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Shared Mobility and the Transformation of Public Transit (2016)

Chapter: Chapter 2 - Findings

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Suggested Citation:"Chapter 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 10
Suggested Citation:"Chapter 2 - Findings." 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 2 - Findings." 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 12
Suggested Citation:"Chapter 2 - Findings." 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 13
Suggested Citation:"Chapter 2 - Findings." 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 14
Suggested Citation:"Chapter 2 - Findings." 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 15
Suggested Citation:"Chapter 2 - Findings." 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 16
Suggested Citation:"Chapter 2 - Findings." 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 17
Suggested Citation:"Chapter 2 - Findings." 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 18
Suggested Citation:"Chapter 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 2 - Findings." 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 32
Suggested Citation:"Chapter 2 - Findings." 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 33
Suggested Citation:"Chapter 2 - Findings." 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 34
Suggested Citation:"Chapter 2 - Findings." 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 35
Suggested Citation:"Chapter 2 - Findings." 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 36
Suggested Citation:"Chapter 2 - Findings." 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 2 - Findings." 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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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

7 Transportation and Lifestyle Choices Associated with Shared Mode and Transit Use Among survey respondents, greater use of shared modes is associated with greater likeli- hood to use transit frequently, own fewer cars, and have reduced transportation spending. Supersharers (people who routinely use several shared modes, such as bikesharing, carsharing, and ridesourcing) report the greatest transportation savings and own half as many cars as people who use transit alone. An online survey of more than 4,500 mobility consumers in the study cities explored travel behaviors and attitudes with a particular focus on the interaction of transit and new shared modes and associated effects on automobile ownership and use. The survey methodology is discussed in Chapter 1 and in Appendix B, and the complete survey instrument is presented in Appendix C. It is important to note that the survey relied on convenience samples of transit and shared mobility users in several large cities, and is not necessarily representative of these popula- tions overall, nor should it be interpreted as establishing causality in the behavior described by respondents. Although this study finds evidence that shared modes appear to discourage auto- mobile ownership and complement transit use overall, and in general focuses on the larger scale lifestyle changes made possible by new mobility options, the evidence also points to possible competitive impacts on transit operations in some specific situations. Additional and ongoing research is needed to more fully understand the net impacts and to track their changing nature over time and in other settings. Urban transportation is evolving rapidly, and even during the brief course of this research new transportation products came to market that may have impacts that were not studied in this work. Responses to the survey suggest that rail and bus transit were the most frequently used shared modes, followed by bikesharing, carsharing, and ridesourcing (Figure 1). Some 10% of respon- dents could be classified as supersharers, having reported using at least three non-transit shared modes (bikesharing, carsharing, and ridesourcing) across some combination of trip purposes (commutes, errands, or recreation) within the last 3 months. The supersharers represent the group of people who take broadest advantage of the range of mobility options available to them. These results were notable. As a group, supersharers are likely to be early adopters, and although the survey respondents were concentrated in urban areas, their behavior can give some insights into how travel choices among the broader population may change as the mobility menu gets larger in more and more cities. Approximately 57% of supersharers said public bus or train was the single shared mode they use most often, followed by bikesharing, ridesourcing, and carsharing (Figure 1). Asked about Rail and bus transit were the most frequently used shared modes, followed by bikesharing, carsharing, and ridesourcing. C H A P T E R 2 Findings

8 Shared Mobility and the Transformation of Public Transit the entire range of mobility options rather than a single top mode, supersharers said they used transit and all of the other shared-use modes with frequency equal to or greater than the gen- eral respondents (Figure 2), and reported driving alone or with friends about 10% less than the overall group. Asked how they would travel if their favored mode was not available, 30% of super-sharers would choose another form of transit (18% bus, 12% train), about one quarter would use one of the other shared-use modes, and another quarter would ride their own bike or walk the whole way, for a 78% total of modes other than personal automobiles (Figure 3). Note: Throughout these figures, data labels are rounded for convenience. Where data labels are equal, variations in column height reflect differences at the decimal level. Source: Cross-tabulated responses to survey ques ons 4 and 9 (see Appendix C). 65% 12% 12% 10% 57% 22% 10% 11% 0% 10% 20% 30% 40% 50% 60% 70% Public bus or train Bikesharing Carsharing Ridesourcing All respondents Supersharers Figure 1. Single shared mode used most often—supersharers versus all respondents. Source: Cross-tabulated responses to survey ques ons 7 and 9 (see Appendix C). 45% 48% 42% 43% 27% 14% 17% 45% 51% 31% 35% 47% 19% 23% 0% 10% 20% 30% 40% 50% 60% Public bus Public train Drive alone Drive w/friend Bikesharing Carsharing Ridesourcing All respondents Supersharers Figure 2. Frequent use (once or more per week) by mode—supersharers versus all respondents.

Findings 9 The picture of how people mix and match various mobility options can be further devel- oped by looking at all of the modes respondents reported having used in the last 3 months (Figure 4). (Given that the question on which Figure 4 is based allows for choosing multiple modes for a given trip purpose, the figures represent the proportion of respondents who had used that mode for that purpose, and do not add to 100% across modes.) Transit forms the backbone of all respondents’ mobility picture, but respondents who reported the heaviest use of shared modes also reported heavier use of transit. For every trip type, a 5% to 10% greater proportion of supersharers reports using transit compared with the overall group. • Commute trips. For their commutes, both groups most frequently cited transit modes; but more than half of supersharers also said they had used bikesharing. • Errands. For running errands, the overall group tends to turn to personal vehicles, distantly followed by transit and shared modes. Supersharers are most likely to use carsharing. Ride- sourcing was the least-used mode for errands in both groups. • Recreational trips. For recreational trips, supersharers report use of every mode (including driving) in proportions greater than the overall group. Especially notable are the very high proportions of supersharers who reported making recreational trips using ridesourcing, bike- sharing, or carsharing—percentages that far outweighed those of the overall group (by more than double in the case of bikesharing). The wider variety of modes used for recreation versus commuting likely reflects the greater variety of destinations for social events, at times and places where transit coverage is not necessarily reliable. Broadly speaking, these responses suggest that the supersharers take advantage of the whole menu of mobility choices, readily switching to the mode that makes the most sense for a given trip and purpose. People who use transit and shared modes reported lower car ownership and less driving, as well as increased physical activity and decreased transportation spending. People who take greater advantage of shared modes report lower household vehicle ownership and decreased spending on transportation. Compared with people who haven’t used any shared modes beyond transit, respondents who are experienced with new forms of shared mobility report owning nearly half a car less—1.5 versus 1.05 vehicles per household (Figure 5). Vehicle ownership is even lower among super- sharers, who report 0.72 cars per household. By comparison, the average ownership rate across the seven study regions is 1.72 vehicles per household. It’s not possible to discern cause and effect People who use transit and shared modes reported lower car ownership and less driving, as well as increased physical activity and decreased transportation spending. Source: Cross-tabulated responses to survey ques ons 5 and 9 (see Appendix C). 18% 9% 5% 8% 13% 7% 8% 20% 6% 2% 18% 12% 9% 8% 17% 7% 10% 12% 3% 1% 0% 5% 10% 15% 20% 25% All respondents Supersharers Pu bli c b us Pu bli c t ra in Bik es ha rin g Ca rsh ar ing Pr iva te bi cy cle Rid es ou rci ng W alk Dr ive al on e Dr ive w /fr ien d W ou ldn ’t g o Figure 3. Alternative if top mode not available—supersharers versus all respondents.

Source: Responses to survey queson 9 (see Appendix C). 56% 60% 39% 49% 51% 62% 54% 60% 34% 44% 54% 64% 27% 52% 25% 49% 31% 75% 24% 30% 32% 54% 40% 66% 21% 31% 16% 30% 54% 85% 35% 28% 56% 48% 50% 51% 25% 21% 56% 51% 71% 74% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% All respondents Supersharers All respondents Supersharers All respondents Supersharers Commung Errands Recreaon Public bus Public train Bikesharing Carsharing Ridesourcing Drive alone Drive w/family/friend Figure 4. Mode use in last 3 months, by trip purpose—supersharers versus all respondents.

