The spatial impact of Smart Mobility solutions Exploring the spatial impact of Smart Mobility solutions on the existing urban structure in cities The Smart City movement has gained popularity powered by technological innovations. Smart Mobility solutions are one of the main facets of a Smart City, and many new technologies are being developed and applied . This research provides an overview of various Smart Mobility solutions and the spatial consequences of these solutions on the existing urban structure in cities using ďŹ ve research methods : literature review, trend analysis, interviews, policy analysis and data analysis . Six Smart Mobility solutions have been analysed which include Bike!Sharing, Car!Sharing, Autonomous Vehicle, Public Transport, Electric Vehicle and Multi!Modal Mobility. The short term spatial impacts of these solutions are li&le as they only slightly reduce the current parking pressure, and the infrastructure for Electric Vehicles is not aesthetically pleasing. However, the long term spatial impacts are positive as they will reclaim space used for street!parking and perhaps even parking garages . For older cities these innovations provide an opportunity to, at least partially, return the city to its original structure . The innovations will free up space mostly in the city centre which can be used to accommodate pedestrians and contribute to a higher quality of life .
Wri&en by: Evelien Florijn
Keywords: Smart Mobility; Car!Sharing; Bike!Sharing; Autonomous Vehicle; Multi!Modal Mobility; Electric Vehicle; Public Transport; Spatial impact
1. Introduction The first ideas on smart cities came about in the 1850’s and started growing in popularity soon after. From the very beginning both sustainability and the use of new technologies were key to develop smart cities. One of the most wellMknown concepts is that of the garden city by Sir Ebenezer Howard dating from 1898, which was a vision on a healthy and functional city opposing the cities of the early industrial revolution (Angelidou, 2015) . New transport solutions such as the railway and the automobile made it possible to live away from the city in a more sustainable seYing . Table 1 shows how the Smart City concept has developed over time . Table 1.Historical over view of the Smart Ci ty con cep tbased on (Angelidou, 2015) 1850’s First ideas on Smart Cities 1898 Garden City concept by E . Howard, focus on healthy versus industrial 1904 T . Garrier: ‘Une cite industrielle’, includes newest technology advances such as hydropower 1909M1916 Futurist movement, focus on machines and industry 1919M1932 Bauhaus, focus on mass industrial production 1922 Le Corbusier’s ‘Ville Contemporaine’, using new construction materials 1939 Bletchley Park, United Kingdom, first intelligent city using knowledge and information in spatial proximity (Komninos, 2011)
Post WWII New Towns movement, planned cities using new materials and construction methods 1960’s New technologies inspired thinkers and designers:‘PlugMin City’ (P. Cook), ‘Walking City’ (R . Herron), ‘Electronic Urbanism’ including teleMworking, Mcommunication and Mspaces (T . Zenetos) 1980’s Use of terms such as ‘Networked City’, ‘Cyber City’, ‘Intelligent City’, ‘Digital City’ etc . 1990’s Internet causes globalization, which will result in the disappearance of physical cities. Birth of Smart City term 1991 First mentioning of the Internet of Things Mid 1990 The future city will have ICT as city management 2009 Smart City becomes central theme in the development agendas of large cities Global hubs of innovation, knowledge Now management is everywhere . Small scale Smart City pilots, driven by a technology push and a demand pull Many researchers state that there is no agreed definition of the term Smart City in literature (Angelidou, 20 15; Cuddy, et al ., 2014; Grip, 2015; Hollands, 2008; Knowles & Rozenblat, 20 16; Lee, Hancock, & Hu, 2014; Letaifa, 2015; NeiroYi, et al ., 2014), which hinders literary research as it is difficult to compare different research
results. A distinction can be made between two main approaches. The first approach, most prevalent in literature, focusses on the implementation of information and communication technologies (ICT) at different scale levels .Peter Hall, a well¿known town planner and urban geographer, described the ‘Smart City’ as the result of an increasing complexity due to material wealth and communication technology and characterized by a shift from an economy based on production to one based on consumption (Knowles & Rozenblat, 2016) . Whereas Grip (2015) sees the Smart City as the collision between two trends : digitalization and urbanization, resulting in an extra digital layer onto the existing infrastructure. Hollands (2008) has a similar view as according to him ‘smart’ implies a positive urban¿based technological innovation . NeiroÔi, et al . (20 14) state that the Smart City uses ICT based solutions to improve sustainability, but also invest in human capital . Letaifa (20 15) takes a more neutral stance, describing the Smart City as using technology, or at least moving in a forward way . Lee, et al . (2014) oppose this as they state that ICT is key in all concepts and variations of the Smart City . The second approach focusses on the quality of life, working with existing solutions and communities. Marsal¿Llacuna and Segal (in press) argue that a Smart City will not become the best based on technology, but by optimizing resources, efficiency, environmental protection and including social factors. Joris Scheers, a spatial planner and sociologist, claims that the Smart City is about listening and acting in a community¿ sensitive way (Grip, 20 15) . Whereas Cuddy, et al . (2014) recognize the potential of ICT, as it can provide insight into, and control, the various systems, they also stress the importance of focussing on quality of life. Finding a shared definition is difficult, not only because different cities have different contexts (NeiroÔi, et al ., 2014), but also because Smart Cities can be seen as hybrid between different city types that originated in
