National Academies Press: OpenBook

Shared Mobility and the Transformation of Public Transit (2016)

Chapter: Chapter 1 - Introduction

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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Shared Mobility and the Transformation of Public Transit. Washington, DC: The National Academies Press. doi: 10.17226/23578.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Shared Mobility and the Transformation of Public Transit. Washington, DC: The National Academies Press. doi: 10.17226/23578.
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Page 4
Page 5
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Shared Mobility and the Transformation of Public Transit. Washington, DC: The National Academies Press. doi: 10.17226/23578.
×
Page 5
Page 6
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2016. Shared Mobility and the Transformation of Public Transit. Washington, DC: The National Academies Press. doi: 10.17226/23578.
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3 This study focuses on the intersection of public transit and shared mobility in seven U.S. met- ropolitan regions of varying character whose transit systems represent a range of sizes and maturi- ties. Some regions include lower density, younger cities that have experienced high levels of urban growth in recent decades but have little tradition of public transit use and are primarily dependent on individual autos. At the other end of the spectrum are regions that include older cities with very dense cores and mature, robust, and widely used public transit systems. Basic characteristics of the regions and their transportation systems are listed in Table 1. Appendix A lists the positions of the public-sector and private-sector individuals who were interviewed in the seven regions. Definitions Because shared-use mobility is a relatively new field, the terms for various business models and technologies are still in flux. This study uses terms that might not yet be in common use or widely agreed upon. Table 2 summarizes the definitions used in this report, which generally conform to the definitions set out in TRB Special Report 319 (2016) and Shaheen et al. (2015). Research Overview This study draws on several sources of information, including the following: • Interviews with more than 75 public-sector transportation officials and private operator rep- resentatives from approximately 30 agencies and private companies (listed in Appendix A); • A survey of more than 4,500 shared mobility users (detailed below and in Appendices B and C); • Analysis of transit and ridesourcing comparative travel times (detailed in Appendix D) and capacity and demand (detailed in Appendices E and F); • An assessment of practices and regulations relating to paratransit provision; and • A compilation of current business models and public-private partnerships that build on new technologies from the emerging shared mobility sector. Together, these elements provide a snapshot of a rapidly widening mobility ecosystem at an early moment in its evolution, and form the basis for a number of recommendations for balancing the benefits of innovation with public agencies’ responsibility to the common good. Survey Methodology The user survey was distributed through both private shared-mobility operators and transit agencies in September and October 2015. C H A P T E R 1 Introduction