Findings 11 in these responses; however, if these ownership differences are indeed attributable to the early- adopting supersharers’ selecting from a larger menu of mobility options, these findings suggest a promising path to vehicle ownership reductions and associated benefits from reduced solo car travel if these lifestyle choices become more broadly dispersed. Increasing the breadth of shared mobility options and broadening access for more neighborhoods and communities could help cities meet goals to reduce single occupancy driving. Across both groups (supersharers and general respondents), lifestyle changes since beginning to use shared modes are notable, with a net movement away from trips by personal automobile and toward greater use of transit. These responses represent qualitative results—the survey did not gather information on the magnitude of behavior or lifestyle changes for individual users. Con- sidering the net difference between users who reported less driving and those who reported more, 31% of general shared mobility users and 33% of supersharers reported driving a car to work less often; 22% and 26%, respectively, drove less for errands and recreation; and 43% and 42% said they used public transit more, versus 28% and 32% who said they used transit less (Figure 6). Breaking the groups out individually, 35% of general shared mobility users reported Source: Cross-tabulated responses to survey ques ons 19, 1, and 9 (see Appendix C). 1.5 1.05 0.72 0 0.5 1 1.5 2 Transit experience only Non-transit shared-mode experience Supersharers Figure 5. Household vehicle ownership, by shared-mode experience. Source: Cross-tabulated responses to survey ques ons 10 and 9 (see Appendix C). 4% 35% 10% 32% 43% 28% 54% 5% 4% 37% 11% 37% 42% 32% 65% 3% 0% 10% 20% 30% 40% 50% 60% 70% Drove a car more to work Drove a car less to work Drove a car more for errands or recrea on Drove a car less for errands or recrea on Used public transit more Used public transit less Became more physically ac ve Became less physically ac ve All respondents Supersharers Figure 6. Lifestyle changes since starting to use shared modes—supersharers versus all respondents.

12 Shared Mobility and the Transformation of Public Transit driving to work less often versus 4% who reported driving more; 37% of supersharers drove a car to work less often, versus 4% who said they drove more. For errands and recreation, 32% of the wider group and 37% of supersharers, respectively, drove less for errands and recreation, versus 10% and 11% who drove more; 43% and 42% said they used public transit more, versus 28% and 32% who said they used transit less. More than half of all respondents and nearly two-thirds of supersharers reported being more physically active since they began using shared modes. Small numbers of respondents in both groups said that they drove more since beginning to use shared modes (about 4% for commuting and 10% for errands). Without knowing more about the individual situations, it’s unclear what the reasons are for such a change, or what the magnitude of the change is. Because errands are the trip type with greater increased driving, it is possible this reflects users who begin turning to carsharing to access destinations they were previously unable to reach or for which they previously used a non- auto mode. Also, some percentage of people will simply move to a location, take a job, or enter a phase of life that requires more driving, despite their desires or previous behavior. When asked about changes to their household and finances since starting to use shared modes, respondents across the board reported shedding vehicles and reducing expenses, though super- sharers reported greater benefits (Figure 7). Among supersharers, 21% reported having post- poned buying a car, 22% had decided not to buy one, and 27% had sold a car without replacing it, while 5% had bought a car. In the overall group, 20% reported postponing a car purchase; 18% reported having decided not to purchase a car, 21% reported having sold a car without replacing it, and 8% reported having acquired a vehicle for personal use. Moreover, among supersharers, 52% reported spending less on transportation, while 22% reported spending more—yielding a net of 30% of supersharers who reported spending less. By comparison, among all respondents, 45% reported spending less on transportation, while 27% reported spending more—yielding a net of 18% who reported spending less. Shared Mode and Transit Usage Patterns Shared modes largely complement public transit, enhancing urban mobility. However, they may compete with transit on some routes and at certain times of day. Ridesourcing services are most frequently used for social trips between 10:00 p.m. and 4:00 a.m., times when transit runs infrequently or is unavailable. Bikesharing plays a peak hour role in augmenting transit systems, while carsharing is mostly used off peak. The car-based shared modes likely substitute more for taxi or automobile trips than for transit trips. Transit is most competitive when it trav- els in its own right of way and provides frequent service. More than half of all respondents and nearly two-thirds of supersharers reported being more physically active since they began using shared modes. Source: Cross-tabulated responses to survey ques ons 11 and 9 (see Appendix C). 20% 18% 21% 8% 27% 45% 21% 22% 27% 5% 22% 52% 0% 10% 20% 30% 40% 50% 60% Postponed buying a car Decided not to buy a car Sold & didn't replace a car Acquired car for private use Spent more on transporta on Spent less on transporta on All respondents Supersharers Figure 7. Household and financial changes since starting to use shared modes— supersharers versus all respondents.

Findings 13 The interviews, survey, and data analysis conducted for this study together suggest that public transit and shared modes complement one another by serving different trip types and making car-free or car-light lifestyles feasible for more people. Different shared modes seem to fill spe- cific niches in the mobility ecosystem, with ridesourcing used most frequently for social trips, late at night, and when alcohol is a factor; carsharing used for errands and off-peak trips to areas without good transit access; and bikesharing used for last-mile connections and acting as a pres- sure valve for crowded transit systems during peak hours. In interviews, transit system officials tended to view new forms of shared mobility as largely complementary to their core mission, though they are carefully watching for signs of whether new, tech-enabled modes will change how riders use transit. Many parties pointed to the complexity surrounding access to a constrained public way (particularly parking spots and curb access) as an area that will increasingly require negotiation and policy attention as shared modes grow. Representatives of cities with robust public transit systems interviewed for the study had the least concern about the impact of new modes on their transit services, and were often already engaged in established relationships with bikesharing and carsharing operators. Transit agencies with more dispersed ridership, fewer fixed guideway routes, or a higher proportion of paratran- sit rides or other expensive operations tended to be the most interested in possibilities for new complementary mobility options and service models. However, some transit agencies expressed concerns regarding the potential impact of ridesourcing on their existing service, and several local regulators addressed tactics by ridesourcing operators—such as commencing operations in a jurisdiction before regulatory authorization was obtained—that they believed made collaboration more politically complicated. Ridesourcing is most commonly used for recreation and social trips, late at night, and often when alcohol is involved. Survey responses suggested that ridesourcing is a common part of the mobility menu for many people. However, it is used far more for socializing than for other kinds of trips. More than half of respondents (54%) indicated that they had used ridesourcing for a recreational or social trip within the last 3 months (Figure 8). Only 21% of respondents said they had used it to commute, and 16% reported using it for shopping or errands. For recreational and social trips, ridesourcing was the single top shared-use mode. Asked about the hours of the day and times of week that they most commonly use various modes, survey respondents cited ridesourcing as the least frequent choice during the morning rush, evening rush, and mid-day, as well as weekdays overall (Figure 9). During the evening and late at night, however, ridesourcing was by far the top choice. The survey findings are bolstered by an analysis of ridesourcing wait time and demand (as reflected in the average surge multiplier applied to base fares) throughout the week and around the clock (Figure 10). In every study city, a clear peak in reported ridesourcing demand is visible at some point between 10:00 p.m. and 4:00 a.m. on weekends, and in the majority of cities this is the time of Ridesourcing is most commonly used for recreation and social trips, late at night, and often when alcohol is involved. Source: Responses to survey queson 9 (see Appendix C). 21% 16% 54% 0% 10% 20% 30% 40% 50% 60% Commute Shopping/errands Recreaon or social events Figure 8. Recent use of ridesourcing, by trip purpose.

Source: Responses to survey queson 14 (see Appendix C). 425 962 590 621 229 463 566 674 326 766 758 213 501 533 690 406 176 756 193 355 517 541 667 395 689 195 244 275 451 936936 405 600 695 708 722 437 815 1590 1006 1187 1413 1079 1038 1195 1018 1200 1001 1484 1881 260 472 716 771 916 510 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Weekdays Weekends Early AM (5am-7am) AM rush (8am-10am) Mid-day (11am-4pm) PM rush (5pm-7pm) Evening (8pm-10pm) Late night (11pm-4am) R e s p o n d e n t c o u n t Public bus Public train Bikesharing Carsharing Ridesourcing Driving alone Driving with family/friend Figure 9. Mode preference by time of day and week.

