U.S. Cities: shifting mobility patterns by Systematica

Transform Transport

U.S. CITIES Shifting Mobility Patterns

" Processed data is information. Processed information is knowledge. Processed knowledge is wisdom." Ankala V. Subbarao

U.S. Cities: shifting mobility patterns U.S. Cities: shifting mobility patterns © 2021 Systematica US Inc. All mobility studies presented in this book are developed by Systematica US Inc. All rights reserved. Unauthorised use is prohibited. No part of this publication may be reproduced in any form or by any means without the written permission of Systematica US Inc. Systematica US Inc. 551 Madison Avenue, Suite 450 New York, NY 10022 www.systematica.net newyork@systematica.net ISBN: 978-88-944179-5-1 Graphic design: www.parco.studio Printed in March 2021

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Table of Contents

Harnessing data: a multi-pronged approach p.9 ● tracing mobility through a multi-focal data lens ● using big data to read travel trends ● the role of real-time in times surreal

Target profiles: a diverse city mix p.17 ● megaregional methodology: an economic geography of the U.S. ● multi-dimensional city growth index ● diverse city profiles on the growth spectrum ● observing the shift: cross-city trends

Tracing emerging mobility trends p.27 ● are there new mobility directions? ● strength in numbers: where are millennial populations growing? ● the modal choice shift: a generational gap?

● telecommuting on the rise ● where is car reliance the steepest?

Cross-city analysis: trip data under the microscope p.45 ● setting the scene ● zone classifications ● trip trait 1: trip length ● trip trait 2: trip purpose ● trip trait 3: movement density ● trip trait 4: weekend movement ● trip trait 5: external vs. internal trips ● concluding remarks

Towards a new mobility landscape p.69 ● the progressive cities framework ● the big takeaways: lessons learnt and the main trends observed ● when things took a sharp turn: a final reflection

● the back to downtown trend ● sharing mobility: a differential upsurge 3

This book is an open exploration in mobility trends with a focus on five flourishing and forward-looking cities in the United States. The choice of these five cities is based on indicators of positive growth and development reflected in a multidimensional index for socio-economic and travel related metrics. The aim of the book is rooted in data manipulation. It applies both top-down and bottom-up analytical approaches to test emerging trends in mobility: it looks both at largescale changes across census intervals as well as minute, hour-by-hour changes observed across volumes of location based data from big data sources. This multiscalar and data-driven approach aims to offer a broad picture of the current mobility landscape in these five progressive cities while imagining potential direction trajectories for the country's future. 4

Given the COVID-19 moment in which this book was published, we understand the amplified importance of such interrogations at the brink of a turning point; an upheaval of the status quo. We realize that the long-run effects that this moment of disruption may produce in the American mobility landscape are cloaked in uncertainty. Yet, it is precisely because of this uncertainty that the findings within the pages of this publication are so valuable, because they offer a snapshot of some of the defining directions and travel characteristics preceding the major disruption. Beyond results, this book's contribution also lies in its methodology and approach following cities with progressive traits as trendsetters for the country's future. Ultimately, the book offers an evidence-driven narrative for future mobility based on a collection of timely data. 5

Foreword

We’re living in a time of digital transformation. Access to mobility data is as easy as a click to download. We have tools and technologies that allow us to process data faster and analyze complex data sets in minutes, instead of hours or days. However, to bring value, it’s more than collecting data, we must also process, interpret, and make sense of data. Data alone cannot speak for itself. A quote from Pablo Picasso states, “Computers are useless, they can only give you answers”, purporting we need to find the proper questions to ask to get answers that can be applied to make a meaningful impact in our living world. Gone are the days of being problem solvers, we must now be problem finders and data can help with this paradigm shift.

To be problem finders we must engage with communities, public officials, and industry professionals to find the right questions to ask and collect a variety of data that can be used to develop information. Processing mobility data can provide us information on commuting patterns, areas of high origins and destinations, and health-related indicators. With this information, we can build foundational knowledge on urban mobility in cities. We must use that knowledge to make planning decisions that provide transportation options to those that need it most and create flexible conditions for transportation technologies, because the decisions being made right now, in this era of autonomous vehicles, E-scooters and emerging urban air mobility vehicles will impact future generations to come. Mobility data contains rich information on locations and patterns of movement which reflect many aspects of life and can be used for combatting traffic congestion and air pollution. The EPA reports that nearly 30% of greenhouse emissions come from the transportation industry, but with knowledge applied from big data analytics we can achieve efficiency. Estimates show we can have a 15% reduction if we employ better planning tactics, such as avoiding trips, or shifting trips to other modes. We must decide the future we want to build and the type of city we want to live in, together.

IIIV

Image credits: efetova / iStock Photo

Harnessing data: a multi-pronged approach

Tracing mobility through a multi-focal data lens In order to trace mobility trends in the United States, data has been compiled, sorted and employed from a number of robust data sources as outlined on the opposite page. Covering areas ranging from sociodemographic and population statistics to origindestination trip data, the collected data is variously employed throughout the body of this book to test existing arguments and convey new ones based on sound empirical evidence. Chapters 3 and 4, in which the bulk of findings are discussed, follow contrasting data approaches. Chapter 3 starts from a number of predetermined problems and tests them through data, whereas Chapter 4 starts from the data to follow a bottomup approach to identify trends and define problems. The analysis presented herein and in the chapters to follow consists of a multi-scalar approach starting from the national scale, whereby a number of mobility trends are interrogated at the widest lens, and ending with the city scale, whereby a predefined list of selected cities are closely examined to extract mobility information at a higher level of detail. This layered analysis requires a critical coordination of diverse data sets to ensure compatibility and comparability across the spectrum.

Image credits: r.classen / Shutterstock.com

Chapter 3.

Chapter 4.

Top-down approach

Bottom-up approach

Symptomatic

Systemic

Problem

Data

Socio-demographic and Population Transport-related Data Multi-scalar approach

National

Regional

Urban

ACS 2016 5-year (source: United States Census Bureau) EPA (United States Environmental Protection Agency)

Infrastructure Provisions TIGER (Topologically Integrated Geographic Encoding and Referencing -extracts from the Census Bureau's MAF/ TIGER database) U.S. Department of Transportation, Bureau of Transportation Statistics

Traffic Data (Origins and Destinations) Residence County to Workplace County Commuting Flows for the United States and Puerto Rico: 5-Year ACS, 20092013, source: U.S. Census Bureau, American Community Survey CTPP 2006-2010 Census Tract Flows, source: US Department of Transportation, Federal Highway Administration LEHD 2015, source: US Department of Transportation, Federal Highway Administration 11

Using Big Data to read travel trends As popularized by MetLife VP Oscar Herencia, the ability to harness the benefits of big data depends on how we manage its five defining V's: volume, velocity, variety, veracity and value. The same applies to applications in the transport planning field, where information potentials diverge from traditional data gathering methods.

Benefits

Continuous monitoring offers possibilities to produce high-level resolution information on travel behavior, especially in terms of temporal aspects, which are systemically weak in traditional datasets. It also allows for long-term tracking, which had previously been very difficult to quantify. Moreover, the fact of automated data collection eliminates data inaccuracy associated with travel survey responses due to human error.