the 1980’s (see Table 1) (Lee, et al ., 20 14) . Letaifa (2015) suggests that the Smart City is a hybrid between the intelligent city, with a top¿down approach focussing on technology, and the creative city, with a boÔom¿up approach focussing on community based solutions. What all of the definitions have in common is a focus moving forward and using sustainable solutions to generate economic opportunities. The Smart City can be divided into different facets. Giffinger, Haindlmaier & Kramar (20 10) distinguish six different facets : Smart Economy, Smart People, Smart Governance, Smart Mobility, Smart Environment and Smart Living . Literature indicates that Smart Mobility is most prevalent in current cities (Hollands, 2008; Lee, et al ., 2014; NeiroÔi, et al ., 20 14; Letaifa, 20 15) . This is because technology in this field is more mature (NeiroÔi, et al ., 2014) .A variety of applications is already available to indicate vacant parking spots and real time traffic information (Mazhar Rathore, Ahmad, Paul, & Rho, 2016) . Our daily paÔerns of movementsaremuch morecomplex than 60 years ago: people move further away from home and most travel occurs between cities (Nabielek & Hamers, 20 15) . Existing research focusses on various aspects of Smart Mobility such as computer modelling to increase traffic flow, autonomous vehicles (Zhang, Guhathakurta, Fang, & Zhang, 2015), multi¿modal transport solutions, the impact of electric vehicles on the Smart Grid (Calvillo, Sánchez¿Miralles, & Villar, 2016), fleet management (Horn, 2002), demand responsive transport (Horn, 2002), car¿sharing models, the use of renewable energies as fuels, and Smart City policies (Angelidou, 2014) . And though some mention that Smart Mobility innovations have spatial consequences (Hollands, 2008; Angelidou, 20 14; Ricci, 2015), none describe what these spatial consequences entail . Especially in the western world, where there is no need for new cities but where existing cities are regenerated
(Angelidou, 20 14), these innovations can have a great impact on the existing urban structure. Therefore more research is required in this field, so the creation of Smart Cities can happen in a controlled and well designed manner . This has to happen simultaneously with research into Smart City solutions to prevent cities from becoming a hodgepodge of different technologies after their implementation . Smart Mobility solutions can be applied at different scale levels .Giffinger (20 10) describes Smart Mobility as the accessibility on a local and (inter)national level, with the availability of ICT infrastructure and sustainable, innovative and safe transport systems. Table 2 gives an overview of the most commonly described solutions that will be used as a theoretical framework throughout this article, to describe the spatial implications of these solutions. It is important to note that they cannot be viewed separately, as often, multiple solutions are implemented simultaneously. This article aims to give an overview of the (possible) spatial impacts of various Smart Mobility solutions by answering the following research question : How do Smart Mobility solutions affect the urban structure in existing cities? In order to answer this question, five different methods of research were chosen : literature review, trend analysis, interviews, policy analysis and data analysis. In this research, only existing information and data has been used . The results from this research will not only clarify the spatial impacts of the various solutions, but will also serve as a guide for policy makers and urban planners who are developing Smart Cities . 2. Methodology Research approach This research aims to define the spatial consequences of six Smart Mobility solutions. These solutions (see Table
2) came forward from an extensive literature review and will function as a theoretical framework throughout this article. Information derived from the research methods will describe the spatial influence of these solutions. For this paper secondary research has been done, as the information and datasets were already available. This research, however, combines existing research at a higher level of abstraction through a meta=analysis. Methods Five different methods were chosen to determine the spatial consequences of the six chosen Smart Mobility solutions. All methods will be discussed shortly, describing both advantages and disadvantages . As no direct research has been done into this topic, the different methods containing indirect clues on the spatial consequences are combined . Literature review An extensive literature review provides information on current developments in science. Recently, a lot has been wriGen on Smart Cities, how they should be ranked (Giffinger, et al., 2010), and how to create them (Angelidou, 2015) . Within this field of Smart Cities, a lot is being said on Smart Mobility solutions .It is especially interesting to see Smart Mobility solutions that have not been (fully) implemented such as the Electric Vehicle and its charging infrastructure, the Autonomous Vehicle, and new methods of arranging Public Transport . Literature discusses technical aspects such as modelling software and related problems, but also explore how upcoming services such as Car=Sharing and Bike=Sharing can be optimized . The initial literature review resulted in the six Smart Mobility solutions described in table 2 . The more in depth literature study that followed resulted in spatial outcomes of such solutions. For this, journal directories such as ScienceDirect (www .sciencedirect .com) and the
Table 2.Smar tM obility sol uti ons des cribe din li terature
Technology
Availability
Source
Autonomous Vehicles
Technology is available. Implementation in city centre estimated in 2060-2075.
2getthere, n.d.; Alessandrini, Campagna, Delle Site, Filippi, & Persia, 2015; Calvillo, et al., 2016; Cuddy, et al., 2014; Fagnant & Kockelman, 2015; Glotz-Richter, 2012; Hagemeier, 2014; Martens, 2016; Meijl, 2015; Mil, Schelven, & Kuiperi, 2016; Siemens, n.d.; Zhang, et al., 2015
Electric Vehicles/ Bikes
Available. Improvements in charging infrastructure, and batteries will come in future.
Calvillo, et al., 2016; Centraal Bureau voor de Statistiek [CBS], 2015; Cuddy, et al., 2014; Filho & Kotter, 2015; Gemeente Eindhoven, 2013; Lievense, 2013; Loose, 2009; Marsal-Llacuna & Segal, in press; Martens, 2016; Meijl, 2015; Mil, et al., 2016; Ministerie van Economische Zaken, 2015; Nabielek & Hamers, 2015; Pol & Hoen, 2013; Waard & Meijles, 2015
Public Transport
Available. Rapid transport bus lanes, but mostly concerns low to 0-emission goals, and is in this research more a subcategory of Electric Vehicles.
Cuddy, et al., 2014; Debnath, Chin, Haque, & Yuen, 2014; Filho & Kotter, 2015; Gemeente Eindhoven, 2013; Gemeente Utrecht, 2015a; Meijl, 2015; Neirotti, et al., 2014; Pol & Hoen, 2013; Waard & Meijles, 2015; Weijer, 2015
Car-Sharing
Available. Different business models. Rapidly growing. Possible to create a synergy with Electric Vehicles.