Findings 15 The x-axis in each chart corresponds to the hours in a 24-hour day (all mes local). The y-axis corresponds to the relevant metrics aggregated across each study region. Sources: Transit data—agency GTFS feeds; ridesourcing data—Uber API. Ridesourcing demand, weekdays Ridesourcing demand, weekends Total scheduled transit capacity, weekdays Total scheduled transit capacity, weekends Figure 10. Scheduled transit capacity (top) and typical ridesourcing demand (bottom) by hour, for weekdays and weekends.

16 Shared Mobility and the Transformation of Public Transit greatest demand overall. It is also the time of the day and week when scheduled transit capacity is at its lowest point and average headways are longest. (See Appendix E for more detail on this analysis.) These findings are further supported by data released by the New York City Taxi and Limousine Commission, which conducted its own summary counts of ridesourcing service levels for several time periods. Data from the New York study was reviewed by the research team for TCRP Proj- ect J-11, Task 21. The New York study is limited to Brooklyn because Manhattan’s transporta- tion picture is unique in North America in so many respects. In that study, these actual passenger counts follow the same patterns, with the highest use of ridesourcing taking place late at night during the weekends, with a smaller peak during the weekday morning rush. Later in this section, an analysis of the travel time by transit and ridesourcing systematically explores the trade-offs that underlie the late-night preference for these services. People turn to ridesourcing when they’re drinking. The survey for TCRP Project J-11, Task 21 contained no questions specifically about alcohol use, but it did inquire into factors influencing transportation choices and allowed for open-ended answers. Unprompted, more than 100 respon- dents volunteered that alcohol consumption was a major consideration in their mode choice for recreational trips, and several named ridesourcing (or a specific ridesourcing provider) as their preferred choice in that case. It is likely that if alcohol use had been among the explicit answer choices, the number would have been higher. Relatively few people use ridesourcing to commute—and those who do, do so occasionally. Some people use ridesourcing to get to and from work at least some of the time. Figure 10 shows clear demand peaks during weekday rush hours, which bears this out. However, ridesourcing did not appear to be a major part of the mobility picture for the majority of commuters who responded to the survey. Among the 21% of respondents who reported using ridesourcing to commute, 38% said that their most recent ride on a bus or a train was today or yesterday, whereas about one-quarter of the group (or about 5% of total respondents) said that they had last used ridesourcing today or yesterday (Figure 11). For trips within the last week, the transit proportion declined to 18%, whereas ridesourcing increased to 37% (Figure 11). Together, these changes suggest that people use ridesourcing Relatively few people use ridesourcing to commute—and those who do, do so occasionally. Source: Cross-tabulated responses to survey ques ons 3 and 9 (see Appendix C). 38% 38% 16% 13% 24% 18% 17% 9% 22% 37% 15% 13% 7% 14% 22% 0% 5% 10% 15% 20% 25% 30% 35% 40% Public bus Public train Bikesharing Carsharing Ridesourcing Today or yesterday Within the last week Within the last month Figure 11. Most recent use of each mode—respondents reporting recent ridesourcing commute.

Findings 17 situationally—and generally not daily—as a mode that fills in gaps or works under specific cir- cumstances rather than as the core mode of their commute. This pattern is similarly reflected in the frequency of use: even among respondents who reported ridesourcing as their top shared mode, only 7% said they use ridesourcing daily, whereas 42% reported using it 1–3 times per month. (See Appendix B, Table B-2 for a full breakout of frequency by mode.) Lifestyle Clusters Among shared modes, bikesharing appears to have a role more like transit, whereas carshar- ing and ridesourcing are used similarly to personal automobiles. In listing alternatives if their preferred shared mode was not available, respondents seem to cluster into two groups: those with “active” transit-centered lifestyles and those with auto-centered lifestyles that feature lower initial levels of transit use (Figure 12). Bikesharing seems to be very much a part of the active transit-centered lifestyle cluster, with 50% of this group reporting that they would ride a bus or train if bikesharing were not avail- able, and another 39% saying they would walk or ride their own bike; only 7% reported that they would drive or use ridesourcing. This result underscores bikesharing’s role as an exten- sion of the transit system—though it could also be seen as evidence of bikesharing diverting some trips from transit, a phenomenon that has been evaluated in several cities by Martin and Shaheen (2014). The responses from carsharing and ridesourcing users suggest that this group is more auto- centered, with about a third of those modes’ top users reporting they would drive alone or with a friend if their preferred mode was not available. Pointing to the level of crossover between modes, 15% of carsharers would use ridesourcing, and 24% of ridesourcers would use carshar- ing. Both carsharers and ridesourcers are lighter transit users: 23% of carsharers and 15% of ridesourcers would ride a bus or train instead. Some 8% of ridesourcing users say they’d use another mode entirely, and all but one of the open-ended responses to this question mentioned using taxicabs. These findings suggest two things: 1. Unlike bikesharing, ridesourcing and carsharing are largely not chosen as substitutes for tran- sit trips, but rather as substitutes for private auto trips or taxi rides; and 2. People who prefer carsharing and ridesourcing are probably more likely to have access to a car, and these shared modes give them a way to leave that car at home more often. Studies by Cervero et al. (2007), Lane (2005), Martin et al. (2010), and others have established that carsharing users are likely to shed personal vehicles, and the results of the TCRP Project J-11 research point to the possibility of a similar effect for ridesourcing. More research is needed to understand the net effects of these substitutions. The table of frequency of use by mode (Table B-2, Appendix B) strengthens the associations observed between these lifestyle clusters. Respondents who named carsharing and ridesourcing as top modes reported frequent driving, both alone and with friends or family, at almost twice the rate of those who named bikesharing, bus, or train as their top mode. Top carsharers and ridesourcers also named bikesharing as their least frequently used mode, with nearly 80% and 70%, respectively, saying they use it less than once a year, or never. Ridesourcing and Transit: Travel Time Trade-offs Transit is more competitive when it travels in a dedicated right of way or is otherwise not subject to traffic congestion.

Source: Cross-tabulated responses to survey ques ons 5 and 4 (see Appendix C). 0% 33% 16% 20% 9% 12% 0% 34% 3% 6% 4% 10% 0% 1% 4% 15% 3% 1% 0% 24% 18% 10% 15% 14% 7%7% 6% 3% 15% 0% 11% 2% 24% 7% 6% 17% 26% 4% 25% 20% 8% 4% 0% 7% 14% 2% 3% 0% 1% 0% 4% 2% 3% 4% 8% 0% 5% 10% 15% 20% 25% 30% 35% 40% Top mode: Public bus Top mode: Public train Top mode: Bikesharing Top mode: Carsharing Top mode: Ridesourcing Public bus Public train Bikesharing Carsharing Private bicycle Ridesourcing Walk Drive alone Drive w/friend Wouldn't go Other Figure 12. Alternative for most frequent shared-mode trip if that service was not available—by top shared mode.

Findings 19 Trip length and speed may be a key concern in decisions about which mode to use, with faster modes increasingly preferable as trips get longer. Although the survey did not ask specifically about the distance of particular trip types, it did ask about typical trip lengths by mode. Among respondents who named ridesourcing as their top mode and who had a ridesourcing commute, 58% reported their most frequent ridesourcing trip was under 5 miles. By contrast, 65% of respondents who named public train as their top mode reported their most frequent train ride was over 5 miles. Asked about the length of their most frequent one-way trips using various modes, respon- dents reported taking the longest trips when driving, averaging about 12 miles both alone or with a friend (Figure 13). For the next-longest trips, the mode most frequently reported was public train, at 9.6 miles. Carsharing and ridesourcing were used for somewhat shorter trips (8.5 and 6.6 miles, respectively), but still for longer trips than the typical bus ride of approximately 5 miles. Bikesharing was used for the shortest trips, at just over 3 miles. To create a broad picture of the time trade-offs for various trips, and to reveal areas where transit has a particular advantage or disadvantage, the researchers systematically queried a Google-based trip-planning tool to produce a grid of the comparative travel times for transit and for ridesourcing at points across the whole of each study region. Times were cal- culated for trips from a single origin point located in the highest employment census block group in the region’s core county to every other point in the region, along a half-mile grid. Figures 14 and 15 show the ratios between travel times by scheduled transit and travel times by ridesourcing for Chicago, IL, and Austin, TX, including typical wait times and traffic for both modes. As noted, the comparative travel time analyses for transit and for ridesourcing were per- formed with all trips originating in the census block group with each region’s highest job count. A more comprehensive analysis would create comparisons for a number of origin points— including residential areas, nightlife districts, and large commercial nodes—beyond the business district that is generally the focus of regional transit. This wider analysis was beyond the scope of the present study because of the time required to compile queries and generate the maps; however, the analytical approach shown in this report is readily adaptable to such comparisons. Additional research is being conducted under TCRP Project J-11, Task 25, which will include further analysis along these lines. Trip length and speed may be a key concern in decisions about which mode to use. Source: Responses to survey queson 8 (see Appendix C). 5.0 9.6 3.2 8.5 6.6 11.8 12.1 0 2 4 6 8 10 12 14 A vg . t ri p le ng th , m ile s Pu bli c b us Pu bli c t ra in Bik es ha rin g Ca rsh ar ing Rid es ou rci ng Dr ive al on e Dr ive w /fr ien d Figure 13. Average trip length, by mode.

