Although cars are becoming more fuel efficient, 2.9 billion gallons of fuel are still being expended in the USA alone due to congestion delays. While road pricing is an option regarding internalizing this externality, it is widely accepted that a more cost effective and efficient method is to increase fuel taxes.

(Wadud, et al., 2016) Have summarized existing research studies on efficient driving and fuel consumption. One study concluded that if a gasoline-powered engine was operating at its most efficient point, helped by eco-driving programming that minimizes braking, an AV could reduce fuel consumption 35 percent to 50 percent in heavily congested driving conditions, although such congestion is only occasionally found in the real world.

In contrast, the adoption of an AV smart infrastructure would see far superior fuel efficiency than even the most fuel conscious driver could achieve today. Platooning would decrease the effective drag coefficient of following vehicles, reducing fuel use by up to 20 percent. Furthermore, in a virtually crash-free environment, the need for safety features such as reinforced steel bodies, crumple zones, and airbags means that vehicles can become much lighter. A 20 per cent reduction in weight corresponds to a 20 per cent rise in efficiency. These lighter vehicles also correspond to less road damage costs, as trucks, which cause nearly all road damage can become significantly less heavy (RUSSELL-CARROLL, n.d.)

Wadud, MacKenzie, and Leiby also looked at studies on highway platooning, in which vehicles follow each other closely on the highway, thereby decreasing the air resistance for cars following the leader. They estimated that universal adoption of platooning for light-duty vehicles—an ambitious assumption—could reduce the energy intensity of vehicles 3 percent to 25 percent. If all heavy-duty trucks platooned, a feature that automated technology could facilitate, their energy intensity would drop 10 percent to 25 percent.73 it is unclear whether universal adoption of platooning is practical in real-world conditions, and these predictions are based on extrapolation of existing observations. Factors such as the saturation of AVs on the road and the ratio of heavy-duty trucks to light-duty vehicles could influence the impacts on fuel economy. (Alexander-Kearns, et al., 2016) The benefits of electrification and sharing are evident from the fact that with electric cars the emissions in terms of CO2 will reduce drastically. Researchers estimate (Greenblatt & Shaheen, 2015) that AVs could reduce energy use up to ~80 % from platooning, efficient traffic flow and parking, safety-induced lightweighting, and automated ridesharing. In addition, (Greenblatt & Saxena, 2015) have found that small, shared electric AVs in combination with a future low-carbon electricity grid could reduce per-mile (km) GHG emissions by ~90 % compared with today’s vehicles.

In terms of vehicle miles travelled it has been stated that VMT is likely to increase because of the induced demand which according to many researches is somewhere around 15%. Also, there are some trips that will be termed a dead trip with 0 occupancy so, even when total trips by humans increase 15% the dead kilometre trips can increase these trips. The extent to which this will come into play is something that is not certain. But in Scenario 2 of this thesis the VMT and dead kilometres are going to be more than scenario 3 because of efficient planning and distribution of fleet along the network. But it highly unlikely that increase in the se VMT will outrun the benefits that will come from efficient traffic, regulated flow and platooning etc.

Whether AVs mitigate or worsen carbon pollution from light-duty vehicles in the transportation sector will depend on three key factors: their effect on the total vehicle-miles travelled in the United States; their impacts on congestion; and their fuel efficiency and fossil fuel consumption.

The potential energy savings estimated from AVs are much larger than the estimated worst-case growth in energy use and other factors—such as increasing fuel prices and road congestion, and indications that younger people are driving less would also tend to lower energy use. Moreover, the increased use of 80

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electric vehicles could significantly lower energy use and GHG emissions relative to gasoline. However, if the overall change does increase energy consumption, the increase would likely be modest due to these ameliorating effects.

On the contrary there are other line of thinking that suggests that the electric grids in the cities will need a considerable upgrade. If the grids are not decarbonised than in some case the fossil fuel saved in cars will fuel the power plants for electricity. In the growing economies this concern is bigger than that of developed world. Growing economy because of the sheer population numbers and energy deficient nature can give rise to increased fossil fuel consumption than in the current scenario. The battery that is used currently is made from lithium and cobalt as one of the important raw materials. Time and again a concern has revolved around the idea of polluting and energy intensiveness in order to extract these substances. So, there is a need to regulate this process in order to make it more environment friendly. There is no sheer of doubt that the air quality is likely to improve but there are many ways in which that pollution can be extended to other areas and create inefficiencies.