Baptista, Melo, & Rolim, 2014; Boerrigter, 2015; CBS, 2015; Cuddy, et al., 2014; Dieten, 2015; Driel & Hafkamp, 2015; Fagnant & Kockelman, 2015; Filho & Kotter, 2015; Gier & Ettema, 2014; Kennisinstituut voor Mobiliteitsbeleid [KiM], 2015a; Kennisinstituut voor Mobiliteitsbeleid [KiM], 2015b; Marsal-Llacuna & Segal, in press; Martens, 2016; Mil, et al., 2016; Nabielek & Hamers, 2015; Waard & Meijles, 2015; Zhang, et al., 2015
Bike-Sharing
Available. Growing rapidly. Also includes E-bikes
Boerrigter, 2015; Bree, Kamminga, & Theunissen, 2010; Corcoran & Li, 2014; Cuddy, et al., 2014; Ricci, 2015; Gemeente Utrecht, 2015b; Kaspi, Raviv, Michal Tzur, & Galili, 2015; KiM, 2015b; Marsal-Llacuna & Segal, in press; Nabielek & Hamers, 2015; NS, 2016; Waard & Meijles, 2015
Multi-Modal Mobility
Available. Future developments will include a more seamless transition and wider acceptance of Smart Cards.
Boerrigter, 2015; Bree, et al., 2010; Debnath, et al., 2014; Gemeente Amsterdam, 2013; Glotz-Richter, 2012; Neirotti, De Marco, Cagliano, Mangano, & Scorrano, 2014; NS, 2016; Schultz van Haegen, 2013
online library of Eindhoven University of Technology have been used to find relevant articles. The initial literature study also provided keywords which were used to find more information on specific topics. The articles scanned for relevant information, and then labelled based on which of the six Smart Mobility solutions it provided information on . Trend analysis The Trend analysis provides information on which solutions and developments within the field of Smart Mobility are seen as interesting and upcoming. The usage of various solutions is interesting as this indicates what is trending for users, but also for businesses . A different perspective is provided by looking at what is trending in the research and development field where scientists and corporations are working on new Smart Mobility solutions. Traditionally, trend analysis looks at data over a period of time to observe trends. However, Smart Mobility is such a new topic, that its spatial consequences are not yet known and neither are historical trends. Articles predicting future developments therefore form the main source of information for this analysis. Interviews Existing interviews have been used to provide various perspectives on Smart Mobility. These interviews have been conducted by the chief editor of Infrasite at e Keet . nl (e Keet .nl, 2016) . The topics of the interviews vary depending on the specializations of the interviewees.All interviews have been obtained via the Infrasite website. Interviews are a qualitative research method, used to obtain descriptive information .One of the disadvantages is that the interviewee is (unconsciously) influenced by the interviewer . It is therefore very important that the interviewer is well trained to minimize this influence. As the interviews were obtained from a blog, the skills
of the interviewer are unknown which can decrease the validity of the outcomes. Another disadvantage is that interviewees may favour a politically correct or socially desirable answer over the truth . However, due to the nature of the questions in the interviews, which are focused on facts and future speculation, and do not include any controversial topics, both disadvantages are expected to have had li£le effect on the results. Policy analysis Policies of three Dutch cities; Amsterdam, Eindhoven and Utrecht, have been analysed to find existing Smart Mobility solutions, current developments and how these are being implemented . Some of the cities also have future goals which include Smart Mobility solutions, which have been included as well . This provides indirect information on spatial implications of these transport solutions. It also shows the perspective of the policy makers and city planners on what their view on the future city is. This source will mainly provide information on current available technologies and their implementation . Data analysis The data analysis will provide information on the usage of Smart Mobility solutions, and indicate the preferences of users. The data can also indicate shifts in usage due to the implementation of a certain solution or innovation . Existing data sheets have been analysed, together with a dataset provided by KiM Netherlands Institute for Transport Policy Analysis (Hoogendoorn Lanser et al ., 2014) . Excel and SPSS have been used to analyse the data . 3. Results The spatial impact of Smart Mobility solutions depends greatly on the existing urban structure of the city. In 1947 barely 1 in 10 families had a car (Hall, 1989) .