20 Shared Mobility and the Transformation of Public Transit A comparison of the travel times for the same trip using transit or ridesourcing underscores the rational basis for the usage patterns suggested by the survey and demand analysis. For many trips, transit is a much faster choice at rush hour, especially along fixed-guideway corridors. Alternatively, the trips may be close enough in duration that the significant difference in cost would make a ridesourcing trip prohibitively expensive for daily rides, though using ridesourc- ing might make sense situationally. But when traffic congestion is less of a factor, and transit headways are much longer, transit’s time advantage is much more contained. In addition, some areas or corridors might have unusually long transit times because of the need for multiple trans- fers even when traveling a relatively short distance. These areas might be places where specific transit improvements, such as new express service or the implementation of bus rapid transit, could have a disproportionate impact for riders. Figure 14 shows travel time ratios for 5:00 p.m. and midnight in the Chicago study region. The ratios depict estimated transit travel time to estimated driving time (in typical traffic, plus mean TNC wait time for the departure hour and region) from a single origin to each of a 0.5 mile grid of core-county destinations. A ratio below 1.0 (shown in the figure using green points) means that transit is the faster choice for that particular journey; ratios between 1.0 and 1.5 (shown Sources: Google Maps Distance Matrix API (transit and driving me esmates), Uber API (TNC wait me), U.S. Census Bureau TIGER/Line (geography). Core county Cook Cty grid-0.5 mi Central city Origin point 1 10 3 5 mi.2 4 Transit:TNC me rao 2.00 or more Transit:TNC me rao 1.50-2.00 Transit:TNC me rao 1.00-1.50 Transit:TNC me rao 0.50-1.00 Transit:TNC me rao 0.00-0.50 Figure 14. Chicago, IL, region travel time ratios.

Findings 21 Sources: Google Maps Distance Matrix API (transit and driving me esmates), Uber API (TNC wait me), U.S. Census Bureau TIGER/Line (geography). Figure 15. Austin, TX, travel time ratios. using yellow points) are essentially a wash in terms of time, where it seems likely that cost would play a greater role in the choice; and ratios above 1.5 (shown as orange and red points) mean that ridesourcing is clearly the faster choice. No ratio is calculated for destination points for which no scheduled transit route is available from the origin. (Areas with no available scheduled transit routes appear in the figure as black points on gray.) For example, a peak-hour trip that takes 20 minutes by transit and 40 minutes by ridesourcing would have a ratio of 0.5 (20 / 40 = 0.5). A trip that takes 40 minutes on transit and 20 minutes by ridesourcing would have a ratio of 2.0 (40 / 20 = 2.0). Appendix D of this report provides details on the methodology of this approach and maps of all seven regions. In Chicago, the region’s strong transit service (much of which travels in dedicated rights-of- way) combines with significant traffic congestion to create a map showing large swathes where transit has the advantage or is roughly equivalent to ridesourcing for peak-hour trips from the central business district (Figure 14, left map). Especially along CTA and Metra lines, transit’s time advantage can stretch far beyond the city limits in specific corridors. Conversely, several suburban Cook County areas that lie between the region’s radial transit lines show a time advan- tage for ridesourcing, even with typical peak-hour congestion taken into account. However,

22 Shared Mobility and the Transformation of Public Transit these points are so far removed from the downtown origin that most people making that trip regularly would far more likely be driving themselves. In these areas, last-mile shared mobility efforts might be fruitful. Many outlying areas (20 miles or farther from the Loop business dis- trict) simply have no coverage by scheduled transit, and are so far from regional transit lines that driving is currently the most logical choice for trips downtown. At midnight, however, the picture shifts considerably (Figure 14, right map). At this time only a limited area close to downtown remains more quickly served by transit. In much more of the city, along with the entirety of suburban Cook County, the time advantage of ridesourcing is considerable. For late-shift workers or people returning from a night out, the choice would likely come down to the ability to access or afford the ridesourcing trip. As the demand and survey data show, this is a time when many people decide to pull out their mobile phones. The picture is different in regions that have grown around the private automobile and have made less investment in transit that can move past traffic. Figure 15 shows travel time ratios for 5:00 p.m. and 12:00 a.m. (midnight) in the Austin, TX, study region. Again, the ratios depict estimated transit travel time to estimated driving time in typical traffic and using the same distances. As in Figure 14, a ratio below 1.0 (shown in green) means that transit is the faster choice for that particular journey. In Austin, dedicated-guideway transit is a small portion of the transit system and buses are in mixed traffic and congestion for much of the day. Here, transit is the faster way out of the cen- tral business district for fewer destinations. Moreover, for much of the core city and the greater part of the region, no transit routes are available from downtown. Congestion’s role in limiting transit accessibility in Austin is underscored by the expansion of transit’s time advantage at midnight on a few central-city corridors (Figure 15, right map). This expansion points to the potential for added transit in a dedicated right-of-way to improve the mode’s competitiveness, especially in the congested corridors that are covering more of the region. Overall, however, for the vast majority of Austin destinations, ridesourcing is currently the faster of the two modes for travelers departing from downtown, regardless of the time of day. Equity in an Expanding Mobility Marketplace Because shared modes are expected to continue growing in significance, public entities are encouraged to identify opportunities to engage with them to ensure that benefits are widely and equitably shared. Transit agencies can improve urban mobility for the entire spectrum of users through collaboration and public-private partnerships, including greater integration of service, information, and payment methods. Everyone can benefit from a transportation system that provides more mobility options through seamless transfers, integrated fare payment methods, and improved information. How- ever, such a system is only possible if public-sector entities make a concerted effort to ensure that collaboration with private mobility providers results in services that work for people of all ages, incomes, and mobility needs. Potential for Partnerships and Collaboration to Expand Mobility Access Many public-sector representatives interviewed for this study (see Appendix A) said they look forward to increased collaboration with the private sector as the shared mobility industry continues to grow and evolve. For instance: • Several transportation agencies already partner with new shared mobility providers. The earliest collaborations were with vanpooling, carsharing, and bikesharing providers, but