One other concern that arises from the fact that today’s cars travel on an average less than 7000 miles/year and have a certain life span but the shared vehicles of future will certainly have a shorter life span and more turnover. People will need less cars but they will need those cars to manufacture sooner than they do it now. There are benefits for this phenomenon to happen since the technology can be updated in a shorter amount of time but when it comes to evaluating environment impacts manufacturing of these vehicles is a very energy intensive activity. All in all, the real impact in terms of environment and energy consumption is difficult to assess but there are certainly areas that can be improved to make it better for the environment.

As discussed in the previous sections AVs will impact the space utilisation in the cities by promoting a dense development and the requirement of parking is likely to decrease. But in this research, we have established that this phenomenon only happens in Scenario 3 when MaaS takes over as a new transport policy of future. Furthermore, it is established in the research that scenario 2 and scenario 3 are both going to converge towards a similar future it can be said that space utilisation in our cities will be better. There is an opportunity to create more green and public spaces instead of big parking lots in our cities.

In the next page are some illustration of city forms of future given by different authors.

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Figure 50:Vision for future of Singapore (Ministry of transport)

Figure 49:Vision of future cities as given by Mckinsky & Co.

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Figure 52:WSP Parkinson Brinckerwells Farrels

Figure 51: Vision of future of cities by Mathew Spremuli (https://www.treehugger.com/cars/our-streets-maybe-clogged-self-driving-cars.html))

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Conclusion, recommendations and way forward

12 Conclusion, recommendations and way forward From the various topics discussed in the sections above it can be said that driverless cars are a reality in the near future and it will in some way or the other impact the way in which we organise our cities. The challenges in the cities all around the world are getting more and more complex. In a system it is said that there is only a certain point to which by amending the rules the system will function, after that point the whole system needs to be reshuffled in order to induce efficiency. Driverless technology is going to change the way in which we organise mobility systems in our cities. In theory the mobility function has remain more or less constant for past 100 years. Even though we have amended the technologies that are used but in principle we are still focussed around private mode i.e. human driven and collective mode i.e. public transport. Because of its inherent nature these systems have reached its peak point and now there a need to change the status quo that will make our cities more efficient. Automation coupled with electrification and sharing will transform the face of transport in our cities.

It is not to say that driverless technology is the only one that is going to develop in future. There are many background technologies and innovations that are going to happen in the due course of time, it might be outside the realm of this research. But a due consideration should be given to these developments and how these innovations will help city governments to make life better for its citizens. The important thing is that the policies should not lose the people centric approach which puts people and the habitats before anything else. Because there have been many instances where because of the hue and cry about the technology the local governments and stakeholders tend to lose the focus in favour of the comfort of an individual. People centric approach deals with a communitarian approach towards policy making.

Technology is agnostic and can be used in multiple ways in order to profit different kinds of people. But when the technology and its implementation is market driven then the common people are not ensured with its full benefits. That’s when the local governments need to take charge of the situation and allow for a participatory and transparent approach.

There are many barriers for the implementation of driverless technology. It is important that in the policy making and decision process governments at different hierarchies have different roles to play in order to implement autonomous technology.

Driverless vehicles have the potential to impact states and municipalities in a number of ways: traffic congestion and tax revenues may increase or decrease, current public transit options may need to become more competitive, parking needs may decrease, and roadway infrastructure may need to be adapted (to name a few). Local governments will need to plan for these many changes.

Driverless vehicles will have significant impacts on many aspects of society, and, as such, local, regional, and state governments need to start planning for these now. In fact, local governments need to consider the following planning and policy actions now and in the next decade, especially since the AV roll-out is well within transportation planning time horizons.

From the all the researches it indicates that the local government is one of the important stakeholders in implementation of driverless technology in the urban environments. But there are some responsibilities that should be dealt at national or world level forums. The issues that relates to privacy, data sharing, safety and liability is something that is beyond the local governments to take the decisions. Local governments are stakeholders in the discussion around it but the final decisions rests on a larger set of officials which is beyond the scope of this study.