In the 1960’s the car became widely available and cities (or neighbourhoods) built from this period on have been designed with a prominent role for the car (Marsal×Llacuna & Segal, in press). Older cities, which have been designed for pedestrians and other forms of non×motorized transport, have been invaded by cars affecting the original design and structure . A general tendency towards more efficient space use can be observed . Grip (20 15) predicts that most spaces will be hybrid and serve multiple functions either simultaneously or at different times during the day . Literature review Bike×Sharing has existed for around 50 years, but has grown exponentially over the last decade (Bree, et al ., 20 10; Corcoran & Li, 20 14; Ricci, 20 15). The system differs from bike rental as it is used for short point to point journeys (Ricci, 20 15), creating a spatially dispersed difference in supply and demand that varies during the day . More bikes and docking stations are needed to cope with this growing demand, and whereas now the docking stations are usually next to public transport terminals, Bree, et al . (20 10) found that there also is a desire for other locations. They also highlight the issue of availability of space, as in some locations, particularly at large public transport terminals, there is no space to expand . In general, the shared bicycle is part of a multi×modal journey that includes other forms of public transport, which in most cases use one integrated Smart Card for all different modes (Ricci, 20 15). Bree, et al . (20 10) do mention that even with a Smart Card, this intermodal transition needs to be smoother . Future technologies will generate ‘Smart Bikes’ with GPS and pollution and noise sensors which can provide real time data and information through an app (Marsal×Llacuna & Segal, in press). Car×Sharing also grew exponentially over the past
two decades. There are three main business models: business to consumer (B2C) with a fixed parking spot, business to consumer with a ‘free floating’ system, and peer to peer (P2P) Car×Sharing using apps and websites where people can sign up their own car for sharing (Dieten, 20 15; Driel & Haÿamp, 20 15; Schmöller & Bogenberger, 20 14). The growing popularity can be partially a ributed to the flexibility of having access to a car without having any fixed costs and burdens (Baptista, et al ., 20 14; Dieten, 20 15; Glo ×Richter, 20 12). Literature appears very positive about the influence of Car×Sharing in reducing parking demand and congestion (Dieten, 20 15; Kaspi, et al ., 20 15), some stating that one shared car replaces anywhere from 3 to 11 privately owned cars (Baptista, et al ., 20 14; Driel & Haÿamp, 20 15; Glo ×Richter, 20 12; Loose, 2009) . It is questionable whether these numbers are correct, as it is difficult to measure . Car×Sharing replaces a privately owned car for 90% of the users (Glo ×Richter, 20 12), but it often replaces a second car or other modes of transport which is more difficult to measure (Baptista, et al ., 20 14; Nijland, Meerkerk, & Hoen, 20 15) . It becomes even more speculative when people who never owned a car but might have bought one if they did not start Car×Sharing are included .Driel & Haÿamp (20 15) claim that including this group of people every shared car will replace 9×22 privately owned vehicles. In theory however, this would create a lot of space, especially in the city centre . In the city of Bremen, 1500 cars have been replaced by Car×Sharing in one year (Glo ×Richter, 20 12). Loose (2009) states that in theory the replacement of 4×8 cars would free up 36×84m2 of space . Combining this information suggests that in one year 13500m2, or roughly two football fields, has been freed due to reduced parking demand . Loose (2009) already mentions that though in theory Car×Sharing should reduce parking pressure, in city centres the demand is so high that other cars take up the ‘free’
spaces . Driel & Ha amp (20 15) confirm this by stating that Car&Sharing does not necessarily lead to fewer cars . One of the most promising Smart Mobility solutions is the Autonomous Vehicle .Though the technology is there in principle, some hurdles, such as safety, still have to be overcome . Siemens (n .d .) describes the implementation of the Autonomous Vehicle as a three step process . First there is ‘connected transport’ in which for instance the users smartphone is connected to the car system . Second is the ‘Co&operative Intelligent Transport System’ where
the vehicle communicates with other vehicles and the infrastructure, in this system the vehicle can brake
Figure 1. Le vel of a utoma ti on an d segrega ti on of a utoma te d vehi cles (Alessan drini, e t al ., 2 015) 1. Limi te d a utoma ti on of s teering .Braking an dlane g ui dan ce Pla tooning + a utoma te d parking (te chn ol ogy is a vailable b ut re quires 2 . hi ve cle to vehi cle comm uni ca ti on) 3 .Res tri cte dselfh dri ving (being tes te d. i .e . Google’s cars) 4 .Selfh dri ving in all con di ti ons
and park itself without human input, up to the point where only limited human input is needed . Third is the Autonomous Vehicle in which no human input is required at all .Alessandrini, et al . (20 15) have visualized a similar process in Figure 1. The increased automation will lead to a less private relation to the car (Glo;&Richter, 20 12) . This provides
great potential for the Autonomous Vehicle to create a synergy with Car&Sharing . Such Shared Autonomous Vehicles are like a taxi on demand (Fagnant & Kockelman, 20 15; Zhang, et al ., 20 15) . These vehicles will have a tremendous spatial impact as they can be parked in depots which takes up only a quarter of the space of a regular garage (see Figure 2) . One Shared Autonomous Vehicle is said to replace 11 privately owned cars (Zhang, et al ., 20 15), but like regular Car& Sharing it is unlikely that it will result in less cars . Fagnant and Kockelman (20 15) argue that there will be a higher demand and wider user base, as the elderly and children are able to use such vehicles as well . Also, Autonomous Vehicles are much more efficient than regular cars as they do not ‘cruise’ looking for free
Figure 2 . Con ven ti onal garage vers us a uton om ous car dep ot (Alessan drini, e t al ., 2 015) Current developments in Public Transport focus on more efficient transportation using special bus lanes (Debnath, et al ., 20 14), and transitioning to the use of electricity as a power source (Calvillo, et al ., 20 16) . Electric Vehicles, though at the moment not widely used, will replace most of the carbon fuelled vehicles in the coming decades (Loose, 2009) . A synergy with B2C Car&Sharing using fixed parking places is easily found as these parking spots can be fiued with the required charging infrastructure . Currently only 9% of the vehicles used in Car&Sharing are electric leaving much room for growth (Baptista, et al ., 20 14) .
parking (Fagnant & Kockelman, 20 15), it is unclear whether this will cancel out the increased demand and
Trend analysis There is a shift of paradigm as car ownership declines and the purchase of mobility services increases . This
eventually lead to a lower number of cars on the street .
can be auributed to an increased demand for individual mobility due to varied lifestyles, increased importance
of leisure activities and liberalization of working hours and conditions (Alessandrini, et al ., 20 15) . Bike Sharing is growing in popularity, and demand increases especially at public transport terminals (Nabielek & Hamers, 20 15; NS, 20 16) . A payment system
A development that is still trending and widely applied is the use of renewable energies for transport fuels (Neiro½i, et al ., 20 14) . Currently the focus is primarily on business related drives who account for 95% of
using an integrated Smart Card to create a seamless
the purchased Electric Vehicles, and the secondary focus group is Car Sharing vehicles (Pol & Hoen,
transition between multiple transport modes is applied more often (NS, 20 16) .