Region Metro Area Pop. (millions) Core City Pop. (millions) Urbanized Area (sq. mi.) Metro Area Solo Auto Commute %; Avg. Household Vehicle Count Carshare Operators; Vehicle Count; Total Cars per 10K Core Pop. Bikeshare Operators; Bike and Sta…on Count; Total Bikes per 10K Core Pop. Ridesourcing and Microtransit Providers; Launch Year Transit Systems: Total Annual Unlinked Trips (millions); Annual Trips per Capita (p.c.) Ausn-Round Rock, TX 1.78 0.91 523 81.4%; 1.77 Car2Go (one-way), Zipcar (tradional); 381 cars; 4.2/10K Ausn BCycle; 375 bikes, 46 staons; 4.1/10K Ly , Uber; 2014 Capital Metropolitan Transit Authority (CapMetro); 36.4; 26.7 p.c. Boston- Cambridge- Newton, MA-NH 4.60 0.66 1873 72.0%; 1.58 Enterprise, Zipcar (tradional); 1265 cars; 19.46/10K Hubway; 1300 bikes, 139 staons; 19.8/10K Ly , Uber; 2012 Bridj (microtransit); 2014 Mass. Bay Transportaon Authority (MBTA); 395.3; 94.5 p.c. Chicago- Naperville-Elgin, IL-IN-WI 9.49 2.72 2443 74.1%; 1.62 Enterprise, Zipcar (tradional); 790 cars; 2.9/10K Getaround (p2p*) 120 cars; 0.4/10K Divvy; 4760 bikes, 476 staons; 17.5/10K Ly , Uber; 2013 Via (microtransit); 2015 Chicago Transit Authority (CTA); 529.2; 61.5 p.c. NE Ill. Regional Commuter Railroad (Metra); 73.6; 8.6 p.c. Pace Suburban Bus; 35.9; 4.2 p.c. Total: 638.7; 74.2 p.c. Los Angeles-Long Beach-Anaheim, CA 12.95 3.92 1736 77.7%; 1.80 Zipcar (tradi€onal and one-way); 241 cars; 0.6/10K Planned, spring 2016 Ly , Uber; 2013 LA County Metropolitan Transporta‚on Authority (Metro); 476.3; 39.2 p.c. San Francisco- Oakland- Hayward, CA 4.40 0.85 524 65.1%; 1.69 City CarShare, Enterprise, Scoot, Zipcar (tradi€onal); 1315 cars; 15.5/10K Getaround (p2p); 1230 cars; 14.4/10K Bay Area Bikeshare; 700 bikes, 70 sta€ons; 8.2/10K Ly , Uber; 2011 Chariot (microtransit); 2014 San Francisco Municipal Railway (Muni); 223.9; 68.2 p.c. Bay Area Rapid Transit District (BART); 126.5; 38.6 p.c. Total: 350.4; 106.8 SeaŽle-Tacoma- Bellevue, WA 3.50 0.67 1010 73.9%; 1.83 Zipcar (tradi€onal), Car2Go (one-way); 905 cars; 13.5/10K Pronto; 500 bikes, 51 sta€ons; 7.5/10K Ly , Uber; 2013 King County Metro Transit; 123.2; 40.3 p.c. Washington- Arlington- Alexandria, DC- VA-MD-WV 5.76 0.66 1322 69.3%; 1.76 Car2Go (one-way), Enterprise, Zipcar (tradi€onal); 1680 cars; 25.5/10K Getaround (p2p); 105 cars; 1.48/10K Capital Bikeshare; 1538 bikes, 204 sta€ons; 23.3/10K Ly , Uber; 2011 Bridj, Split (microtransit); 2015 Washington Metropolitan Area Transit Authority (WMATA); 413.6; 90.2 *p2p = peer-to-peer. Sources: U.S. Census Bureau American Community Survey 2014, 5-year es€mates (metro and city popula€on, commute mode, household vehicles, occupied housing units); Na€onal Transit Database 2013 profiles (transit system data, service area popula€on; trips per capita uses each transit agency’s service area popula€on); SUMC Shared Mobility Database (shared mobility operators and vehicle counts as of December 2015). Table 1. Summary of study cities’ mobility characteristics.

Introduction 5 Term Meaning Other Names/Treatments Bikesharing Short-term bike rental, usually for individual periods of an hour or less over the course of a membership. (Periods can range from a single ride, to several days, to an annual membership.) Informaon technology (IT)-enabled public bikesharing provides real-me informaon about the locaon and demand for bikes at docking staons throughout a community. Bike sharing Carsharing A service that provides members with access to an automobile for intervals of less than a day. Major carsharing business models include tradional or round-trip, which requires users to borrow and return vehicles at the same locaon; one-way or free-floang, which allows users to pick up a vehicle at one locaon and drop it off at another; and peer-to-peer (p2p), which allows car owners to earn money at mes when they are not using their vehicles by making them available for rental to other carshare members. Car sharing Microtransit IT-enabled private mul-passenger transportaon services, such as Bridj, Chariot, Split, and Via, that serve passengers using dynamically generated routes, and may expect passengers to make their way to and from common pick-up or drop-off points. Vehicles can range from large SUVs to vans to shu†le buses. Because they provide transit-like service but on a smaller, more flexible scale, these new services have been referred to as “microtransit.” Dynamic shu†les, private flexible transit Private shu†les Tradional private shu†le services include corporate, regional, and local shu†les that make limited stops, o‹en only picking up specified riders. Employer shu†les, tech buses Ridesharing At its core, ridesharing involves adding passengers to a private trip in which driver and passengers share a desnaon. Such an arrangement provides addional transportaon opons for riders while allowing drivers to fill otherwise empty seats in their vehicles. Tradional forms of ridesharing include carpooling and vanpooling. This term is somemes used to refer to ridesourcing (see below) but unless otherwise noted that is not the meaning employed in this report. Carpooling, vanpooling, slugging Ridesourcing Ridesourcing providers such as Uber and Ly‹—codified in California law as Transportaon Network Companies (TNCs)—use online pla•orms to connect passengers with drivers and automate reservaons, payments, and customer feedback. Riders can choose from a variety of service classes, including drivers who use personal (non-commercial) vehicles; tradional taxicabs dispatched via the providers’ applicaons (apps); and premium services with professional livery drivers and vehicles. Ridesourcing has become one of the most ubiquitous forms of shared mobility. Transportaon network company (TNC); ridesharing; ride- hailing; e-hailing Ride-spli—ng Dedicated operators, as well as several ridesourcing providers, have launched IT-mediated products that allow customers requesng a ride for one or two passengers to be paired in real me with others traveling along a similar route. Dynamic carpooling Shared-use mobility (SUM), shared modes, SUM operators In general, shared-use mobility comprises intra-urban transportaon services in which vehicles are accessed by mulple users for a variety of trip purposes. This umbrella term includes the forms listed above along with tradional public transit, taxis, and other vehicles for hire. Shared mobility Table 2. Definitions.