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The following are the range of issues that can be dealt at local level •

•

•

•

Mobility: There are many factors that will come in play in order to influence the mobility of the cities. The ridership of public transportation vs. the competitiveness of ride hailing and on demand services; resulting in changing modal share along with demand of trips. Another important addition is induced demand which ranges around 15% according to most estimates which will allow for the mobility freedom of handicapped, children and old people. The mobility options the city has to offer needs to have a careful consideration in order to maintain a certain kind of urban lifestyle.

Driverless technology will also have a very serious impact on the road infrastructure that includes reducing capacities increasing travelling speeds in the urban areas. Furthermore, all the signals and signage need to be updated in one way or the other and changes have to be communicated with the people in an efficient way in order to avoid chaos. Parking demand and road space dedicated for parking needs to be reconfigured.

As driverless vehicles become more popular, everything from service coverage to vehicle types to labour requirements stands to change. Transit agencies will need to completely re-think their services, labour needs, and fee structure in order to stay competitive in the new transportation environment.

A widespread use of driverless cars will have a considerable impact on the revenues of city governments that will be lost in the light of low parking, management of manpower in the transit system (governments might have to completely change the kind of people that will be needed to work). Taxes, parking fees, speeding tickets, parking real estate, and incident management costs are just a few of the government revenues and costs likely to be impacted. Governments need to find different revenue streams in order to keep paying for the kind of infrastructure

In the light of these above mentioned issues and challenges there are some actions and plans that the governments can do now and in long term in order to be prepared for the technology to unfold the way which is not very certain at the given moment. There are some short term recommendations and some long term actions that the local governments can take in order to best handle the transition of driverless technology in their cities.

12.1 Short term recommendations These are the actions that the local governments can take regardless of how the technology is going to develop and evolve, these are the preventive measures that will allow the policy framework for city governments.

12.1.1 Monitor the progress of technology Local government needs to be proactive at the progress that is being made in the vehicle technology along with different players in the local market. A special committee can be formed within the city planning departments that can oversee the constant update in technology and its possible implications on the local plans.

12.1.2 Integrate AVs in local plans & goals There are many goals that the governments all across the world are trying to achieve and in many instances AVs will provide the necessary push for it. Some of the examples are Vision Zero (no accidents/year; AVs are believed to reduce the cost and the fatality of accidents by a huge margin so propagating AVs in the cities will ensure less accidents). AVs will help with pollution and GHG reductions(AVs coupled with electrification will ensure that the air quality improves drastically, furthermore better flowing traffic also has a huge gain in terms of GHG reduction). Furthermore by introduction of MaaS the city governments can regulate the transit cost effectiveness and competitiveness; in which AVs will have a crucial part to play (on demand AV, shared AV, shuttle AV).

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12.1.3 Establish relationship with local stakeholders and service providers It is a well-known fact that in the sphere of automation and shared mobility there are many new players in the market. Some of them are start-ups and other are giants like Apple, Google GM etc. It will be imperative that a good working relationship is established with these local service providers in order to make the best from it. It will be a win –win situation since local governments will have a say in how the technology is going to be implemented along with some revenue stream which might open up from the partnerships. The service providers get the market in which the product can be launched and used taking on board the people and the local government.

12.1.4 Revaluate the future local plans and infrastructure needs It is important the governments are able to take some informed decisions based on the researches done.

The infrastructure needs in terms of let’s say road capacity, transit capacity etc. will likely to reduce and huge investment on some ambitious projects forecasted from Business as usual scenario in terms of technology will result in wastage of resources for the local government. Hence, an informed decision should be taken before addressing some of the infrastructure needs of future in the Local plans.

12.2 Long term recommendations and activities In long and medium terms that is the timeline in next 10-15 years many of these changes need to be done in the local government policy planning regime in order to ensure resiliency in our cities. Some of these recommendations can come early in the timeline depending on the local context.