Drawbacks

Opportunities

Since data collected through passive big data sources was originally designed for other purposes, essential attributes typically collected through traditional methods (such as trip purpose) are often missing. Proxies designed to infer these lacking attributes can only partially compensate for these qualitative losses. Moreover, sampling bias tends to be a concern when it comes to GPS, cellphone and Bluetooth data, which tend to overrepresent movements of financially active, technologically apt and younger members of society.

Big data provides the opportunity to trace unexpected trends and changes at a fine scale of granulation as they come up, without the need to extrapolate or deduce events post-hoc. The rawness of the data form allows for direct manipulation at any point in time. Moreover, the sheer overall volume of data allows for countering sampling bias with targeted analysis for otherwise underrepresented groups, who make up a small share but sizeable sample in the overall dataset.

Challenges 'Future-proofing' the analytical methods associated with big data is difficult given the uncertain availability of privately owned non-purpose-oriented data. The tradeoff between data accuracy and privacy is another long-discussed challenge. Filtering systems intended to protect user confidentiality ultimately tamper with the extent to which the data can become useful.

"99 second-hand smartphones are transported in a handcart to generate a virtual traffic jam in Google Maps. Through this activity, it is possible to turn a green street [into] red which has an impact in the physical world by navigating cars on another route to avoid being stuck in traffic." #googlemapshacks Project attribution and image credits: Simon Weckert Text by Moritz Alhert - The Power of Virtual Maps

The role of real-time in times surreal Big data is often perceived as 'the end of theory' because it provides the opportunity to derive insight from empirical evidence rather than building it on theoretical constructs. Never is this need greater than during an emergency situation when an amalgam of unprecedented conditions take place.

Real-time mobility data is having major impacts on the transport planning field in terms of how we are planning for future mobility. The ongoing pandemic situation has shown us how big data input can play a major role in tracing and redirecting travel behavior during different phases of the pandemic response cycle: from complete mobility lockdown to a variety of in-between scenarios. Not restricted to the pandemic, the availability of realtime data during any emergent situation opens up the possibility to design reactive intervention measures long before the causal links to the emerging situation are fully understood. In the U.S. as in around the world, various policy measures have been enacted in attempts to curb the pandemic and reduce the virus spread, in part guided by real-time analysis of mobility patterns. City governments and transit agencies are on the front lines of the pandemic response, relying on the support of real-time data to efficiently allocate resources in rapidly changing times. Beyond contact tracing, travel data is also used to guide street reclamation efforts via vehicular flow restrictions in areas with low pedestrian access to public space. Such programs include “Slow Street,” “Safe Street,” “Stay Healthy Street,” “Keep Moving Street” and a variety of other versions deployed across the country. Likewise, measures to reduce contact on public transport have varied, relying, in part, on data monitoring methods to prevent heavy crowding during rush hour movements. This book focuses on data points captured prior to the pandemic situation, i.e. before the status quo was overturned. Analysis of trip trends before the 14

disruption is important. It provides a detailed look into the direction of mobility trends before the transient, extrinsic factor prompted large-scale structural changes and modified people's mobility behaviors. In times of disruption, often the best lense to use to anticipate trends in the future is not based on what is happening now as a result of multiple confounding factors, but in the moment before that when people and governments were not reacting to an abnormal threat and mobility decisions were based on long-term overarching conditions. To give one example from this book, it helps to see that telecommuting trends were already on the rise before the pandemic reinforced them. This shows us that a significant portion of the American workforce was already willing to give up the office, virus spread risk aside. We can infer then, that the potential for this trend to continue - even if in different ways than anticipated - is strong. What the real-time information analyzed within the pages of this book shows us is a snapshot of how mobility patterns developed under 'normal' circumstances. For the 'new normal' or the 'next normal', it will take continuous monitoring, dissecting and analyzing of large-scale datasets to determine what that might look like on the long run, beyond the current transitory period.

The 57 Freeway in Orange County, California nearly empty at rush hour on March 23rd, 2020, with people staying inside in response to the COVID-19 outbreak. Image credits: Matt Gush

IIIV Target profiles: a diverse city mix

Megaregional methodology: an economic geography of the U.S. Merging labor market regionalization with “natural” community groupings The approach taken to define megaregions in the following studies is based on the methodical approach of Garrett Dash Nelson and Alasdair Rae that combines spatial proximity (and labor market regionalization) with commuter geographies based on strong volumes of commuter flow between census tracts. The 2016 paper uses a dataset of more than 4,000,000 commuter flows to identify a total of 55 megaregions. Two methods are applied: the first relies on a visual heuristic for understanding areal aggregation, while the second uses a computational partitioning algorithm. The visual heuristic approach aims to develop representations which identify core labor market areas based on Euclidean distance (i.e. straight line distance) and network flow volumes, whereas the latter Megaregional economic geography of the U.S.

approach focuses on intra-community connections to detect patterns of “natural” community groupings in commuter geography. The result is a combined computational-visual approach. Unlike traditional geographic divisions of the American landscape, this approach often cuts across state lines, massing regions that are often larger and centered on major metropolitan hubs surrounded by smaller subcenters. In essence, it tests the premise of Tobler’s “First Law” of geography that ‘near things are more related than distant things’. This method, instead, aims to consider the widening scale of labor markets and the integration of capital flows into a new understanding of urban agglomerations.

Heuristic approach

Algorithmic approach

55 megaregions

This graphic is based on the work of Garrett Dash Nelson and Alasdair Rae in An Economic Geography of the United States: From Commutes to Megaregions Source: PLoS ONE Journal

19 19

Multi-dimensional city growth index Positive Growth

City Growth Index Indicators

Population growth Post-Millennials growth Millennials growth Workers at Home growth Workers non-working at home growth Growth of people with at least an associate's degree Employed People growth Focus cities

The following scale places cities of the U.S. on a compound growth metric based on their performance in a number of growth areas as defined in the key below. This combined index acts as a starting point for defining a selection of well-performing cities for deeper analysis.

Negative Growth

Diverse city profiles on the growth spectrum The radar charts presented on the opposite page visualize some fast-growing and slow-growing city profiles in the U.S with respect to a number of preset indicators. The five selected fast-growing cities are chosen as the focus cities for detailed analysis throughout this book. The charts demonstrate the diversity of the selected cities with respect to overall growth volumes and variable growth patterns. Growth volumes of each city are visualized as plane surface areas, measured as the share of the maximum plane (100% growth in all directions).

Radar chart index

Fast Growth

Slow Growth

City growth volumes as percentage of maximum

Radar chart index relative to city growth indicators

Observing the shift cross-city trends Opposing poles

Demographic trend

While the selected cities have seen some of the highest positive changes (5-8%) in terms of attracting young millennials, who are often recognized as drivers of innovation and proponents of environmental mobility forms, commuting times and private modal share changes contrastingly place them at the bottom of the scale. While increased commuting times may potentially point to the economic attractiveness of a city to the surrounding region, it also reflects worsening traffic conditions and potential negative effects on wellbeing. In fairness, average commuting times have only dropped in one city between 2012 and 2016 (Albuquerque Plateaus) while having increased in all other U.S. cities. However, the rate of increase is relatively high in the selected cities, particularly Seattle, which shows the second highest growth in commuting times in the country (about 5%). In retrospect, Seattle is amongst the top 3 performers in terms of private modal share reduction, which shows that although people may be traveling further distances for work and other trips, they are increasingly relying on public modes of transport more. The remaining cities, however, have seen slight rises in private modal shares in the order of 0.0-0.5% within the four-year period.