Findings 23 partnerships increasingly include ridesourcing companies and experiments with microtransit and other forms of dynamic demand response. • Regulation of ridesourcing providers remains a contentious process. At the same time, transit agencies recognize ridesourcing as part of the new urban fabric and an opportunity to extend and expand the use of transit, such as through increased first- and/or last-mile connections. • Transit agencies are happy to let private providers lead in developing customer-facing technologies, and are widely committed to providing the open data that helps make this possible. Most partnerships between ridesourcing providers and transit agencies are still in the very early stages, however, so at this point little empirical record exists on which to assess their impact or value. Some existing forms of partnership and collaboration are outlined in the section on business models at the end of this chapter. In reconciling collaborative opportunities with their mandates to serve the public interest, transit agencies and other public entities can recognize their roles as conveners and gatekeepers to the public way. The same institutional heft that makes transit agencies attractive partners for the private sector also allows them to set the terms of agreement to ensure all users have equitable access to information resources, streamlined payment options, and improved, integrated mobil- ity services. Keeping Service Innovations Fair and Accessible Because it is a precondition to using many shared mobility services, access to information technology, and smartphones in particular, has been pointed to as a barrier to widespread adop- tion of new shared modes, especially among people with lower incomes, elderly people, and those who are less comfortable using new technology. The survey found some differences among the particular tools preferred by various groups of respondents for accessing information about transit and other mobility options. Responses also indicated that transit information technologies are widely used across income and experience levels. (Because the survey was administered online, these results reflect a bias toward users who have some level of familiarity with the Internet.) A comparison of respondents with only transit experience to those who have used new shared modes shows that both groups are broadly similar in their familiarity with transit-related infor- mation technologies (Figure 16). The most notable difference is in the provider of the tools—the transit-only group was much more likely to use transit agency-provided applications (apps) or websites, as opposed to the third-party tools preferred by respondents who have used other shared modes. Looking at differences across income levels, the survey found little difference in overall access (Figure 17). Levels of experience were nearly level (at about 70%) across income groups when it came to using transit agency-provided apps or websites to view schedules, whereas use of third- party tools increased with income. Even among respondents in the lowest income group, about 50% reported having used third- party informational apps, compared with about 70% among the income groups with the highest usage. The difference in adoption rates of transit agency-provided tools versus third-party tools points to the ongoing value of transit agency investment in customer-facing technologies, especially for users who might not have the most current mobile devices. Given that many shared-use services involve using a proprietary mobile app, it follows that use of third-party tools would grow with shared-mode usage in general. Taken together, Public-private partnerships increasingly include ridesourcing companies and experiments with microtransit and other forms of dynamic demand response. The difference in adoption rates of transit agency- provided tools versus third-party tools points to the ongoing value of transit agency investment in customer-facing technologies, especially for users who might not have the most current mobile devices.

Source: Cross-tabulated responses to survey ques ons 15 and 1 (see Appendix C). 77% 23% 55% 36% 53% 29% 61% 24% 35% 17% 43% 4% 35% 4% 39% 24% 21% 22% 57% 43% 31% 59% 35% 51% 43% 44% 31% 33% 37% 5% 27% 3% 25% 43% 12% 38% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool See schedules Map my route Find closest stop/sta on Find out me ll next arrival Track transit vehicles in real me Reload farecard or account Pay a fare Learn new transit routes Learn new non- transit routes Transit experience only Shared-mode experience Figure 16. Experience with transit apps and information services, by shared mode experience.

Source: Cross-tabulated responses to survey ques ons 15 and 22 (see Appendix C). Data labels show ranges of responses. 0% 10% 20% 30% 40% 50% 60% 70% 80% Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool Agency tool 3d party tool See schedules Map my route Find closest stop/sta on Find out me un l next arrival Track transit vehicles in real me Reload farecard or account Pay a fare Learn new transit routes Learn new non- transit routes <$25K $25K-50K $50K-75K $75K-100K $100K-125K $125K+ 70%–73% 35%–49% 40%–51% 51%–69% 39%–54% 46%–60% 49%–63% 42%–52% 35%–42% 32%–40% 44%–49% 4%–8% 29%–47% 3%–6% 27%–43% 42%–51% 12%–25% 38%–46% Figure 17. Experience with transit applications and information services, by income level.

26 Shared Mobility and the Transformation of Public Transit these findings suggest that increasing access to shared-use mobility (SUM) has the potential to improve the transportation picture for people with the fewest options—improving connections to transit and access to the region as a whole. Lack of information remains a significant barrier, but lack of access to technology is decreasing over time. Equity Implications and Other Complexities of Fare and Service Integration Across the country, transit agencies are working to migrate to new electronic fare payment systems. The integration of fare payment and service information is central to innovations in public transit, the emerging mobility models, and the trend toward mobile app-based payment in general. Even if these innovations involve no changes to the actual fare structure, many transit agencies will need to assess the impact of these changes on minority and low-income customers as part of their obligations under Title VI of the Civil Rights Act of 1964. Based on lessons from the Title VI equity analyses performed during recent fare media transi- tions by the Chicago Transit Authority and Portland’s TriMet, transit agencies will need to main- tain the ability for unbanked customers to purchase fares using cash or other means that do not require a bank account or credit card (Chicago Transit Authority 2013; TriMet 2016). Moreover, transit agencies will need to assess whether proposed changes unduly burden disadvantaged com- munities in several other dimensions, including: • New non-fare fee structures; • Fare loading levels; • Changes to the mix of retail outlets for fares and fare media, including purchases by mail; • Access for persons with limited English proficiency; and • Registration requirements. Because they have fewer Title VI reporting requirements, demand-responsive services have more flexibility to change and experiment with new fare structures. As a result, this is an area where many innovations are likely to be initially located. The flipside of this flexibility is that res- ervations and fare payment for demand-responsive service that is adjacent to a fixed-route tran- sit system (such as a microtransit or ridesourcing provider feeding a larger fixed-route system) might have to remain on a separate payment and reservation platform pending the main transit system’s Title VI-compliant adoption of fare changes. So, while this flexibility can help encour- age innovative models for fare payment, customer interaction, or actual delivery of mobility services in the demand-responsive services, full fare integration will always be subject to Title VI obligations when it is rolled out to the entire fixed-route system. Unrelated to Title VI but still central to the discussion of fare integration is the issue of federal transit benefit programs under which pre-tax money can be used for payment of transit fares and certain other forms of commuter transportation. Under present Internal Revenue Service rules, pre-tax dollars cannot be used for carsharing, taxis, ridesourcing, or bikesharing. Thus, any cross-modal fare integration requires the ability (a) to discriminate between modes’ benefit eligibility and (b) to pull from separate payment purses accordingly. Differing Use Patterns Across Incomes Public transit is the mode of choice for every income level. Although the survey revealed differences in how households access the transportation system depending on their income, all households reported one thing in common: Transit was by far the top shared-use mode at every income level (Figure 18). The lowest income riders are most likely to take the bus, whereas riders are increasingly likely to use the train as income level rises. In part, this mode choice may Increasing access to shared-use mobility (SUM) has the potential to improve the transportation picture for people with the fewest options. Public transit is the mode of choice for every income level.

Source: Cross-tabulated responses to survey ques ons 4 and 22 (see Appendix C). 55% 19% 8% 10% 7% 47% 23% 9% 12% 7% 36% 33% 8% 12% 10% 27% 39% 15% 12% 8% 29% 37% 12% 13% 8% 21% 42% 14% 12% 10% 0% 10% 20% 30% 40% 50% 60% Public bus Public train Bikesharing Carsharing Ridesourcing <$25K $25K-50K $50K-75K $75K-100K $100K-125K $125K+ Figure 18. Top shared-use mode, by income level.