12.2.1 Update travel demand model & road capacity As more information will be available on driverless cars based on various researches and pilot project simulations, there will be a need to update the demand models. The travel demand models should ideally reflect updated information regarding who is traveling (e.g., elderly and disabled may travel more due to AVs), where people are living and working, how many trips they are taking, people’s value of time while traveling, what level of shared rides are occurring, and the vehicle ownership model. It should also capture any changes associated with freight delivery. All of these factors are likely to impact travel behaviour. AVs as mentioned in many researches increases the carrying capacity of the same road by making traffic more efficient, adhering to the efficient travel speeds and platooning of vehicles, hence it has be sited many times that the lane width and overall road capacity is likely to change and with increases AV penetration it will become imperative to model these changes. Modelling these impacts will likely be refined as the technology is developed further so that a more accurate assumption is made on the capacity and behaviour in the cities.

12.2.2 Assess transit services and fleet requirement Transit agencies should seek to leverage driverless technology to maximize the cost-effectiveness of their service while ensuring equitable, fairly-priced mobility options for everyone. As such, they will need to evaluate the full mobility eco-system. There are many private players in the market that affect mobility choices of people, the local transit agency along with the city government need to make a fair assessment in order to determine the kind of service that will be appropriate in the era of driverless cars. A careful consideration should be made to understand and analyse the fleet requirements of bus, trams and metro services in order to provide citizens with best service at a competitive cost. Transit agencies can leverage private mobility companies to provide first/last mile solutions to longer-distance transit services .This framework can work under the umbrella of MaaS where all stakeholders agree to a certain market share.

City governments should understand that providing appropriate services to the citizens at the lowest possible cost is their priority. In order to prioritise one mode over the other some initiatives needs to be agreed with the stakeholders like dynamic pricing in peak hours and encouraging higher occupancy vehicles over private individual by regulating the cost. E.g. a private taxi in peak hour can be 5 times more expensive and transit can be made virtually free and some incentives in costs to be given to high occupancy shared 86

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vehicles. This might be an extreme example but such initiatives needs to be put in place in order to regulate the market.

12.2.3 Forecast financial implications on revenues It is inevitable that the local governments will be able to keep their revenues stream based on today’s models. Because in the future with driverless cars a lot of city revenues that are based on parking, transit and land use are going to be altered. Utilizing a cross-functional group of stakeholders, government officials should examine every line item of the budget to evaluate the potential financial implications of driverless vehicles. Here are the list of items that needs to be revaluated and alternative revenues models should e generated.

• Parking revenues (or alternate revenues associated with land previously used for parking) • Speed ticket violation fees • Tax revenues related to vehicle purchases, registration fees, and VMT (on fuel) • Health and life insurance costs • Transit agency costs and revenues • Incident management costs • Insurance costs Apart from cutting down on existing revenues city government will likely have to invest in the necessary infrastructure in order to make the technology available to the people. In this context developing meaningful partnership with the service providers can be a good way to generate revenue streams as well as regulate the city planning and policies. City governments have to move away from providing infrastructure to managing the stakeholders that provide the necessary infrastructure. The local governments have the necessary power and jurisdiction to form these partnerships.

12.2.4 Parking management plan Many parking spots (both on and off-street) may be unnecessary due to the potential for lower private vehicle ownership and the ability of driverless vehicles to park themselves in remote locations. On the other hand, they may still be necessary, but they could be re-located (potentially outside of city centres).

For these reasons, more infrastructure for passenger pick-up and drop-off locations may be required.

Additionally, parking space sizes may be reduced. Parking at all levels will change and city governments need to facilitate this change for the developers in order to have an efficient parking plan for the city.

Some of the policies can be as follows: • • • • •

Eliminate minimum parking requirements in zoning laws and encourage more pick-up/drop-off Establish a city-wide parking space cap Dedicate parking spaces for shared vehicles.

Institute variable priced parking to proactively manage how parking spaces are used.

Establish policies requiring all new parking facilities to be designed and built to be adaptable (since it may not be needed for parking in the future) Furthermore, city government needs to make changes in the building code to facilitate new parking structures to be retrofitted to other uses near city centres which includes guidelines e.g. higher floor height, ramps to be external so that it can be removed in future.