2012-2016 Change

Millennials (20-39yo) Top

Bottom Focus cities

Looking at how the trends of the selected cities rank in key demographic, commuting and behavioral metrics with respect to the rest of the nation gives us a deeper understanding of their relative performance in key mobility indicators.

Commuting trend

Behavioral trend

Average Commuting Time

Private Modal Share

2012-2016 Change

2012-2016 Change Top

Top

Bottom

Image credits: kehn hermano

IIIV Tracking emerging mobility trends

Are there new mobility directions?

Mobility trends are in a constant state of change. Relying on census data, the following studies are an attempt to quantify the latest observable changes, discerning hear-say from real trends evidenced in the latest census data for select cities and across the U.S. Image credits: Matthias Ripp

Strength in numbers: where are millennial populations growing? Data clearly shows population surges in America's Rocky Mountains and Southwest regions

In this study, the geography of the United States is segmented into 55 megaregions, redefining regions as areas with commuting and economic relations. The megaregional map is based on a superimposition of visual and logarithmic computations of megaregions; ensuring it is grounded in empirical analysis but refined using interpretive cartographic methods. A general reading of the millennial population (defined at the time of the study as those falling between the ages of 25-44) in the United States in absolute numbers based on 2016 census data highlights higher concentrations in the coastal regions. This trend is in keeping with general population figures. According to the United States Census Bureau, about 30 per cent of the population lived in counties directly on shorelines by 2017; a 15 per cent increase since 2000. Popular studies and readings on the habits of millennials tend to suggest a clear demarcation from previous generations, and particularly in the United States. Among other things, it is suggested that they are more transit-oriented, less car-oriented and more open to alternative forms of mobility than their predecessors. Looking at the shares of millennials 30

relative to total populations across the country, we find that this share is generally above 20% in most regions today. Comparing this data with the situation just 4 years prior, we find that millennial shares rose in most regions across the United States, with profound increases particularly in parts of central and western America, where increases are recorded at 10% and above. The highest increases in millennial populations are all concentrated in the state of Texas, where three of the five most attractive cities for millennials are located (as previously shown in Chapter Two). What do these demographic changes mean for American mobility trends? The following section studies a number of mobility trends in cities of diverse demographic and economic patterns as a way of investigating emerging mobility cultures. By interpreting data from the most recent census figures, we explore whether these surges in millennial populations are accompanied by any mobility shifts in America's urban areas.

absolute millennial populations (2016) ■ ■ ■ ■ ■ ■

0-100,000 100,000-400,000 400,000-1,000,000 1,000,000-2,250,000 2,250,000-5,000,000 >5,000,000

population share of millennials (2016) ■ 0-20% ■ 20-23% ■ 23-25% ■ 25-28% ■ 28-30% ■ >30%

millennial population change (2012-2016) ■ <-10% ■ -9%--5% ■ -4%-0% ■ 1%-5% ■ 6%-10% ■ >10%

The modal choice shift: a generational gap? Evidence of a 'multi-modal generation' Across the Western world and particularly in the United States, talk of a rise of a 'multi-modal generation' is common. Shifts in behavior are commonly attributed to rising environmental concerns and awareness but could just as well be linked to economic factors. The following study looks at the data of the five selected cities in the United States and shows that this is not necessarily the case everywhere or for all age groups.

a more balanced modal split than the other cities, shows significantly higher public transit and active travel than the national average. Public transit use is also increasing among the younger generations in Seattle, and partially in Savannah as well. Overall, change trends over the four-year period do not indicate adequate replacement between the three main modes of travel.

In the five selected cities, reliance on private modes of transport (predominantly cars) is generally below the national average, and has mostly decreased between 2012 and 2016, with exceptions in the youngest driving age group in Denver and Austin where we see a slight increase in car usership. A notable drop in the Over 65 age group in Austin points to shifting behaviors in this Texas city, however, the trend for public and active modes for the same group is also a declining one, pointing to possible replacement of the car with sharing modes as opposed to public or active modes. Understanding this unique Austin trend requires deeper analysis of contextual conditions of the city, including demographic and migratory shifts.

The data does not support the argument that younger generations today are driving (far) less than older citizens. However, they are using public transit, walking and cycling significantly more than older generations do. The trend of the graph for public transit use follows a downward pattern in all cities. That is to say that the older you get, the less likely you are to rely on public transit. For active transport, there is an even sharper distinction between the youngest age group and the consequent categories. Across cities, this group is at least twice as likely to walk or cycle than any other group. This could owe in part to the lack of dependents. In the United States, the average age for women to have a first child is 26; 31 for men. For many, traveling with dependents makes it more difficult to travel on foot or by cycling. In short, there is some evidence that younger generations are more multimodal, yet further historical evidence is needed to establish a long-term trend.

The following data also shows that in all but one of the cities under study, there are lower public transit ridership and active transport rates than the national average across categories. Seattle, which overall has

traffic and pedestrians on Hollywood Boulevard in downtown Los Angeles. Image credits: Sean Pavone / Shutterstock.com

33 33

As the previous data shows, some factors have stronger linear relationships with modal choice. In other words, some groups of people have higher tendencies to rely on a particular mode than others. The previous section briefly explored the role of age as a determining factor in the use of different modes. In the chart below, factors relating to household structure, income level and the physical environment in which one lives are also explored to better understand whether being part of a particular group raises or lowers one's modal choice tendencies. This data is based on the five core cities of the study and does not necessarily extend to the rest of the nation, nor does it attempt to investigate causality. Furthermore, in most categories with an evident correlation presented below, the general tendency does not necessarily apply to all cities. In all but early millennial car trends, at least one city shows no correlation at all. Early millennials in Los Angeles, however, are unique among their peers in that they show a positive correlation with private car use, whereas the same age group in the other cities has a high negative correlation with private modes. Alternatively, early millennials in all cities have a very

strong tendency to walk or cycle, while Generation X users in all five cities have a strong tendency towards private car use. Several conditions relating to household structure seem to make the average citizen in these cities opt more towards the car: those who live in large households with dependents and those in middle household income groups have a somewhat strong likelihood of relying on private cars. The lowest income bracket, however, is far more likely to rely on public transit. In Seattle, where the public transit offering is already far more advanced than the other cities, this correlation does not exist. Other factors have notable coincidence with specific modal choices such as population density. In the given cities, again apart from Los Angeles, people living in areas of high residential density opt for walking and cycling more, and for cars less. In Los Angeles, these choices seem to be independent of locational variances with respect to density. The data studied also shows that public transport use tends to go hand in hand with active travel rates to a certain degree. The outlier in this case is Savannah, where no relationship is evident.

