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Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles (2018)

Chapter: Chapter 4 Transportation Network Company Rider Characteristics

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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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Suggested Citation:"Chapter 4 Transportation Network Company Rider Characteristics." National Academies of Sciences, Engineering, and Medicine. 2018. Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles. Washington, DC: The National Academies Press. doi: 10.17226/24996.
×
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21 This chapter presents findings on TNC rider characteristics based on the results of recent surveys conducted by the researchers; separate surveys administered by several public transit agencies; and analysis of the TNC trip data described in the previous two sections. Surveys on TNC Awareness and Usage Shared Mobility Survey The first phase of this study, TCRP Research Report 188, included an online survey of more than 4,500 mobility consumers in seven study cities administered in late 2015, with the survey distributed by shared mobility operators and public transit agencies to users of their services. For the present phase of the research, transit agencies in Chicago and Nashville also dis- tributed the same survey instrument, resulting in more than 5,800 new responses. Together these two additional survey efforts more than double the total number of survey respondents, and the inclusion of Nashville adds a midsized market with a lower level of transit usage. This survey (referred to as the Shared Mobility Survey) included questions about awareness and usage of shared modes, including TNCs, along with a variety of questions on respondents’ household characteristics and use of various transportation modes. The methodology for the survey administration is included in Appendix D and the survey instrument is included in Appendix E. Four Agency Survey The researchers were also provided access to results of transit rider surveys administered in 2015 by four large urban transit agencies: • Metropolitan Atlanta Rapid Transit Authority (MARTA) in Atlanta, • New Jersey Transit (NJ Transit) in the state of New Jersey, • Bay Area Rapid Transit (BART) in the San Francisco Bay Area, and • Washington Metropolitan Area Transit Authority (WMATA) in Washington, D.C. These four transit agencies, as part of an informal working group, developed and included a substantially identical set of questions about TNC awareness and usage in their customer satisfaction and marketing surveys. The authors of this study had no role in the development of the Four Agency Survey methodology or survey instrument, nor in its administration, nor the compilation of its results. However, this report represents the first time the results from the C H A P T E R 4 Transportation Network Company Rider Characteristics