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12.2.5 Update urban design guidelines for streets and junctions Driverless cars are likely to change the way in which we organise our streets and junctions. Streets will need dedicated space such as shared ways in order to host a family of vehicles that are compatible with pedestrians and cyclists. Streets will have to be more integrated with multiple functions. Another reason is that these streets will host different arrays of drop offs and pick up spots. There will also be a need to have EV charging stations and potentially some of the on street parking can be converted into it. The junctions are likely to change as well, junctions will host different kinds of single systems and hence the design should respond to it giving away the priority to soft mobility users more than before. There can be a situation in which some of the vehicular traffic will be completely be segregated from soft mobility hence necessary guidelines should allow for it to happen. Besides these changes other important changes are the way transit stops and pickups are organised (dynamic routing, shuttle services etc.), signal systems at junctions, speed limits for the roads and hierarchies of roads; moving towards an isotropic grid that is resilient and can adapt to future changes in mobility needs.

12.2.6 Managing road pricing and congestion charges City governments need to make sure that ride sharing and ride hailing services are prevalent in order to take the full benefit of AVs. It is known that driverless cars without sharing and electrification will not be able to reduce congestion and GHG emissions hence it is important to make necessary policy that supports shared mobility behaviour rather than individual mobility. The policies should encourage multi-modality behaviour for its citizens. Here are some of the recommendations for the same: • • •

•

Discourage single occupancy and 0 occupancy trips, it should be taxed with more roadway tolls.

Add or designate more high occupancy vehicle (“HOV”), high occupancy toll (“HOT”), and express lanes Add congestion charges for the downtown areas (which is already present but a system should be put in place that taxes single occupancy vehicles and eventually petrol driven vehicles should be phased out) High occupancy vehicles will need more curb space and priority access to be given in order to encourage shared mobility behaviour.

12.2.7 Update land use policies and zoning in order to discourage sprawl.

In the scenario 2 of this thesis it is been shown that sprawl is likely to increase in the event that driverless cars are not shared and are privately owned. The reason being the values of time travelled will reach close to 0 if people can travel in a comfortable environment hence , they are more likely to choose to live further that will impact the cost of real estate and also is an environmental concern that encourages sub urban sprawl. It will be important for local governments to establish policies that encourage high density, walkable communities in order to minimize urban sprawl. Some of the policy initiatives include: • • • •

Encourage transit oriented development not just along the corridor but near the high density interchange hubs which will be more common in future than they are now.

Create policies and processes that encourage developers to build walkable communities Develop policies that make Greenfield development and septic-based development very expensive and onerous while, similarly, support infill development Improving the services that includes schools and neighbourhood very attractive walkable and safe in order to encourage density development.

It is inevitable to stop driverless cars from coming to the market. They will hit the roads with or without the support of the governments and the policy makers. How the future will shape is something that is beyond comprehension but there are many steps that can be taken in order to take the benefit from the innovations in the field or mobility and transportation. From scenario 1, 2 and 3 we know that there are many ways in which mobility behaviour of people can change either people will choose to live farther to 88

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work , increase the congestion or there is an opportunity to make our cities liveable , walkable and equitable for various communities.

The lines between public and private is going to fade away. This revolution happened in terms of private and public spaces to form collective spaces but now we are going towards an era in which same phenomena is happening to the transport and mobility of an individual. Governments all across the globe are faced with the challenge of providing sustainable mobility solutions to the ever increasing number of people in the cities. This revolution is a great opportunity for solving some of very complicated problems in urban and transport planning (e.g. last mile connectivity, safety for road users, reducing GHGs, congestion etc.).

There is an array of new stakeholders that are entering the market of providing mobility solutions to the people in the cities, this has created a competition for the local transit authority in terms of cost and convenience. Currently, things are not in favour of public transportation but these equations can be changed with careful consideration on shared mobility and MaaS with AVs paving the way for this to happen effectively. With the coming of driverless vehicles, the governments (at all levels) have the opportunity to proactively establish goals and policies that can continue to support the driverless vehicle revolution while keeping the traveling public safe and mobile This research touches upon building the base and establishing the fact that driverless cars are going to make impact in our future mobility choices. In order to further this work each and individual topics need to study in a specific context in order to determine the real impacts and policy implications.

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14 Appendix Master Plan study done at Systematica

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