6 Shared Mobility and the Transformation of Public Transit The survey sample frame included adult residents of the study regions who have used one or more shared-use modes, including transit. The researchers requested distribution of the survey by transit agencies and shared mobility operators in all of the seven study markets, and also in New York City. The recruitment method was through invitations emailed and distributed via social media by cooperating agencies and operators, inviting customers to complete a web-based survey instrument. A link was directly emailed by distribution partners to more than 75,000 email recipients in addition to a large number of newsletter and social media followers. This email received 4,551 at-least partial responses. Provider-specific links, called collectors, allowed tracking of response sources and permitted deactivation of particular channels at the end of a 2-week open period. The overall count represents a net response rate of 6.0% for the sources the researchers were able to track. Sampling Considerations In each market, the researchers were limited to working with convenience samples—those individuals able to be reached via the partners who agreed to distribute the survey, all of whom were people who had previously supplied their email addresses to the agencies or operators. Given this constraint, it is advisable to be cautious about using the survey sample to make inferences about the wider population of shared mobility and transit users and certainly to the general popu- lation. The survey was administered using an online format and email links. This implies a basic level of technological facility on the part of respondents, and also a willingness to participate in research about transportation. Also, the survey took place in several of the largest, densest, and most expensive cities in the country. These cities were chosen for this study specifically because of their known high levels of shared mobility usage. Thus, the sample is likely over-representative of higher income, more highly educated individuals compared to the general U.S. public. We should also make note of the small sample sizes in some markets relative to others, par- ticularly in Boston and San Francisco. In addition, we might expect some bias related to the mode of the distribution channels for the various surveys. In Los Angeles and San Francisco, the survey was distributed almost exclusively via the transit agencies; in Boston, Chicago, and New York, the survey was distributed solely via bikeshare operators; and in Austin and Seattle, the primary channel was a carsharing operator. One subpopulation this distribution method might miss would be people who use ridesourcing exclusively among shared modes, including transit. The research team did not have a way of estimating the size of this population because so little sys- tematic knowledge currently exists about levels of ridesourcing usage in urban areas and among the traveling population overall. Ongoing research—from other behavioral surveys, public and private operator data, personal travel inventories, and other data sources—is needed to continue building the understanding of the use and effects of ridesourcing and other shared modes. The survey is described in greater detail in Appendix B, and the survey instrument is presented in Appendix C.

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TRB's Transit Cooperative Research Program (TCRP) Report 188: Shared Mobility and the Transformation of Public Transit examines the relationship of public transportation (including paratransit and demand-responsive services) to shared modes, including bikesharing, carsharing, microtransit, and ridesourcing services provided by companies such as Uber and Lyft. The report also explores issues and opportunities and challenges as they relate to technology-enabled mobility services, including suggesting ways that transit can learn from, build upon, and interface with these new modes. A supplemental infographic summarizes the aspects of of the sharing economy and its intersection with transit.

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