20 13) . In general there is a trend towards E vehicles, including E bikes and E scooters which will likely
Car Sharing has boomed between 2005 and 20 10 (Dieten, 20 15) .It does not only replace a privately owned
become important modes of transport for relatively short distances . A large electrical infrastructure is
car (Pol & Hoen, 20 13), it also replaces other modes of transport such as public transport, biking and walking
needed to support these vehicles with both private and
(Glo¯ Richter, 20 12; Nijland, et al ., 20 15) . Interestingly,
public charging outlets . This infrastructure is currently growing and can cause several problems . Not only are
the same literature also suggests that people who use Car Sharing walk, bike and use public transport more
the charging poles vulnerable to being damaged, they are also not aesthetically pleasing (Pol & Hoen, 20 13) .
than they did before using the service (Baptista, et al ., 20 14; Dieten, 20 15; Glo¯ Richter, 20 12) . This paradox can be caused by using different data sources, or it can be the difference between what motivated people to
Interviews Boerrigter (20 15) states that initially the focus should
start using Car Sharing services and what their current
be on improving the use of existing infrastructure by providing services such as real time parking
travel habits are . What is clear, is that Car Sharing users drive 30% less kilometres than they did in a privately
information, Car Sharing and Bike Sharing, before moving on to future modes of transport . Van de Weijer
owned vehicle (Dieten, 20 15), which can be explained by a difference in pricing . With Car Sharing, a user usually
(20 15) agrees by saying that at the moment “Smart
pays a fixed fee plus additional costs per kilometre
mobility is … Smart vehicles on stupid infrastructure” . Especially parking aids are seen as promising to reduce
owned vehicle a large sum is paid to buy the vehicle and
the parking demand in city centres . The Netherlands is working on a national parking platform to reduce the
then regular costs for fuel, but the actual costs per ride are unclear . The reduction in kilometres driven does
amount of cars cruising for a parking spot, which can account up to 30% of all traffic in city centres (Martens,
suggest that there are fewer cars on the road . Many renowned tech companies and car manufacturers
20 16) . Martens (20 16) predicts that the Autonomous Car will be fully working in the city centre around 2060 2075,
which gives the costs for one ride, whereas in a privately
are working on developing an Autonomous Vehicle such as Audi, BMW, Cadillac, Ford, General Motors, Google, Mercedes Benz, Nissan, Tesla, Toyota and Volvo, and the first test specimens are driving around various cities (Fagnant & Kockelman, 20 15) . As a lot of the prototypes
as the current models stop for all other traffic, resulting in the car standing still most of the time . Though the great effects and full implementation will take time,
are still being developed, this trend for now is taking
certain short term developments can be expected . Hagemeier (20 14) and Martens (20 16) expect that co
place in the industry rather than the physical city .
operative mobility, and cars that park themselves in a
parking garage will be available in a few years .With this technology cars can be parked closer to each other . Public Transport and especially MultiéModal Mobility , should be developed further (Boerrigter, 20 15; Meijl, 0 15) as it offers alternative travel options during rush 2 , hour (Schulõ van Haegen, 20 13), and though it is more expensive, it has a large capacity (Weijer, 20 15) . Electric Vehicles are to replace existing vehicles for Public Transport with the primary goal to reduce
the city centre (Gemeente Eindhoven, 20 13; Gemeente Amsterdam, 20 13; Mansveld, 20 15) . Subsidies are used as an incentive for people to use clean transportation
emissions (Schulõ van Haegen, 20 13) . One problem is charging these busses as they are always moving . However, it is possible to install wireless chargers at bus
lanes provides fast transport between suburbs and the city centre, with the aim to reduce car use (Gemeente
stops, so that at every stop the bus charges a liýle (Meijl, 0 15) . 2 Lievense ( 0 13) stresses that though Electric Vehicles 2 are a good start, conceptually we should look beyond to other possibilities . Schulõ van Haegen (20 13) agrees by stating that it is most important to keep options open for further development, and not to change the infrastructure in such a way that it only facilitates one technology . Policy analysis Policies tend to focus on pedestrians and other forms of nonémotorized transport such as bicycles (Gemeente Eindhoven 0 13; Gemeente Utrecht 0 15a) . A new , 2 , 2 concept in policy making is the use of hybrid spaces, for example space used for delivery trucks in the early morning and terraces in the afternoon (Gemeente
Amsterdam, 20 13) . BikeéSharing, more facilities to accommodate bikes and the integration with a Smart Card for payment are seen as important (Cuddy et al . , , 0 14 G 0 13) . 2 ; emeente Amsterdam, 2 One of the common goals is to reduce the amount of cars in city centres . This is done by actively promoting CaréSharing (Cuddy et al . 0 14; Mansveld 0 15; , , 2 , 2 Ministerie van Economische Zaken, 20 15; Waard & Meijles, 20 15), and reducing the amount of parking in
methods, such as CaréSharing, Public Transport, Bikeé Sharing and Eébikes instead of fuel powered vehicles ,
(Waard & Meijles, 20 15) . A seamless connection to Public Transport is seen as key in the city centre (Gemeente Utrecht, 20 15a) . High quality Public Transport using specified bus
Eindhoven 0 13) . ,2 For both private and Public Transport the aims are to , reduce emissions by promoting Electric Vehicles and developing the required charging infrastructure (Filho & Koýer, 20 15; Gemeente Eindhoven, 20 13; Mil, et al ., k 0 16 i i i E i 0 15) . For 2 ; M n ster e van conom sche Za en, 2 Public Transport a fast charging system at bus stops can ensure that the vehicle is fully charged at the end of the line (Cuddy, et al ., 20 14) . A possible technology for this, which also has great potential for CaréSharing, is inductive or wireless charging which can be integrated invisibly (Filho & Koýer 0 15; Mil et al . 0 16) avoiding ,2 , ,2 , the “significant diversification in townscape” caused by conductive charging stations (Filho & Koýer, 20 15) .