Modal choice correlation with multiple factors in 5 selected cities

Probable modal choice by major categories

ca. 1913

Congress Avenue, downtown Austin

Congress Avenue, Downtown Austin ca. 1913; 2010 Image credits: (top) Austin History Center - (bottom): Lone Star Mike featured on Wikimedia Commons

2010

Congress Avenue, downtown Austin

The Back to Downtown trend

Millennials are in fact increasing in central areas of all five cities under study and decreasing from some surrounding districts. Save Austin, the same trend cannot be traced for the Over-65 population, which in the short fouryear span has tended to increase in suburban areas. In Savannah, a cluster of increases is recorded in the area just northeast of the downtown area.

Where college graduates moved in the 50 largest U.S. metros by decade

Downtown Suburbs

Source: Bloomberg CityLab; Data from: Couture & Handbury (2016)

millennial population change (2012-2016)

SEATTLE

DENVER

LOS ANGELES

AUSTIN

SAVANNAH

over 65s population change (2012-2016)

Downtown areas are defined in this study by overlapping census-based data, morphological characteristics and flow analysis (OD relations).

Sharing mobility: a differential upsurge Diverse growth trends as new mobility options set their course From carsharing to bikesharing and scootersharing, much has progressed in the U.S. mobility landscape in the past decade or so. New shared mobility services have been exponentially growing in the past years, with various trends across the country. As the opposite maps show, bikeshare systems, which have been growing since 2010, today range from less than 1000 bikes in some cities to 10,000 bikes in others. Scootersharing, in retrospect, has only been around since 2017. Yet, a number of cities have rapidly surpassed the 10,000 scooter mark. Likewise, the carsharing market has seen a continuous upward trend in the decade 2006-2016, growing by 15 times the number of registered members across North America in 10 years (as shown below). This diverse uptake of shared mobility services is evident in the cities under study. Taking bikeshare as an example, we find that Denver's B Cycle system is ahead of the race, dating back to 2010. Los Angeles' Metro bikeshare system on the other hand has only been around since 2015. Even newer, Seattle's bikeshare system, which as of 2019 is composed of two private providers (JUMP and Lime) and is only just beginning to find ground after failed pilot attempts in 2015/2016.

84 M

shared micromobility trips in 2018

Shared micromobility system sizes across contiguous U.S. (2018)

Docked Bikeshare <1000 bikes <2000 bikes <7000 bikes 10,000+ bike

Dockless Scootershare <2000 scooters <5000 scooters 10,000+ scooters

Data and map source: NACTO

1.8 M

carsharing members in 2016*

Scootershare Dockless bikeshare Docked bikeshare Number of members

Shared Micromobility Growth in the U.S. 2010-2018. Data source: NACTO

Carsharing Growth in North America 2006-2016. Data source: Shaheen, S., Cohen, A., & Jaffee, M. (2018). Innovative Mobility Carsharing Outlook. UC Berkeley.

The selected cities vary in their offerings of shared mobility services. Bikeshare trends are one example: while Denver's B Cycle system has been around since 2010, Seattle's bikeshare program only began in 2016 and companies are still changing year on year.

Micromobility services available in selected cities by type and provider. Data source: NUMO (New Urban Mobility Alliance)

Denver B Cycle

Austin B Cycle

Los Angeles Metro Bikeshare

Bikeshare growths for individual providers in selected cities. Data sources: various

Telecommuting on the rise Prior to the large-scale adoption of home working across the U.S. during the 2020 pandemic situation1, a clear rise in telework popularity was already in motion. Between 2001 and 2016, the share of full-time teleworkers more than doubled in the U.S., suggesting that it was already gaining popularity before there was a direct need for it. share of full-time teleworkers in the US, 2001 to 2016. Data source: American Community Survey 3.4%

3.2%

3.1%

3.0%

2.8%

2.8% 2.6%

2.5%

2.4% Share of teleworkers (%)

2.2%

2.1%

2.0%

1.6%

1.2%

2.3% 2.3%

1.9%

1.8%

1.4%

2.2%

2.6%

1.2% 1.2%

1.3%

1.7%

1.4% 1.4%

1.0% 0.8% 0.6% 0.4%

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

0.0%

2001

0.2%

1 Initial surveys of American workers in early April and May of 2020 showed that about half of those employed before COVID-19 were working from home (http://www.nber.org/papers/w27344). Where this share is set to plateau following the pandemic is still unclear, but it is likely well above the shares prior to the emergency situation.

Home-based: the role of age and place Data from 2016 also reveals that higher age groups are more prone to teleworking than their younger counterparts. The category with the largest workforce percentage telecommuting is that of 45-54. Locational differences are also notable. The below map, based on U.S. Census figures from 2018, divides U.S. states based on the percentage of full-time telecommuters in each state. Colorado (7%), Washington (5.6%), California (5.4%) and Georgia (5.1%) all rank among the highest states in the United States for full-time telecommuters, while Texas falls in the second highest category (4.3%). Colorado, the highest of the group, is also the number one state for fulltime telecommuters across the country. telecommuter

non-telecommuter

GA TX

Source: U.S. Census Bureau

5-7.1%

3-3.9%

4-4.9%

2-2.9%

city trends

Share of full-time teleworkers in the US by State (2018 data)

Seattle

5.5%

0.3%

Savannah 0.2%

Los Angeles

Denver

Austin

4.4%

5.5%

0.4%

0.7%

% remote % remote workers 2016 workers 2016

6.8%

5.5%

%% change remote change of of remote workers 2012-20162012-2016 workers

Looking at the selected cities, we find that all cities but Savannah surpass the U.S. average for shares of remote workers in 2016. Denver, Colorado has the highest share of 6.8%, well above the 5% average. This is not surprising given the city's high shares of telecommunications and technology industries. Percentage change in the 2012-2016 period indicates that remote workers are increasing in all of the selected cities. Remote workers in Austin, Denver and Los Angeles are increasing at rates higher than the U.S. average, while Seattle and Savannah show slower relative rates of increase. 41

AUSTIN LOS ANGELES

Cost of transport depends on the mode used and where one lives. While the average annual transportation costs per household dropped between 2013 and 2016 nationwide, where a person lives has been found to influence cost, especially for car owners. In Seattle, Denver and Los Angeles, it is about 20% cheaper to get around for those living in Downtown than those living in the suburbs.

SAVANNAH

Where is car reliance the steepest?

% Income on Car Expenditure

Percentage of income on car expenditure in the U.S. (2013-2016)

20%

17.6%

17.0%

2014

2015

15.8%

DENVER

16% 12% 8%

2013

2016

vehicle purchase

gasoline

other vehicle purchases

transportation

Transportation costs as a percentage of income ■ <8% ■ 8-18% ■ 18-29% ■ >29%

SEATTLE

Downtown Maps of transportation costs as a percentage of income in the five selected cities

The Who and Where of car ownership The likelihood of a person or household of owning a (or several) cars varies significantly across demographic groups and from city to city. The below chart shows the correlation between car ownership and various socio-demographic traits. In general, there is consensus across cities on the plausibility of owning a car depending on your social situation, with some exceptions. For example, you are less likely to own a car as an early millennial living in all cities but Los Angeles. Generally, the likelihood of car ownership increases if you are part of a large household and if you are in the higher income quintile. You are less likely to have a private car at your disposal if you are an early millennial,

if you are part of a low-income household or if you live in a high density area. The graphs at the bottom of this page demonstrate trends of car ownership, private modal shares and active modal shares per 1000 people in each city. The diagrams show that Seattle is the city with the most progressive trends amongst all studied cities. It is the only city with a decreasing car ownership trend, the only one where the modal share of private vehicles is decreasing at a considerable rate and it is also the city showing the most promising status on active modal shares, which are relatively high and increasing.