22 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles Four Agency Survey have been communicated in context with one another or with another survey, and the resulting insights add to the understanding of shared mobility usage and behavior. Findings In addition to the two surveys described above, key rider-related findings from the analysis of the five regions’ TNC trip data are also presented to provide additional context. Zip codes with the most TNC use are younger, higher income, more densely populated, and have fewer personal vehicles and more non- SOV commuters. Some basic insights into TNC riders can be derived from the demographic characteristics of the areas of highest TNC usage, on the assumption that one side or the other of many trips includes TNC users’ homes. Demographic indicators for ZCTAs comprising each region’s 10 highest volume flows are shown in Table 9, with data points greater than the municipal average shown in bold. The municipal averages themselves are the top lines of each market’s group. (Because of single-ZCTA flows and concentration of TNC trips to and from a few ZCTAs, there are fewer than 10 total ZCTAs in each market.) Median Household Income Median Age Race: White, Black, Hispanic, Other (%) Persons/ sq. mi. Mean Household Size Mean Vehicle per Household Commute Mode: SOV, Transit, Walk/Bike Education: BA+ (%) Chicago $50,702 34.2 32/31/29/8 11,944 2.5 1.10 50/28/9 36.6 60614 $92,714 30.1 80/4/7/9 21,925 2.0 0.95 35/43/9 83.0 60657 $65,121 30.9 82/3/6/9 30,414 1.9 0.85 30/49/7 80.4 60647 $56,257 31.4 39/6/51/4 21,818 2.0 1.12 47/32/8 44.1 60613 $72,126 31.9 75/6/10/9 22,712 1.9 0.89 35/46/7 74.2 60622 $75,163 31.3 59/8/26/6 22,511 2.3 1.08 41/34/11 63.0 60611 $87,280 38.4 71/4/6/19 31,165 1.5 0.70 28/22/36 83.1 Washington, D.C. $50,187 33.8 36/47/11/6 10,994 2.2 0.88 34/36/18 56.8 20001 $85,976 30.6 35/46/10/9 20,026 2.1 0.76 26/35/30 60.9 20002 $74,303 34.0 37/52/6/5 10,929 2.3 0.90 34/36/18 54.3 20009 $94,213 32.3 60/17/14/8 39,178 1.9 0.64 19/43/27 78.4 20007 $119,267 33.2 78/5/8/9 8878 2.1 1.14 39/23/19 87.5 LA $52,024 35.0 28/9/49/13 8475 2.9 1.56 70/10/5 32.6 90024 $57,202 23.0 53/2/11/34 17,270 2.2 1.38 50/4/31 71.8 90028 $27,436 33.2 51/6/33/10 19,931 1.8 0.99 54/17/15 41.6 90046 $58,585 38.9 74/4/12/11 8930 1.8 1.33 70/4/5 60.3 90069 $78,979 42.8 72/4/14/10 9523 1.6 1.36 75/2/5 64.5 90026 $47,993 34.7 23/3/55/19 16,222 2.7 1.39 61/17/4 35.6 90095 N/A N/A N/A N/A N/A N/A N/A N/A Nashville $51,393 34.1 56/27/10/6 1375 2.4 1.66 79/3/2 37.9 37203 $33,072 29.6 49/37/5/9 2877 1.9 1.15 69/4/8 47.9 37206 $43,782 33.0 60/32/4/3 3408 2.4 1.49 74/6/2 39.4 37212 $54,140 24.3 73/14/3/9 7258 2.1 1.54 61/3/19 71.0 37214 $52,743 38.1 75/14/6/6 1177 2.2 1.69 87/1/1 36.5 37201 $58,508 32.2 54/38/6/3 6118 1.3 1.21 75/3/11 42.9 Seattle $80,349 35.5 66/7/6/21 8164 2.1 1.38 49/21/15 62.1 98105 $49,647 23.3 65/2/5/27 11,034 2.4 1.29 37/24/24 73.4 98122 $60,563 31.4 63/13/7/18 15,149 2.0 1.09 36/24/28 62.4 98101 $51,159 41.5 69/5/6/15 21,747 1.4 0.62 25/18/45 55.9 98109 $85,957 33.4 74/3/5/17 12,229 1.7 1.14 42/21/24 71.5 98104 $32,568 37.6 44/15/8/33 17,666 1.5 0.50 19/30/42 38.0 Note: Municipal-level igures are italic, ZCTA-level igures greater than the municipal average are in bold. Source: ACS 2015 5-year data. Table 9. Demographics indicators in ZCTAs comprising top 10 flows per market.

Transportation Network Company Rider Characteristics 23 The zip codes with the highest levels of TNC activity in the five regions studied tend to share several characteristics. Compared to the cities in which they are located, most of these zip codes have: • Higher household income levels (Seattle is the exception, with only one ZCTA higher than average); • More young residents and more white residents (again, Seattle is the exception, where more than half of the ZCTAs have greater-than-average proportions of non-white or Hispanic residents); all five study regions, however, have some high TNC ZCTAs with more black or Hispanic residents than the city average, including several that are majority black or Hispanic; • Greater population density (often 2 to 4 times that of the municipal average, even in the lowest density cities) and smaller households; • Fewer vehicles per household, fewer solo driving commuters, and more transit and walking or biking commuters; and • Higher average education levels, with greater proportions of residents who have at least a bachelor’s degree (Perhaps related to this, at least five of the ZCTAs include college campuses, and one in Los Angeles is a ZCTA that makes up a non-residential portion of the UCLA campus, and thus has no associated demographic population.) All the top zip codes except two in Los Angeles, both associated with the UCLA campus, are among those supplied as home zip codes by respondents to the Shared Mobility Survey, and together these responses represent some 14.5% of the total responses received. To the degree that these demographic factors can be discerned in the survey responses (respondents’ race or education levels, for instance, were not requested), these general characteristics tend to be evident among TNC users. Regional variations in the Shared Mobility Survey point to the availability of transit on TNC use Survey respondents varied greatly region by region in their choice of top shared mode (Figure 9). Nashville stood out from other cities in several key respects. A large proportion of respondents cited TNCs as their top shared mode—the only market where a non-transit mode came out on top. This is likely a reflection of the lower level of transit use in the Nashville region. In regions with an extensive commuter or heavy rail network, trains were the top shared mode. 0% 10% 20% 30% 40% 50% 60% 70% 80% Chicago Los Angeles Nashville Seattle San Francisco Washington D.C. Train Bus Bikesharing Carsharing TNCs Figure 9. Top shared mode, by region. Source: Shared Mobility Survey.10 10Shared Mobility Survey Question 4 cross-tabulated with Question 16.