Data analysis There is a greater demand for all types of vehicles including bikes Eébikes mopeds cars Electric Vehicles , , , , , and motorbikes, but also a greater use of Public Transport (CBS, 20 15; Gemeente Utrecht, 20 15b; KiM, bike 0 15 ) C li i 2 a . yc sts have expressed the des re for fast lanes in the city of Utrecht the Netherlands (Gemeente , Utrecht, 20 15b), which is not surprising as the bike is the
most popular way of transport for short distances (see Figure 3) (CBS 0 15; HoogendoornéLanser et al . 0 14) . ,2 ,2 The data on CaréSharing appears contradicting . Caré Sharing replaces other modes of transport such as the
privately owned car (40%), train (35%), or is a ride not done without the availability of the service (16%) (KiM, 20 15b) .Yet 65% of the users of Car=Sharing services have
a Public Transport plan, as opposed to 35% of the non= users (Gier & EEema, 20 14), which suggests they use Public Transport more . Different datasets do find some commonalities . Car=Sharing users drive less kilometres
various policies, though Car=Sharing also is a popular topic . The datasets used mostly provided information on Car=Sharing, and the use of other transport modes as it is limited to currently employed technologies . Answering the second research question How do these Smart Mobility solutions aff ect the urban structure in existing cities ? requires a more elaborate approach .
on average (KiM, 20 15b) . 50% of the users do not own a car, and for 37% the service replaces a second car (Gier & EEema, 20 14; KiM, 20 15a) . Though the KiM (20 15a) admits that liEle is known on the effects of Car=Sharing, it does bring out some interesting estimations on the service . As of 20 15 120 .000m2 of
The increasing popularity of Bike=Sharing requires expansion of the existing docking stations and the implementation of new ones . At some locations this
parking spaces have been ‘freed’ due to Car=Sharing, as one shared vehicle replaces 4=8 privately owned and up to 36=84m2 parking space .Shared vehicles take up equal
Using a Smart Card for payment ensures integration in the Multi=Modal transport network . Seamless Multi=
space as regular cars, but are used 62 minutes a day on average as opposed to 30=45 minutes . So privately owned vehicles spend more time idle, but this piece of data suggests that one shared vehicle replaces two privately owned ones .
Surprisingly 88% of drivers in the morning rush hour , drive alone, and 80% in the evening rush hour (CBS, 20 15) providing great potential for ride=sharing . , Comparative analysis It is interesting to note that information on the six Smart Mobility solutions is not equally distributed among the different research methods . Literature focuses on Bike=Sharing and Car=Sharing systems but also on the , technical aspects of the Autonomous Vehicle and the Electric Vehicle . The trends mostly concern Bike=Sharing
and Car=Sharing whereas the Autonomous Vehicle is limited to tech companies and car manufacturers . The interviewees described the Autonomous Vehicle to have a lot of future potential . For now, however, the main focus is on optimizing Public Transport and Electric Vehicles . A similar viewpoint as can be observed in
expansion can be problematic as there is liEle physical space to expand, which would imply taking away space from pedestrians to accommodate this growth .
Modality requires that docking stations are in easy reach of other modes of transport .
A lot of the information on Car=Sharing appears false or somewhat contradicting .It is especially unclear whether Car=Sharing leads to a reduction of cars on the road and a decrease in parking pressure . Assuming that it does have a certain impact, albeit not as big as suggested, Car=Sharing has the potential to affect the city structure in a positive way . This is especially the case for older cities, in which the reduced number of cars can be compensated by reducing the number of road=side parking spots, restoring the original street scape and freeing space for pedestrians and cyclists . Naturally , these effects will grow as Car=Sharing gains popularity .
A successful ride=sharing system could enhance this effect tremendously .As 88% of cars during the morning rush hour and 80% during the evening rush hour have single occupancy, ride=sharing has a vast potential to not only reduce the amount of cars on the road, but also the amount of privately owned cars . The Autonomous Car can possibly fulfil this role by creating a synergy with Car=Sharing . In a perfect world where everyone uses Shared Autonomous Vehicles
there would be no road side parking, cars cruising for parking or traffic jams . Cars will be parked in large depots outside of the ity entre whi h frees up not only c c c space currently used for road side parking, but also (underground) parking garages . Of ourse reality will never be like su h an utopia and c c , even the large scale implementation of the Autonomous Vehi le will take de ades . But developments towards c c the Autonomous Vehicle will have spatial impacts in a shorter time span . The Co operative Intelligent Transport System provides the technology through which cars park autonomously, and hence can be parked closer to each other which can create more space in parking garages to repla e street parking . Real time c information on available parking pla es an redu e c c c ising reating more spa e on the roads themselves . r u c ,c c 4. Conclusion The Smart City is a trending topic and many of its facets are being researched . Not only does it include technological innovations, but the use of human capital and focus on the quality of life are key aspects of creating a Smart City . Within the field of Smart Mobility six solutions are often
des ribed in literature and these have been taken as the c , theoretical framework . They are in no specific order : 1. Bike Sharing 2. Car Sharing 3. 4. 5. 6.