Likelihood of owning a car by major categories A large household (with children between 0-19yo) Early millennials (20-29yo) A 0-50k income household A 100-150k income household Living in a high population area

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IIIV Cross-city analysis: trip data under the microscope

Setting the scene Methodological approach This chapter takes a closer look at movement patterns in the selected cities to see how they differ from one another with respect to a set of trip characteristics. For a deeper understanding of city patterns, this chapter is oriented towards a fragmental analytical approach: it is structured such that in each city, differences in patterns are detected at the scale of classified zones in order to identify similarities/dissimilarities transversally between cities. For a cross-study benefit the same classified zones were identified for each city. These zones are: downtown, university area, high-income residential and suburban residential zones. In continuity with previous

chapters, these zone typologies were chosen to reflect contrasting profiles for socio-economic traits that have been identified as criteria for emerging mobility trends. Such traits include millennial population, millennial population growth, active modal shares, etc. The following is a synthesis of the main findings resulting from the detailed analysis of five main trip traits and how they vary spatially and how they can further inform urban planning decisions. These five traits are trip length, trip purpose, movement density, weekend variation and field of movement (internal versus external movements).

Morphological characteristics

Flows analysis OD Relations

5 Metropolitan areas

Census-based boundaries

components of analysis

Zones selection Seattle Seattle

Denver Denver

Los L o s An gelAngeles es

Aust

Fort Collins

Lancaster Boulder Everett

S. Clarita Denver

Oxnard

S. Bernardino Los Angeles

Bellevue Seattle

Kent Tacoma

Long Beach

Anaheim Irvine Temecula

Colorado Springs

Seattle Denver LosAngeles Austin Savannah

3-scale analysis

Cities

Downtown University High-income residential

Zones

Residential suburban

Trip Traits

Trip length Trip purposes Movement density Weekend movement External vs. Internal trips City Dissection

Austin Austin-San Antonio

SavannSavannah ah-Charleston

Waco

Killeen

Temple

Charleston

ardino

Austin

emecula

Hilton Head Island Savannah

S. Marcos New Braunfels S. Antonio

Zone classifications For each city, areas associated with distinctive socio-economic profiles (and corresponding travel patterns) were identified to create a basis for studying movements in and out of -and between- these articulated zones1. The fixed zone typologies identified for each city were the two attractor typologies (downtown and university areas) and two generator typologies (high-income and suburban residential areas).

Seattle

Denver

Los Angeles

Austin

Savannah

Downtown

Zone 09

Zone 25

Zone 24

Zone 19

University zone

Zone 23

Zone 24

Zone 07

Zone 23

Zone 21

High-Inc Residential

Zone 16

Zone 02

Zone 12

Zone 07

Zone 24

Suburban Residential

Zone 18

Zone 21

Zone 16

Zone 19

1 These classified zones correspond to a careful synthesis between census-based boundaries and morphological features. The boundary-setting of each zone took into consideration existing physical barriers (such as highways, natural barriers, etc.) and functional organization differences such that each zone is a functional class of its own (e.g. residential, industrial, mixed-use), practically functioning as a continuous and homogeneous area.

Denver

Los Angeles

Austin

Savannah

Modal share

Income level

Age group

Seattle

Trip trait 1

Trip length

One of the biggest advancements allowed by trip information collected through big data is the affordance of 24-hour tracking. Analyzing trip distance trends throughout the day for a typical weekday can reveal critical information about overall mobility trends and commuting patterns. Our analysis thus concentrates on two dimensions: the spatial (through zone distinctions) and temporal (developments over time). If further investigated, this type of data could supplement critical planning decisions. For example, learning that short trips happen more frequently during lunchtime in downtown areas can prompt city councils to invest in infrastructure for alternative travel modes in order to alleviate the energy and time cost burden placed by car traffic. At the same time, the understanding that trip distances made to and from a city’s university zone is higher than the average for similar zones in other cities can alert planners to the need to increase student housing options in that area.

longer trips are more concentrated in morning and evening hours. The same trend holds for trip times. However, shorter trip times are more concentrated towards the evening segment of the day as opposed to midday. Trips travelled in Los Angeles and Austin tend to be longer in distance on average, while Los Angeles reserves the highest average trip time of the five cities. Savannah, on the other hand, being a much smaller city, maintains the lowest average trip time. A deeper look at internal variances by city zone allows us to distinguish further trends.

Aggregate Mobility Trends

Geographic Variances

Despite contextual variances, it is interesting that trip profiles for each distance category (shortest: 0-1 miles to longest: 10-20 miles) have very similar trends in all cities throughout the day; as we move from short to long trips, the peak point in the graph gradually moves from center to ends. This means that the percentage of short trips are highest in the middle of the day while

A zone-based analysis of average daily travel distances in each city exposes some pattern distinctions. Furthermore, it confirms the recurring phenomenon of longer distances travelled in the morning hours in all areas, whereas the afternoon trip concentration is more widespread. Additionally, the analysis shows that the greatest concentration of

travel time correlations

low

high

trips does not necessarily correspond to trips made to and from downtown areas in all cities. Though we expect that downtown trips are highest in length, since they attract more movements from farther away, the data reveals that this is only the case in the cities of Seattle and Los Angeles. Looking specifically at short trips (those falling in the range of 0-2 miles), we deduce that the highest percentage of short trips occur in the midday hours, i.e. during lunchtime. Spatially, we see that this increase is less accentuated in residential areas

than it is in downtown and university areas. To take an example, short trips in Seattle increase by factors of 2 and 3 in residential areas during lunchtime, whereas in university and downtown zones they multiply by factors of 5 and 7, respectively. In Austin’s university district, almost 50% of trips made during lunchtime are short trips, pointing to a high transformative potential to non-motorized transport. A comparative assessment of short, medium and long trips by zone reveals that downtowns have a higher concentration of short (0-2 miles) and long

% of movements

travel distance 24-hour trend

time of day

% of movements

travel time 24-hour trend

time of day

trips (10 miles and more) than other areas. Midlength movements (2-10 miles) are conversely more pronounced in the residential zones, accounting for more than half of all trips in all five cities.

Demographic Variances

High-Income Residential

University District

Suburban Residential

Downtown

High-Income Residential

University District

Suburban Residential

Downtown

(0-2 mi)

Seattle

Denver

short trips

Los Angeles

Austin

Savannah

trip average

Studying the relationship between typical trip duration and diverse population groups can help us understand the positionality of each group based on their income levels. This also includes looking at where people of different income groups live to understand how urban form affects trip times.