28 Shared Mobility and the Transformation of Public Transit reflect differences in the geographic availability of bus and train services, especially in the regions studied. Among non-transit shared modes, carsharing is evenly popular across income levels, whereas bikesharing becomes more popular at higher household income levels. Similar patterns emerge when looking at frequent use (at least weekly) across all modes, split by income level (Figure 19). Bus ridership falls by half as income increases, whereas solo driving roughly doubles between the lowest and highest income levels. Carsharing is used more fre- quently at the lower end of the income scale, whereas the opposite is true of bikesharing. Lower income households have much to gain from wider availability of shared-use modes and from carsharing in particular. Shared-use modes expand options for lower income households. As noted before, the option to drive rises with income. Moreover, at three times the rate of every other cohort, the lowest income group reported that if their top mode was not available, they simply wouldn’t go (Figure 20). Among non-transit shared modes, carsharing was reported as the top alternative mode for low- to moderate-income respondents, with its use decreasing at higher incomes. These data underscore the role that carsharing can play in helping people access destinations more easily reachable by car while avoiding the costs of full-time car ownership. Public-Private Collaborations to Improve Paratransit Public-sector agencies and private mobility operators are eager to collaborate to improve paratransit using emerging approaches and technology. Although regulatory and institutional hurdles complicate partnerships in this area, technology and business models from the shared mobility industry can help lower costs, increase service availability, and improve rider experience. Paratransit and other community transportation services (which often take the form of sub- sidized door-to-door trips in wheelchair-accessible shuttles and taxis) play a vital role in serv- ing older adults and persons whose disabilities prevent them from readily accessing traditional public transit. These services are highly regulated and expensive to operate, and both demand and costs are rising steeply. A recent FTA study found that between 1999 and 2012, the annual number of ADA paratransit trips increased from 68 million to 106 million, while the average cost increased from $14 to $33 per trip—a cost increase of 138%, compared with an increase in the unit cost of fixed-route bus service of 82% over the same period (FTA 2014). Representatives from transit agencies and private operators who were interviewed for this study expressed a strong interest in finding ways to harness emerging shared-use business mod- els and technologies to increase mobility, lower costs, and improve the rider experience asso- ciated with paratransit and related services. (Agencies interviewed are listed in Appendix A.) Slowing the growth of costs could have a major impact on transit agencies’ operational spending. Several transit agency representatives noted the lack of clear federal guidance addressing some of the emerging partnership models, particularly about the degree to which public agencies’ regulatory obligations extend to private partners. Future research could explore areas where clearer federal guidance is needed. The technologies and business models of the new shared-use modes will likely find applica- bility to paratransit in two main ways. First, individual technologies developed for new shared mobility services can be folded into existing paratransit operations as part of the ongoing techni- cal evolution of the sector. Some applicable methods and technologies include: • Interactive reservation, confirmation, schedule adjustment, and cancellation systems; • Dynamic dispatch and routing of vehicles; Lower income households have much to gain from wider availability of shared-use modes and from carsharing in particular. Technology and business models from the shared mobility industry have the potential to lower costs, increase service availability, and improve rider experience.

Source: Cross-tabulated responses to survey ques ons 7 and 22 (see Appendix C). 72% 46% 22% 18% 13% 24% 34% 60% 38% 22% 19% 15% 32% 33% 54% 47% 21% 15% 19% 40% 39% 45% 50% 30% 14% 16% 39% 41% 41% 49% 26% 12% 14% 44% 43% 34% 52% 35% 13% 17% 46% 49% 0% 10% 20% 30% 40% 50% 60% 70% 80% Public bus Public train Bikesharing Carsharing Ridesourcing Drive alone Drive w/friend <$25K $25K-50K $50K-75K $75K-100K $100K-125K $125K+ Figure 19. Frequent use (once a week or more), by income level.

Source: Cross-tabulated responses to survey ques ons 5 and 22 (see Appendix C). 21% 10% 14% 19% 10% 16% 12% 19% 10% 11% 13% 18% 20% 10% 13% 19% 14% 11% 13% 22% 16% 13% 24% 9% 3% 7% 8% 9% 8% 6% 9% 4% 9% 9% 6% 2% 4% 7% 7% 6% 2% 9% 4% 5% 9% 7% 1% 6% 9% 6% 7% 6% 2% 9% 8% 6% 6% 8% 5% 1% 0% 5% 10% 15% 20% 25% 30% Public bus Public train Bikesharing Carsharing Private bike Ridesourcing Walk Drive alone Drive w/friend Wouldn't go <$25K $25K-50K $50K-75K $75K-100K $100K-125K $125K+ Figure 20. Alternative if top shared mode not available, by income level.

Findings 31 • Route combination for riders with similar origins/destinations; • Mobile app-based payment integrated into reservation systems; • Ability to track vehicle arrival and share trip details, location, and estimated arrival time with caregivers or other third parties; and • Real-time customer feedback. The second, and perhaps more revolutionary, application would be the direct provision of transportation services to persons with disabilities by ridesourcing or microtransit operators. Engaging such services might seem like an extension of traditional taxi subsidies or dial-a-ride forms of demand-responsive transportation, but fundamental differences in the underlying business models of traditional and shared-mode transportation options make it a more compli- cated step. At the same time, such an arrangement offers the possibility for greater change if the business questions can be resolved. Complexities of Direct Paratransit Provision by Ridesourcing Companies Much of the complexity regarding current ridesourcing business models as they relate to public transportation springs from the nature of drivers’ relationships with the ridesourcing companies (i.e., whether the drivers are employees or independent contractors). This question is currently being litigated in several jurisdictions. As long as drivers are considered independent contractors who can be provided with incentives but cannot be subject to employment condi- tions, several hurdles make it difficult for ridesourcing companies to begin providing contracted paratransit services using federal monies. Those hurdles include: • FTA-required drug and alcohol testing. Such testing applies to any party contracted to pro- vide transportation services for a public transit agency (Nelson\Nygaard Consulting Associ- ates et al. 2007). Testing is required for operators, dispatchers, and maintenance personnel for transit agencies or contractors receiving funding under Sections 5307, 5309, and 5311, the major public transportation funding programs, including taxi companies in a contractor (rather than vendor/voucher) relationship (49 CFR Part 655 final rule effective June 25, 2013). Section 5310 organizations (which provide services specifically for the elderly and people with disabilities) are exempt from the testing requirements only if they do not provide any services for an agency funded under the other programs. • Liability and occupational safety relating to transfers and loading/unloading of non- ambulatory riders. Potential exists for injury to both drivers and passengers if drivers are not properly trained to help people with impaired mobility to load, unload, and secure their wheelchairs. • Provision of door-to-door (versus curb-to-curb) service, which is determined by indi- vidual agency policy. Even if the general practice is to provide only curb-to-curb service, however, a driver must “provide assistance to those passengers who need assistance beyond the curb in order to use the service unless such assistance would result in a fun- damental alteration or direct threat” (FTA 2015). Although providers may ask passengers to request assistance in advance, the driver must provide such assistance as would actually allow the passenger to use the transportation to get from the origin to destination, even if the policy is curb-to-curb service and if the passenger fails to request assistance. Any private contractor being used to provide paratransit service would need to follow these rules. • Requirements for accepting accessible rides and for accommodating wheelchairs or service animals. Ridesourcing companies have had inconsistent results in this area, although it is of increasing interest to some companies.

32 Shared Mobility and the Transformation of Public Transit • Heightened vehicle safety and inspection requirements and insurance costs associated with ADA provision and the transportation of fragile individuals. These requirements and costs go beyond the already-identified questions about the applicability of non-commercial insurance in a ridesourcing provision. Even if the employment question is resolved, other considerations remain if ridesourcing or microtransit companies move into direct paratransit provision. Such considerations include the following: • Fleet-level accessibility requirements. Unlike fixed-route transit fleets, which must be 100% accessible, demand-responsive transit service can be delivered with a fleet that offers a mix of accessibility levels, as long as the level of access provided to riders with disabilities is equiva- lent to the level of service it provides to riders without disabilities (49 CFR 37.77[b]). FTA guidance states that a mix that includes inaccessible vehicles may be used for provision of complementary paratransit “as long as accessible vehicles are dispatched to riders who need them”(FTA 2015). • Fleet ownership prohibitions. In some jurisdictions, questions of fleet-level accessibility may be moot—most notably, throughout the state of California, where TNCs are by definition prohibited from owning vehicles or fleets used in their operations (California Public Utility Commission, Rulemaking 12-12-011, 2013). In these situations, accessible vehicles would have to be provided by drivers under incentives from the companies (leased vehicles are permissible under the rules), or through partnerships with other providers who can own accessible fleets. • Buy America provisions. Most federally funded rolling stock procurements above $100,000 are subject to the requirement that vehicles and components be substantially manufactured and assembled in the United States. Some flexibility exists in the application of these requirements and waivers are available, but the auditing requirements can add significantly to the unit cost of the kinds of smaller vehicles used for paratransit or other demand-responsive services (Macek et al. 2007). The clearest and quickest way to address the first set of hurdles is for existing paratransit providers to consider licensing portions of these new ridesourcing technologies and deploying them within existing structures. Pilot initiatives along these lines could begin immediately. In the longer term, public agencies may work toward reforming or creating new classes of regula- tion for emerging business models in order to encourage greater innovation from the private sector to help improve paratransit provision. Building on the Innovations of Shared-Use Modes for Paratransit A close reading of the regulations and a review of the policies and practices of paratransit sys- tems across the country suggests a number of applications for emerging shared-use models and associated technologies in serving ADA rides. Public transit agencies can build on the innova- tions of shared-use modes that include: • Bringing reservation systems into the 21st century. The paratransit sector is ripe for change in the area of reservations. In 2014, FTA found that less than 15% of paratransit systems used voice-interactive or web-based applications for reservations, with electronic fare collection similarly slow to be taken up (FTA 2014). Telephone reservations will always need to remain available for reasons of accessibility, but considerable staff costs could be saved by the wider use of electronic customer interfaces. Several transit agencies, including Capital Metro in Austin, have opened mobile app- or web-based reservation systems for customers who can use those options, while preserving their live telephone reservation systems. • Using concierge services. In several cities, shared mobility providers are piloting services that act as a human front-end to an electronic service interface for customers who want to