Autonomous Vehicles Public Transport Electric Vehicles / Bikes Multi Modal Mobility
These solutions cannot be seen separately, as they function like a Venn diagram, with many touching and overlapping elements . Though all solutions within the framework were des ribed throughout the various resear h methods c c , Multi Modal Mobility does not appear to have any
dire t spatial impa t other than influen ing the lo ation c c, c c of other solutions such as docking stations for Bike Sharing . It operates more as a connective element between the different modes of transport . The Ele tri c c Vehi le in most ases forms a synergy with one of c c the other solutions . However, it does have a direct spatial impact due to the charging infrastructure that is required proving it to be a relevant ategory . The , c remaining four categories concern the main modes of transport: the car, bike and Public Transport . An additional Smart Mobility category that has not been des ribed above is the pedestrian as walking is a form of c transport . Furthermore, in the city centre the pedestrian is the main fo us of poli y makers and this will likely c c in rease in the future . Other Smart Mobility solutions c will not only reduce the amount of cars, but will also make transportation more efficient .For older cities these innovations provide an opportunity to at least partially , , return the city to its original structure . The innovations will free up space mostly in the city centre which can be used to accommodate pedestrians and contribute to a higher quality of life . This research provides a first step in discovering the spatial impacts of Smart Mobility solutions . Though more research is required so that policy makers, urban planners and others working on the implementation of Smart Mobility solutions know beforehand what the spatial consequences of these solutions will be . Ideally this information will be gathered in a comprehensive modelling tool . It is essential to obtain more information on the effects of these Smart Mobility solutions on usage . Currently this information is un lear as an be seen with the results c , c of Car Sharing which, in theory, should lead to a great reduction in the amount of privately owned cars and parking demand, but clearly fails to do so .Technological innovations within other fa ets of the Smart City an c c perhaps be used to gather such information .
Another crucial knowledge gap is how these Smart Mobility solutions affect the quality of life in a city , which is relevant for policy makers and urban planners working on the Smart City . The same can be said for the other Smart City themes as described by Giffinger, Haindlmaier & Kramar (20 10) . Though they describe Smart Living as the determining facet for quality of life, it should not be underestimated how much the other facets affect this too . It is of paramount importance to know the different impacts of these facets in order to design the cities of the future successfully . 5. Acknowledgements In this publication I made use of data from the Netherlands Mobility Panel administered by KiM Netherlands Institute for Transport Policy Analysis .
The MPN is a household panel, which main objectives are to establish short run and long run dynamics in travel behaviour of individuals and households, and to determine how changes in personal and household characteristics and in other travel related factors (e .g . economic crisis, reduced taxes on sustainable transport,
changes in land use or increased availability and use of ICT) correlate with changes in travel behaviour (see Hoogendoorn Lanser et al . (20 14) for more details) .
Starting July 20 13, respondents 12 years or older from 2 500 complete households recorded their travel data , using a three day travel diary . For each respondent, the diary provided information about all trips (stages) the respondent had taken (transport modes, trip purposes, travel companionship, delays, parking costs) . Between
20 13 and 20 16 this will be repeated at least yearly , with the same respondents . At the same time different , questionnaires were filled out offering a large amount of background information on respondents and their households .
6. References 1. 2.
3. 4. 5.
6.
7. 8.
9. 10. 11.
12.
13.
14.
2ge here. (n.d.). Schiphol GRT. Retrieved from 2ge here: h p:// www.2ge here.eu/projects/schiphol+grt/ Alessandrini, A., Campagna, A., Delle Site, P., Filippi, F., & Persia, L. (2015). Automated Vehicles and the Rethinking of Mobility and Cities. Transportation Research Procedia, 145+160. Angelidou, M. (2014). Smart City Policies: A Spatial Approach. Cities, S3+S11. Angelidou, M. (2015). Smart Cities: A Conjucture of Four Forces. Cities, 95+106. Baptista, P., Melo, S., & Rolim, C. (2014). Energy, environmental and mobility impacts of car+sharing systems. Empirical results from Lisbon, Portugal. Procedia+ Social and Behavioral Sciences(111), 28+ 37. Boerrigter, M. (2015, March 20). Interview met Michel Boerrigter, Founder en Director Calendar42, over de toekomst in mobiliteit. (C. Ketelaar+Damen, Interviewer) Bree, M., Kamminga, J., & Theunissen, L. (2010). Hoe bevalt de OV+ fiets? Klantenonderzoek 2009. Fietsersbond. Utrecht: Fietsersbond. Calvillo, C., Sánchez+Miralles, A., & Villar, J. (2016). Energy Management and Planning in Smart Cities. Renewable and Sustainable Energy Reviews, 273+287. Centraal Bureau voor de Statistiek [CBS]. (2015). Transport en Mobiliteit 2015. Den Haag: Centraal Bureau voor de Statistiek. Corcoran, J., & Li, T. (2014). Spatial analytical approaches in public bicycle sharing programs. Journal of Transport Geography, 268+271. Cuddy, M., Epstein, A., Maloney, C., Westrom, R., Hassol, J., Kim, A., . . . Be isworth, C. (2014). The Smart/Connected Cityu and Its Implications for Connected Transportation. Washington: U.S. Department of Transportation. Debnath, A. K., Chin, H., Haque, M., & Yuen, B. (2014). A methodological framework for benchmarking smart transport cities. Cities, 37, 47+56. Dieten, R. J. (2015). Identifying preferences regarding carsharing systems. (Master Thesis): Eindhoven University of Technology, Eindhoven. Driel, P., & HaSamp, W. (2015, December). De effecten van autodelen op autogebruik. Tijdschrift Vervoerswetenschap, 51(4), pp. 18+38.
15.
16. 17. 18. 19. 20. 21. 22.
23. 24. 25.
26. 27. 28.
29.
30.