Taking a closer look at the demographic distribution, we find that there are some clear correlations between certain socio-economic profiles of travelers and trip time distribution. Data shows that in most studied cities, longer trip times tend to be associated with larger households, lower-income households, and a high private modal share in these respective areas. In other words, with a few exceptions, trips tend to be longer for travelers from large household sizes, of lower income groups and in cities that rely most highly on private car trips. While the first two elements may be linked to suburbanization and longer trips travelled between the suburbs and the city (high trip time due

to distance), the third trait may indicate car-induced traffic (high trip times due to congestion). Population groups in these categories are particularly timedeprived with respect to their counterparts. Moreover, city data analysis shows us that there is a negative correlation between trip times and younger travelers – particularly part of the millennial age group (20-29 years old) – as well as people living in areas with high population density, and high active modal shares. In line with the ‘Back to Downtown’ mobility trend, data shows that younger people who are more likely to live in the city and not be tied to larger families, are more likely to enjoy lower travel times. At the same time, almost counter-intuitively, travelers living in high-density areas travel faster. This could simply be due to home-work proximity, since jobs are mostly concentrated in dense city centers, long

and the wider offering of various transport modes in busy areas. In the same vein, high-density areas make travelling by active modes such as walking and cycling easier given the proximity of destinations and the higher degree of walkability owing to inner-city planning. Out of the five cities, Los Angeles seems to stand in clear contradiction to the general trend on several accounts. In Los Angeles, travel times tend to increase with all factors, save active modal share with which there is no correlation. It is interesting to note that even younger travelers and high-density populations are more likely to sustain longer travel times. However, due to the polycentric morphology of Los Angeles city, movement patterns are more spatially varied than the other cities under study. With Denver as well, another polycentric city, there is no correlation between population density and travel times.

short

medium

Seattle

Denver

Los Angeles

Austin

Savannah

res-suburb downtown

"shorter and longer movements compared to other zones."

"more than 50% of movements are medium distance."

Trip trait 2

Trip purpose By collecting location-based data from devices such as smart phones and vehicles, transport planners can use this information to create an Origin-Destination map of trips made within a specified area. Data from tracking over long periods of time can be used to infer trip purpose, understood as one of three types: Home-Based Work, Home-Based Other and Non-Home Based.

By looking at real-time movement data for each of the five studied cities, we gain a comprehensive perspective of trip purpose distributions throughout the day that were previously limited to specific time periods and small user samples due to the high resource cost of traditional travel surveying methods. Gathered data from the four typological zones of the 5 cities under study repeatedly show similar distributional trends among university and downtown areas, and similar trends among residential areas. For all areas, we find that Home-Based Work trips

dominate the distribution during the morning hours, roughly between 5am and 8am. However, while HomeBased Other trips are concentrated in the evening hours in downtown and university zones, they are evenly distributed throughout the day in residential areas, with a slight increase in the evening. NonHome-Based trips are highest in the middle of the day for all areas, yet their volume is significantly higher in downtown and university zones. This tells us that trips are far more diversified in downtown and university areas, and thus less predictable from day to day than those taking place in residential areas.

Home-Based Work

Home-Based Other

Non-Home Based

HBW

HBO

NHB

Frequent trips from home and to/from a specific destination

All other trips starting or ending from/to home

Trips made from/to random destinations other than home

Downtown

University district

Suburban residential

High-income residential

Seattle

More specifically, home-based trips (whether for work or other purposes) make up the majority of trips (more than 70%) in evening hours in residential areas, whereas in university and downtown districts, non-home-based trips continue to make up as high as half of all trips. During lunchtime, the percentage of non-home-based trips is even higher in these zones, reaching 6070% of all trips, whereas in residential zones, non-home-based trips remain around the 50% mark. In downtown Austin, 70% of all trips during lunchtime are non-home based, increasing by nearly 20% from the morning segment. Compared to a 15% average increase in all areas, this surge is particularly high, indicating a high concentration of jobs in Austin’s downtown area and a high percentage of worker-commuters. NHB HBO HBW

High-Income residential

Suburban residential

University district

Downtown

NHB

Denver

Seattle

average increase during lunchtime

+14%

+15%

+16%

100%

75%

50%

25%

LUNCH

100%

75%

50%

25%

LUNCH

HBO HBW NHB

LUNCH

Austin

Los Angeles

US Major Cities – Mobility Research Study – February 7th, 2019

100%

75%

50%

25%

LUNCH

US Major Cities – Mobility Research Study – February 7th, 2019

100%

75%

50%

25%

LUNCH

Savannah

US Major Cities – Mobility Research Study – February 7th, 2019 100%

100%

75%

50%

25%

LUNCH

US Major Cities – Mobility Research Study – February 7th, 2019

average NHB during lunchtime

NHB

52%

US Major Cities – Mobility Research Study – February 7th, 2019

NHB

48%

NHB

59%

NHB

67%

Non-Home Based (NHB) trips tend to increase in all areas during midday hours by an average of 15 per cent. These surges correspond to lunchtime movements with marked rises in downtown and university areas of all cities. Learning this information can be particularly useful for urban planners to determine the potential for trip capture using public transportation and alternative modes of travel. At the outset, trip purpose distribution can help urban planners differentiate between regular trips and irregular daily trips, as well as indispensable and dispensable trips. In areas with high irregular trip generation, municipalities can focus on providing diverse modes of transport to fit various single-user

travel needs. This information can be enriched when overlaid with trip length information that supports the idea that short-distance travel is in highest demand during the time in which non-home-based trips reach their peak. Space-saving and emissionsreducing micro-mobile vehicles (such as single-user kick scooters, electric mopeds and bikes) could thus effectively replace car use for these short, multipurpose midday trips.

people moving in downtown Austin, Texas. Image credits: GSPhotography / Shutterstock.com

Trip trait 3

Movement density The weekday movement density, i.e. the intensity of trips created on a typical working day in each zone of a city demonstrates intriguing information about the potential movement generation of that area relative to other areas. In essence, we find that downtown areas generate weekday movement that ranges from 6 to 38 multiples of their respective city averages.

heavy rush hour traffic through the center of Seattle, Washington. Image credits: Ceri Breeze / Shutterstock.com

Weekday movement density in Seattle (all zones)

17x

5.3x 1.6x

0.6x

City average

High-Income residential

Suburban residential

High-Income residential

University district

Downtown

Downtown 6x

17x

1.8x

22x 38x

12x 0.6x Seattle

1.8x Denver

1.2x Los Angeles

zone weekday movement density

Austin

0.8x Savannah

Seattle

city weekday movement density average

For example, a comparison between the four studied areas of Seattle shows us that, compared to the city average, the high-income residential area generates 40% less trips, whereas the typical residential suburb generates 1.6 times that average (60% higher). At the other end, Downtown has a potential trip generation of 17 times the average, whereas the University district density is only 5 times the average. A comparative analysis of high-income residential areas and downtown areas in each of the cities shows an anti-correlation between the densities of the two. A relatively high density of downtown movements in one city is linked to a relatively low density of movements in its high residential district, and vice versa. Among downtown areas, we find that there are major density differences between the studied cities. On the one hand, Los Angeles and Denver’s downtown