Findings 33 access these services but either don’t have a smartphone or can’t use the default interface. Because it ultimately delivers the request to a ridesourcing provider, this arrangement is at present outside the realm of paratransit; however, paratransit providers that move to dynamic reservation systems could use this option. Together with automated scheduling and rapid improvements in routing software (which are being quickly taken up by paratransit agencies), concierge services could reduce reservation staff requirements. • Providing same-day paratransit rides. Paratransit provision is governed by rules requiring advance reservations, with reservations accepted up to 1 day in advance of the requested ride. These requirements result in a customer experience marked by inflexibility and foreclose the possibility of spontaneous choices. However, FTA guidelines and rules do not prohibit paratransit providers from offering a same-day “premium” service. Because a premium ser- vice is not governed by the usual rules regarding complementary paratransit (which include restrictions on service areas, fares, and permissibility of limiting riders based on purpose), a premium service can offer greater scheduling flexibility. Offering a premium service does not remove the paratransit provider’s obligation to make available regular ADA paratransit ser- vice that complies with regulatory requirements. Several paratransit agencies already provide premium services to ADA-eligible passengers. • Making greater use of feeder paratransit. Feeder paratransit service offers rides to and from transit, rather than door-to-door service. At present this service is used fairly infrequently, likely because of the expense to transit agencies and the additional trip time caused by transfers. More efficient linkages arising from the opportunities and innovations available with shared modes could make feeder paratransit a more practical format and enable riders to make more efficient use of existing transit infrastructure. Private-Sector Providers Can Improve ADA Services New technology-enabled services for passengers with disabilities are not yet being widely provided in the context of paratransit, but such services could offer many paratransit customers greater flexibility and better customer service. Private mobility providers can further enhance their ability to serve passengers with diverse needs by taking steps such as: • Expanding niche services. Service models are beginning to emerge that recognize the diverse needs of passengers with disabilities, and the higher standards required of the drivers who work with them. Services like SilverRide (which focuses on older adults who either prefer not to drive or can no longer drive) hire and train drivers to accommodate the specific needs of their customers. For example, drivers receive training in first aid, safe lifting and trans- fers, and improved communication. Companies like HopSkipDrive and Kango (which offer families ridesourcing for their children) also provide extra training, background checks, and even outside certification of drivers. These companies show how the shared mobility industry is creating new models to accommodate the specific needs and vulnerabilities of various populations. Although these niche services could potentially be bolstered by federal guidance, the role of such services in relation to formal paratransit that makes use of federal funds is still evolving. • Providing incentives to drivers for taking accessible rides and using accessible vehicles. Many of the most innovative features of new shared-use modes, and ridesourcing in particular, are based on the idea of using incentives to produce desired outcomes. To better serve riders with disabilities, companies could provide a way to request drivers willing to accommodate specific needs, and offer incentives for drivers to provide the needed services. Such a system could work best if there are clear state or local regulations that encourage companies to provide univer- sally accessible service, particularly in situations where they receive public monies—which could prompt operators to absorb or underwrite the additional expense to drivers of leasing/ purchasing and maintaining accessible vehicles.

34 Shared Mobility and the Transformation of Public Transit • Making accessible interfaces standard. Riders might not necessarily want to use paratransit, but in many places it is the only option available to people who can’t drive themselves. By making accessible interfaces available (i.e., interfaces that can easily be used with a screen- reader and don’t require dropping a pin or dragging a map), shared mobility providers could make their services useful for a wider range of customers. Emerging Mobility Business Models and Partnerships Emerging business models include new forms of public-private partnership for provision of mobility and related information services. Public entities, including transit agencies and local transportation departments, are already engaging with private operators and using new technologies from the shared mobility world. This section describes several efforts that can pro- vide examples for agencies interested in beginning to collaborate or incorporate new approaches into their operations. Cross-Modal Trip Planning, Reservation, and Payment App Integration Cross-modal trip-planning, reservation, and payment application integration can include integration of fare media, trip-planning technology, and physical integration of modes. Such public-private partnerships are important because they can help increase ease of use and trans- fers between disparate modes. Several public entities have taken the lead in establishing partner- ships to integrate existing transportation services with private mobility providers. • Buffalo Niagara Medical Campus GO BNMC Program, Buffalo, NY. Especially notable because the effort is led by a regional institution and large employer rather than a transporta- tion agency, the GO BNMC initiative connects campus employees to alternative transporta- tion options, linking access to carsharing, bikesharing, shuttles, secure bicycle and car parking, and other transportation services through campus IDs that act as contactless smart cards. The campus has received some funding from the New York State Energy Research and Develop- ment Authority to launch a second pilot focused on the creation of an integrated mobility hub at which employees and residents can access and make connections between an array of transportation services at a single location (Marlette 2015; BNMC, Inc. 2013). • Go LA multimodal trip planning mobile app. The City of Los Angeles partnered with Xerox to launch the Go LA wayfinding app in January 2016. The app aggregates every available mode of transportation—including transit, carsharing, ridesourcing, private bike, and eventually bikesharing—for a given route and calculates the time, cost, and carbon footprint for each option. As the system learns about its users’ individual travel preferences, it will eventually recommend and highlight personalized commuting options. Future updates to Go LA will also include a single payment system that lets users pay for multiple transportation options through the application (Korosek 2016). • TriMet-GlobeSherpa Partnership, Portland, OR. Portland’s TriMet transit system announced in March 2016 that riders will soon be able to hail a Lyft ride or reserve a car2go vehicle using its new mobile ticketing application, developed by payment solutions provider GlobeSherpa (now moovel North America). The multimodal integration will be powered by RideTap, a software tool that lets apps integrate with shared use systems and other transportation options (Njus 2016). • Twin Cities HOURCAR/Metro Transit multimodal integration. In 2015, the Twin Cities carsharing company HOURCAR upgraded its vehicle technology to recognize the chips in Metro Transit Go-to cards. This technology allows members to swipe a registered transit pass to lock and unlock the doors of any HOURCAR vehicle (HOURCAR 2016). Several public entities have taken the lead in establishing partnerships to integrate existing transportation services with private mobility providers.