Fagnant, D., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy reccomendations. Transportation Research Part A, 167+181. Filho, W., & Ko er, R. (2015). E+Mobility in Europe. (electronic): Springer International Publishing AG SwiUerland. Gemeente Amsterdam. (2013). Amsterdam Aantrekkelijk Bereikbaar. Amsterdam: Gemeente Amsterdam. Gemeente Eindhoven. (2013). Eindhoven op Weg. Eindhoven. Gemeente Utrecht. (2015a). Actieplan Voetgangers. Utrecht: Gemeente Utrecht. Gemeente Utrecht. (2015b). Utrecht Monitor 2015. Utrecht: Gemeente Utrecht. Gier, M., & E ema, A. (2014). Monitor Autodelen 2014. Amsterdam: TNS NIPO. Giffinger, R., Haindlmaier, G., & Kramar, H. (2010). The role of rankings in growing city competition. Urban Research & Practice, 299+312. GloU+Richter, M. (2012). Car+Sharing + “Car+on+call” for reclaiming street space. Procedia Social and Behavioral Sciences, 1454+1463. Grip, S. (2015). Smart Cities. Den Haag: Ministerie van Infrastructuur en Milieu. Hagemeier, F. (2014, October 03). Frank Hagemeier, Siemens: ‘Coöperatieve mobiliteit zal wegkantgebonden signalering vervangen’. (C. Ketelaar+Damen, Interviewer) Hall, P. (1989). And that was the future... Futures, 498+507. Hollands, R. (2008). Will the real smart city please stand up? City, 303+320. Hoogendoorn+Lanser, S., N. Schaap & M.+J. Olde Kalter (2015). The Netherlands Mobility Panel: An innovative design approach for web+based longitudinal travel data collection. 10th International Conference on Transport Survey Methods, Transportation Research Procedia 11 (2015) pp 311+329. Horn, M. E. (2002). Fleet Scheduling and Dispatching for Demand+ Responsive Passenger Services. Transportation Research part C, 35+63. Kaspi, M., Raviv, T., Michal Tzur, & Galili, H. (2015). Regulating vehicle sharing systems through parking reservation policies: Analysis and performance bounds. European Journal of Operational
31.
32.
33.
34. 35.
36. 37.
38. 39.
40. 41.
42.
43.
44.
Research, 969p987. Kennisinstituut voor Mobiliteitsbeleid [KiM]. (2015a). Mijn auto, jouw auto, onze auto. Den Haag: Ministerie van Infrastructuur en Milieu. [KiM]. (2015b). Kennisinstituut voor Mobiliteitsbeleid Mobiliteitsbeeld 2015. Den Haag: Kennisinstituut voor Mobiliteitsbeleid. Knowles, R., & Rozenblat, C. (2016). Sir Peter Hall: Pioneer in Regional Planning, Transport and Urban Geography. (electronic): SpringerNature. Komninos, N. (2011). Intelligent cities: Variable geometries of spatial intelligence. Intelligent Buildings International, 172p188. Lee, J., Hancock, M., & Hu, M.pC. (2014). Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco. Technological Forecasting & Social Change, 80p99. Letaifa, S. (2015). How to strategize smart cities: Revealing the SMART model. Journal of Business Research, 1414p1419. Lievense, E. (2013, November 20). Interview met Elbert Lievense, Stichting DOET/ICU, over ‘Duurzaam Vervoer, hoe nu verder’. (C. KetelaarpDamen, Interviewer) Loose, W. (2009). Autodelen: De impact op het milieu. Hannover: Bundesverband CarSharing e.V. Retrieved from www.momopcs.eu Mansveld, W. (2015, June 19). Kansen en onzekerheden van autodelen. The Hague, Zuid Holland, The Netherlands: Ministerie van Infrastructuur en Milieu. MarsalpLlacuna, M.pL., & Segal, M. (in press). The Intelligenter Method (I) for making “smarter” city projects and plans. Cities. Martens, P. (2016, March 9). Interview met Peter Martens over de parkeerbrache naar aanleiding van Intertraffic Amsterdam 2016. (C. KetelaarpDamen, Interviewer) Mazhar Rathore, M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, 63p80. Meijl, J. (2015, April 15). Interview met Jan van Meijl, VDL Bus & Coach Nederland, over Collectief Vervoer. (C. KetelaarpDamen, Interviewer) Mil, B., Schelven, R., & Kuiperi, F. (2016). Terugblik en vooruitblik op het beleid voor elektrisch vervoer. Den Haag: KWINK groep.
45. 46. 47.
48. 49. 50. 51.
52.
53.
54. 55. 56.
57.
Ministerie van Economische Zaken. (2015). Green Deals in beeld. Den Haag: Rijksoverheid. Nabielek, K., & Hamers, D. (2015). De Stad Verbeeld. Den Haag: PBL. Neiro¯i, P., De Marco, A., Cagliano, A., Mangano, G., & Scorrano, F. (2014). Current trends in Smart City initiatives: Some stylised facts. Cities, 25p36. Nijland, H., Meerkerk, J., & Hoen, A. (2015). Effecten van autodelen op mobiliteit en CO2puitstoot. Den Haag: PBL. NS. (2016). Jarrapportage HRN Concessie 2015. Utrecht: NS. Pol, M., & Hoen, A. (2013). Nieuwe elektrische vervoersconcepten in Nederland. Pe¯en: ECN. Ricci, M. (2015). Bike sharing: A review of evidence on impacts and processes of implementation and operation. Research in Transportation Business & Management, 28p38. Schmöller, S., & Bogenberger, K. (2014). Analyzing External Factors on the Spatial and Temporal Demand of Car Sharing Systems. Procedia p Social and Behavioral Sciences, 8p17. Schul² van Haegen, M. (2013, December 17). Interview Infrasite met minister Schul² van Haegen over de weg naar Duurzame Mobiliteit. (C. KetelaarpDamen, Interviewer) Siemens. (n.d.). Copoperative mobility: oppertunities and choices. White paper. Waard, E., & Meijles, A. (2015). Actieplan Schoon Vervoer 2015p2020. Utrecht: Gemeente Utrecht. Weijer, C. (2015, May 1). Interview met Carlo van de Weijer over overeenkomsten tussen wegverkeer en railverkeer. (C. Ketelaarp Damen, Interviewer) Zhang, W., Guhathakurta, S., Fang, J., & Zhang, G. (2015). Eploring the impact of shared autonomous vehicles on urban parking demand: An agentpbased simulation approach. Sustainable Cities and Society, 34p45.