Denver

Los Angeles

Austin

Savannah

factoral deviation from city average

densities increase by factors of 6 and 12 compared to the city average, while on the other hand, Austin and Savannah’s downtown densities reach 22 and 38 times the city averages, respectively in these zones. Balanced and imbalanced distributions of density point to fundamental morphological differences between the cities’ urban structures: whereas the downtown area in the second group of cities tends to be overpoweringly the dominant center for jobs and services, the first group of cities have polycentric structures, meaning that jobs and services are more evenly distributed among several areas, besides downtown. This pattern becomes clear when we visualize movement density over a linear spatial representation of the cities of Austin and Los Angeles as examples. In the Austin Daytime Population visual (next page), it is clear that the movement intensifies in a single central area, whereas in the case of Los Angeles, the increase is more widespread across several of the city’s areas. 59

Monocentric and polycentric cities differ in how movement density is distributed across space and time. Monocentric cities tends to be associated with higher travel costs, greater travel distances and higher congestion levels. Austin and Los Angeles are two striking prototypes for the diversity between these two models. Monocentric city Traditional Model

Polycentric city Modern Model

Trip trait 4

Weekend movement The movement density profile on a typical day on the weekend differs drastically from the weekday trend. For all zones under study, the density pattern over the 24-hour period has an inverse shape to the one exhibited on weekdays: on a weekend, the peak movement is during the middle segment of the day in all areas, resulting in an inverted v graph shape, as opposed to the weekday movements trend which highlights morning and evening peaks.

weekday weekend

The weekend downtown downturn The data also shows that the overall movement volume in different zones on a given day on the weekend compared to a weekday is variable across cities depending on the nature and activity of their downtown/university and suburban residential areas.

34%. Even yet, we find that movement in downtown is still significantly higher on the weekend than any of the other zones; more than double that of the university zone in Seattle and about 10 times that of the residential areas on average.

All cities observe a weekend movement reduction in their downtown areas. However, the extent of the reduction is related to the extent to which a specific downtown area hosts residential space alongside office, commercial and entertainment space. In Savannah, for example, downtown contains a high degree of single-family houses, making it hardly distinguishable from other areas of the city. For this, the drop in movements in downtown Savannah only amounts to 7%. In Seattle and Los Angeles, in contrast, a dominant portion of downtown land is used up by commercial and office developments and they both exhibit a weekend movement reduction of

The opposite trend takes place in residential suburbs: while Seattle shows a low decrease of around 2% in weekend movements, Savannah shows a 21% reduction, with all remaining cities falling somewhere in between. This could be linked to a much lower gravitation factor of the downtown area in Savannah for suburban communities compared to the downtown areas of the other cities. That being said, it appears that longer distance movements recorded for residential suburban areas (likely made to areas beyond the city) are higher in weekends in all cities. The distribution of internal and external movements in all areas is the focus of the following section.

Weekend nightlife activity on Austin, Texas' 6th Avenue. Image credits: Ryan J Lane / iStock Photo

re e b

c pla

downtown weekend movements Seattle

Denver

Los Angeles

Austin

Savannah

Seattle

Denver

Los Angeles

Austin

Savannah

suburban weekend movements zone weekend movement reduction city average weekend movement reduction

Trip trait 5

External vs. internal trips From the cross-city data analysis, we could determine that trips made in all areas are predominantly external, ranging from 70-90 per cent of all trips made at any given time of day. Residential areas tend to have a higher share of external movements, while downtown and university zones demonstrate a higher share of internal movements than their residential counterparts.

internal vs. external Denver

Los Angeles

Downtown

University district

Suburban residential

Seattle

External (IN + OUT)

Internal trips

Austin

Internal movements signify any movements made within the same zone; whereas external movements are any trips made between that zone and any other, including inbound and outbound movements. This trend of heightened internal movement in downtown and university zones is particularly visible during lunchtime hours. Internal trips in downtown in particular are concentrated in the lunchtime and evening hours. Conversely, external movements are most highly concentrated in morning and evening hours. As a result, in terms of distribution, lunchtime is the segment in which there is not only the highest concentration of internal trips, but also the highest share of internal trips relative to external ones. The increase of internal trip share during lunchtime in downtown areas is around 5% on average in all cities, ranging from 7% in Los Angeles and Seattle to 3% and 2% in Austin and Savannah, respectively. However, these share increases are relative to the day average. Alternatively, when compared to the morning segment, Austin and Savannah observe the highest rises in internal trip share amounting to 85% and 80% rises respectively, compared to a 65% average. Put simply, internal trips almost double in the downtown areas of

these two cities from morning to lunchtime. In university zones, this relationship increases gradually from the morning hours to lunchtime, whereas in downtown the transition happens sharply around lunchtime hours (roughly counted as 12pm to 3pm). This is likely due to the differences between the nature of work and educational schedules, the latter being more flexible than the former, such that students and university faculty produce a higher number of short internal trips throughout the day. Looking independently at external movements, and differentiating between those made into and out of each zone, it becomes clear that residential areas have more outbound external trips in the morning and more inbound external movements in the evening. The opposite is true for downtown and university areas, which – to use the technical terms – tend to ‘attract’ more trips in the morning, and ‘generate’ more trips in the evening.

external (in vs. out) Denver

Los Angeles

Austin

Savannah

Downtown

Attract in AM/ Generate in PM

University district

Suburban residential

Generate in AM/ Attract in PM

High-income residential

Seattle

External (IN)

External (OUT)

Concluding remarks Zone distinctions and the 'twin-zone' phenomenon Our initial assumption that the four zone typologies focused on have different movement patterns was supported by the data. At a glance, distinctions of movement patterns could be broadly drawn between two groups of ‘twin’ zones: the two residential types (high-income residential and residential suburban) as one group and downtown and university zones as the other. To that end, it became clear through the analysis that a zone’s functional nature is a strong factor determining the type of trip characteristics it exhibits. This was the case for each of the studied trip traits. In some instances, there are differentiated trends even between twin zones. Movement density is one such diversified trait: high-income residential areas have the lowest overall movement density of all areas and suburban residential areas have three times that density. A similar distinction can also be made between densities of the twin group on the higher end of the scale: on average, downtown generates 3 times the average movement of university zones. In any case, the performance of downtown areas is more closely linked to the morphological structure of the city, as discussed earlier in the role of mono- and poly-centricity.

1 2

In other studies, distinction between downtown and university zone performance lends itself more to scheduling factors relating to the main function of each area. For example, internal movement in university zones are amplified more broadly over the morning and afternoon period, whereas in downtown areas the rise is more restricted to the short two/three-hour segment corresponding to office lunch breaks.

Lunchtime patterns Another recurring element emphasized throughout the analysis is distinct lunchtime patterns. The data has shown that lunchtime trips tend to be shorter in distance, though not necessarily in time. Example given, non-home based1 movements are more likely to be internal than any other time of day2.These patterns generally tend to register higher in downtown and university areas, downtown areas in particular. Considering the fact that movement density multiplies by large factors in downtown areas relative to other zones, it becomes clear that a micro-mobility transport offering, which consumes less road space, is more energy-and-cost-efficient and is specifically designed for short, single-user trips, could go a long way towards improving the flow of downtown movement, especially those still heavily reliant on car use. In university zones, this potential capture timeframe is stretched over a longer period of time (from morning to the end of lunchtime).