Findings 35 • Ventra Mobile App, Chicago, IL. Transit users across the Chicago area can access and pay for rides with the region’s three transit agencies—the Chicago Transit Authority (CTA), Metra, and Pace—from their smartphones using the Ventra mobile app (Metra 2014). Riders on Metra can use their phones to display their train passes. Microtransit/Dynamic Demand-Response These models extend the reach of fixed-route transit into lower density areas with dispersed ridership, provide service in core areas outside of peak travel times, or augment fixed-route tran- sit in corridors that are operating at or beyond capacity. Using dynamically dispatched multi- passenger vehicles such as vans, shuttles, or buses, the services optimize routes and stops by balancing multiple customer requests on the fly. Rather than providing door-to-door service, these models may use a service zone with origin and destination points determined to serve the mix of customer requests at a given moment. The services listed in this section generally connect to fixed-route public transit services at one end of the trip. Some of these services are public- private partnerships and others are operated directly by the transit agencies using emerging reservation and routing technologies. • Denver Regional Transportation District (RTD) Call-n-Ride Program. Denver RTD’s Call-n-Ride program provides dynamic shuttle service within 20 service zones in lower den- sity areas of the metro area and focuses on connecting riders with bus routes, rail stations, and Park-n-Ride sites. The system builds routes from phone- or web-based reservations that can be made 2 hours to 2 weeks in advance, and the cost to riders is the same as a local fare elsewhere in the RTD system. Some Call-n-Ride service areas also offer flex-route service dur- ing morning and evening rush hours, which provides reservation-free rides from designated stops within the service area (RTD-Denver 2016). • Kansas City Area Transportation Authority (KCATA)/Bridj pilot program. In March 2016, Kansas City’s transit agency launched a 1-year pilot program that uses the transit agency’s drivers and KCATA-branded vehicles to operate a microtransit system built on the Bridj technology plat- form. The pilot is based in two zones around downtown Kansas City, MO, and the University of Kansas Medical Center district (KU Med Center) just across the state line in Kansas City, KS. The latter zone is a large suburban campus that is poorly served by public transit and experiences high peak-hour congestion. During weekday rush hours, riders can use the Bridj mobile app to request rides within and between the two zones for a $1.50 fare (the same as a KCATA bus ride but paid through the Bridj app), and the platform matches groups of riders and dynamically generates routes based on common origins and destinations (KCATA 2016; Hawkins 2016). • Milton, Ontario, GO Connect pilot program. Piloted between May 2015 and April 2016 in suburban Toronto, this demand-response pilot addressed passenger connectivity challenges between the regional commuter rail system (GO Trains) and a smaller suburban system through an application-based system. The service operated during the weekday morning and evening peak periods, connecting customers to and from their preferred GO Trains. Shuttles operate on optimized routes, based on reservation requests through the RideCo software platform, which dynamically adjusts routes and pick-up/drop-off locations to maximize operational efficiency and minimize real-time travel delays (Town of Milton News 2015). • Santa Clara Valley Transportation Authority (VTA) FLEX pilot. In January 2016, the VTA launched an on-demand, dynamic transit program that provides connection service between regular transit stops and high density employment centers and retail centers within a 3.25-square-mile section of Santa Clara County. After customers request and pay for rides via a smartphone application, the system creates optimal shuttle routes among a network of stops (VTA 2016). • West Salem Connector. Salem-Keizer (Oregon) Transit’s Cherriot bus system began pilot- ing a zone-based dynamic shuttle system in June 2015. The service provides dynamically

36 Shared Mobility and the Transformation of Public Transit routed trips among a network of 26 set stops in the suburban area of West Salem, OR, with the aim of connecting dispersed riders to the transit agency’s scheduled routes (Salem-Keiser Transit 2015). The system, based on the DemandTrans reservation/scheduling platform, takes web and phone reservations between 30 minutes and 2 weeks before a given ride to generate routes and stops, and uses the same fare system as the transit agency’s regular ser- vice. Using the transit agency’s vehicles and drivers, the system is being tested as a replace- ment for several low-ridership routes, with the transit agency instead focusing on providing greater frequency along key corridors, with the new service acting as a feeder based on actual demand (Southward 2016). Private Access to Public Rights-of-Way Use of the public right-of-way by private operators—for parking, passenger loading/unloading, deliveries, or travel in limited-access zones such as dedicated transit lanes—can lead to conflict, especially in busy areas with many competing demands for curb access or parking. However, it can also lead to opportunity. Some cities have taken a proactive approach to managing private use of street parking spots, transit stops, and other common areas, and used these new pilots and ordinances to generate revenue, encourage compliance, and ensure that mobility compa- nies help to serve the public interest. Several jurisdictions have tied these policies to broader objectives, such as ensuring equity in the geographic distribution of services and addressing climate goals. • DC Carshare Street Space Ordinance. Beginning in 2011, the District of Columbia Depart- ment of Transportation (DDOT) established a program to allow one-way carsharing mem- bers to park shared vehicles in residential permit parking zones throughout the city. The ordinance requires carsharing providers to maintain an area of operation that includes the entire District of Columbia and to keep at least one percent of its fleet available in each ward of the city at all times. Additionally, DDOT’s ordinance requires that a set number of carsharing vehicles be located in low-income neighborhoods as identified by DDOT, even if such loca- tions are not desired or requested by the company (DDOT 2016). • San Francisco Municipal Transportation Agency (SFMTA) Commuter Shuttle Pilot. The SFMTA launched an 18-month Commuter Shuttle Pilot Program in August 2014, with the goal of minimizing the negative impacts of the region’s many private commuter shuttles while offering a framework to manage their operation. Central to the pilot was the creation of a limited network of shared Muni and commuter shuttle stops throughout the city. To use the network, shuttle service providers had to apply for a permit and pay a fee of $3.67 per stop-event (as of Fiscal Year 2016). Meanwhile, the program was enforced by the SFMTA to ensure that shuttle operators followed all rules and regulations outlined in the pilot (SFMTA 2016). • Seattle Carsharing Ordinance. Flowing in part from local climate action goals, this 2015 ordinance realigns curb-access priorities while significantly raising the cap on “free-floating” (one-way) carsharing in the city, allowing carsharing companies to eventually operate a total of 3,000 one-way vehicles and tying this total to full coverage of the city. At the same time, the law seeks to replace revenue lost to carsharing’s use of metered parking spaces by both one- way and round-trip vehicles. The ordinance sets annual permit fees for free-floating vehicles ($1,730 per vehicle, which includes access to both metered and residential permit spaces, with annual adjustment for actual use of metered spaces) and for dedicated on-street spaces for round-trip carsharing ($3,000 per metered space and $300 per non-metered space). The program initially caps each one-way operator at 500 vehicles, with a requirement that each establish within 2 years a service area covering the entire city, at which time they may operate up to 750 vehicles (Seattle Municipal Ordinance 124689, 2015).

Findings 37 Service Links and Hand-offs Several transit agencies have begun working directly with ridesourcing companies and other private providers to link their services or to promote hand-offs through targeted marketing agreements. These arrangements—which include first- and last-mile partnerships, linked mobile apps, and guaranteed ride home programs—can help facilitate the creation of a robust, inter- connected network of mobility options that supports car-free and car-light living. • Dallas Area Rapid Transit/TNC Partnerships. In April 2015, DART announced a partner- ship with Uber that would allow transit riders to connect with Uber through DART’s GoPass? mobile ticketing application. DART customers are able to “walk through” the agency’s appli- cation to access Uber’s app. To encourage people to try the new combination, Uber offered a free first ride (up to $20) to new customers. In October 2015, DART announced a similar partnership with Lyft (DART 2015a; DART 2015b). • King County Metro and Redmond Real-Time Rideshare and Emergency Ride Home Pro- grams (Seattle and Redmond, WA). King County Metro and the City of Redmond have partnered with iCarpool, a mobile ridesharing app, to facilitate carpooling among local com- muters. Drivers and riders are able to share the cost of the trip via an automated in-app pay- ment system. Drivers post their trip on the application up to an hour before leaving, and riders searching for a trip along the same route or to the same destination will be matched with the driver. Through the service’s Emergency Ride Home program, carpool users who can’t get a ride home through the iCarpool application are also eligible to receive up to eight free rides with Uber or Lyft per year. The system is designed to allow users to take part in the program with the confidence that they’ll be able to get home if an unusual situation were to arise, such as a child care emergency or an unplanned late night at work (City of Redmond 2016). • Metropolitan Atlanta Rapid Transit Authority/Uber Partnership. In July 2015, MARTA launched a Last Mile Campaign in partnership with Uber. Through the partnership, users who are new to Uber can sign up with a promotional code for a first free trip, up to a $20 value (Uber 2015; Irvin 2015). • Pinellas Suncoast Transit Authority-Taxi/TNC Partnership. Through the Direct Connect program announced in February 2016, the PSTA will pay half the cost of an Uber or local taxi ride (up to $3) to or from transit to help provide first- and last-mile connections in the region. Designed to facilitate use of local bus service, the program allows riders to use Uber (in Pinellas Park, FL) or United Taxi (in Pinellas Park and East Lake, FL) to travel within a specific geographic zone to or from a series of designated stops. From there, riders can connect with the regular PSTA public transit bus system. On the return trip, transit riders can use Uber or United Taxi to travel from the designated stop back home or to work (PSTA 2016). Arrangements such as first- and last-mile partnerships, linked mobile apps, and guaranteed ride home programs can help facilitate the creation of a robust, interconnected network of mobility options that supports car-free and car-light living.

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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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