On average, more than half of all lunchtime trips and more than two thirds of downtown lunchtime trips are non-home-based. Internal trips have an average share of a quarter of all trips during lunchtime.

Image credits: Deva Darshan / pexels.com

Weekend comparison On the weekend, movement is generally lower in all zones of any one city, though the amount of reduction is linked to the functional relationships between the different zones in that city. For example, it is no rule that downtown movements show greater reductions than residential areas. The extent to which movement is reduced on the weekend in downtown areas will depend on the functional distribution of the city and the level of functional interdependence of its various zones. The congruence of trip generating areas (residential) and trip attracting areas (commercial, business or schools) plays a role in these variations. In terms of hourly distribution, weekend movements exhibit the reverse trend of weekdays: on the weekend, movements reach their peak in the afternoon as opposed to morning and evening peaks of weekday movements. In addition to this, longer distance movements tend to be higher for residential areas in weekends as opposed to weekdays in all cities.

Population groups and time poverty The amount of time spent traveling per day per person contributes to their ability to utilize time for other purposes including work, rest and leisure – a measure known as ‘time poverty’. From the data, it was deduced that certain demographic factors have higher correlations with long daily travel times, i.e. a high degree of time poverty. These populations include larger households, lower-income households, and people living in cities with a high share of private travel modes (mainly private cars). Time poverty for these groups is caused by a combination of long-distance travels of suburban communities and traffic congestion. On the other end, younger travelers, and populations living in dense urban areas or in areas with high active travel modes (walking and cycling) are more likely to complete their commutes in shorter times. Naturally, there are exceptions to these rules. The data showed that Los Angeles travelers are more likely to be time-poor across the spectrum. This in part owes to the city’s stratified clustering of jobs and services, as well as its sheer size compared to the other cities under study. 67 67

IIIV Towards a new mobility landscape

The mobility ecosystem is gradually expanding into a more diversified portfolio: the personal and collective mobility spheres are exploring shared, electric, automated, on-demand, microand integrated solutions to fit conflicting needs in a fast urbanizing world. Trends discussed within the book point to the growing adoption of American cities of new alternative mobility options.

The progressive cities framework Within this study, the progressive (versus nonprogressive) cities framework was used as a way to read mobility trends. In progressive cities, which were the focus of this book, a number of co-existing socio-demographic, economic and behavioral conditions were used as parameters feeding into a 'progressiveness index'. These parameters were designed with the aim to highlight cities showing healthy economic growth trends and a significant representative share of progressive behavioral outliers (millennials, in this case). A select number of cities, which performed well on these scales were tested for a number of progressive mobility trends, as signals of a potential model mobility landscape to replicate across the country.

The big takeaways Lessons learnt and the main trends observed

The highest increases in millennial populations across the U.S are concentrated in the State of Texas. Three of the five most attractive cities to millennials are located in Texas, including Austin. Younger generations are not driving far less than others, but they are more multi-modal, in the sense that they walk, cycle and use public transport at far greater levels than older age groups. Across cities, younger groups are at least twice as likely to walk or cycle than older generations and are progressively less likely to use public transport as they get older. Los Angeles is an outlier in the sense that people are more likely to drive (and own cars), including the younger generations, across the spectrum. The potential to break the driving cycle is generally more difficult in this large, sprawling metropolis. A 'Back to Downtown' movement is prevalent among millennials in all of the focus cities, while the Baby Boomers are mostly pouring out to the suburbs save Austin, where a notable increase is recorded in central areas near millennial clusters. There is an overall upward trend in shared mobility development but with large variations between cities, highlighting the newness of these systems, which are still largely in their growth phase, pre-consolidation. Growing even before the pandemic, work from home trends doubled in scale in the first quarter of the 21st century. Prior to the pandemic, the tendency to work from home was higher for older age groups.

Denver, Colorado comes out strong in its share of remote workers among the group, with the highest rate of increase as well. The State of Colorado has the highest share of full-time telecommuters in the country, which makes sense given its concentration of scientific research and high-technology industries. Seattle is the only city among the group with a declining car ownership. It also shows a high rate of decrease in the overall share of private transport modes, coupled with high public transport ridership and active travel uptake. When it comes to trip characteristics, differences between cities fade and differences between zones within cities supersede. The morphology of each city plays a great role in how movement density is distributed between zones at different times of the day, whereas city size plays a much slimmer role in average trip length and trip time differences. Temporal distinctions also play a major role in trip data: clear 24-hour and weekday/weekend patterns emerge across cities, with characteristic differences between zones of different functional and demographic natures.

the featured map represents carpooled transportation trips across the U.S.

75 75

When things took a sharp turn: a final reflection Throughout the book, the goal was to use a diversified data approach to observe growing mobility trends, understand where they prevail (topographic tendencies), when they prevail (chronological tendencies) and who their main proponents are (demographic structure). While the trends may be forever altered after the current moment of disruption has passed, the methodology remains intact. It will take continued observation of location-based data over the next several years to patternize the country's new mobility landscape. Only time (and data) will tell. Nov 2012 2,974,668

Jan 2010 2,952,073

Feb 2020 3,277,753

Dec 2016 3,174,407

This line graph reflects the moving 12-month total vehicle miles travelled trend in the U.S. from January 2010 until August 2020.

Aug 2020 2,934,617

Source: US Federal Highway Administration

77 77

Systematica Established in 1989, Systematica is a transportation planning and mobility engineering consultancy with offices in New York, Milan, Beirut and Mumbai. Systematica operates at multiple scales and provides a wide array of integrated consultancy services in the sectors of transport and urban planning, including national, urban and development scale transport planning, strategic advisory and due diligence for infrastructure investments, traffic analysis and management, mobility engineering in complex buildings and events venues with a special focus on pedestrian flows, parking design, vertical transportation, and application of advanced infomobility systems and technologies. Systematica is committed to its company statement and mission to deliver highly ethical and professional invest in Research and Development for seeking new approaches and solutions for the ever-changing issue of mobility and transport planning; put social inclusion on top priority, and; search for sound engineering solutions to support sustainable growth. www.systematica.net

Transform Transport Transform Transport is a research unit focused on innovative mobility solutions. While mobility and transport related technologies are emerging with increasingly fast paced, Transform Transport explores how they can have positive impacts on our cities, neighborhood and buildings. Founded by Systematica, it grounds on 30 years of experience in the field of transport planning and mobility engineering, investigating the future of Milan and other cities worldwide. www.transformtransport.com

Credits e-mobility U.S. Cities: shifting mobility patterns ISBN: xxx-xx-xxxxxx-x-x ISBN: 978-88-944179-5-1 Authors: xxx Authors: Lamia Abdelfattah, Rawad Choubassi, Jonelle Hanson Team: Team: xxx Marianna Zuretti, Alessandro Vacca, Nicola Ratti, Dante Presicce A special thanks to all collaborators of Systematica Thanks to all collaborators of Systematica who contributed to who contributed to this book. this book. A special thanks to Kevin Hall from Texas A&M Transportation Institute for review.

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