24 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Chicago Los Angeles Nashville Seattle San Francisco Washington D.C. 0 1 2 3 4+ Figure 10. Reported number of household vehicles, by region. Source: Shared Mobility Survey.11 Figure 11. Top shared mode by age group. Source: Shared Mobility Survey.12 0% 10% 20% 30% 40% 50% 60% 70% 80% Bus Train Ridesourcing Bikesharing Carsharing No shared mode use 18-24 25-34 35-44 45-54 55-64 65-74 75+ (Figure 9 does not include respondents from several other metro areas who did take part in the survey, including Austin, Boston, and New York City.) Nashville respondents also reported more household cars, including both the smallest proportion of zero-car households and the largest proportion of two and three-car households (Figure 10). In Chicago, Seattle, and Washington, D.C., 65% or more of the respondents reported having zero or one-car households. (The relatively high car ownership results from respondents in San Francisco can likely be explained by the low number of respondents from that region, since census data suggests that car ownership in San Francisco should be on par with Washington, D.C., and Chicago.) Breaking out all respondents by age group and stated top shared mode (Figure 11), the largest cohort of TNC users were in the 25 to 34 age range, the cohort with the highest level of experience with shared modes overall. 11Shared Mobility Survey Question 16 cross-tabulated with Question 19. 12Shared Mobility Survey Question 4 cross-tabulated with Question 22.

Transportation Network Company Rider Characteristics 25 People who use transit or drive alone do so as part of a routine, while TNCs are used more occasionally. Several points in both the Shared Mobility Survey and the Four Agency Survey suggest that TNCs are being used more as a “gap filling” transportation option and less as a mode for daily commuting or other frequent trips. First, while occasional use of TNCs is fairly widespread, frequent use is less so than with other modes. One way to illuminate this is by comparing the proportion of frequent users to all users of a mode (Figure 12). Compared to other modes, and transit particularly, a much smaller pro- portion of recent TNC users (measured as people who have used it in the last three months)13 report that they are frequent TNC users (i.e., report use once or more per week). Nearly four in five respondents who had used transit within the last three months also reported using transit once or more per week. For solo driving, this proportion was around two in three, and for bike- sharing about one in two. But only one in four recent TNC users reported a frequency of use of weekly or greater. Frequent TNC use is less common than frequent transit use or driving. Among respondents overall, fewer than 10% reported using TNCs weekly or more (Figure 13), a proportion in line with other non-transit shared modes such as bikesharing and carsharing, while frequent bus, train, solo driving, and driving with friends or family were each reported by 25% to 30% of respondents. Frequent use of TNCs in combination with frequent use of any other mode, including solo driving, was comparatively rare, making up 5% or less of respondents for all combinations of modes. The Four Agency Survey had similar findings about recent and frequent TNC use. All the transit agencies except NJ Transit asked about the frequency of TNC use within the last week (Table 10). The overall level of recent TNC usage was low, with roughly three-quarters of respondents not having used one recently. Almost all those who had used a TNC recently had used it once or twice in the previous week, with percentages in the low single digits having used it more often. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% TNCs Transit (bus + train) Drive alone Bikeshare Figure 12. Ratio of frequent mode use (weekly or more) to use within the last three months. Source: Shared Mobility Survey. 13Shared Mobility Survey Question 7 cross-tabulated with Question 9.

26 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles Travel and wait time were top concerns of those who replaced transit trips with TNC trips. The Four Agency Survey requested details on respondents’ most recent TNC trip in the transit agencies’ regions and divided these into three classes of trips depending on their orientation to the transit agencies’ public transit services (see Table 11): • TNC trips used to get to or from public transit (i.e., connecting or first-mile/last-mile trips); • TNC trips taken in lieu of public transit (i.e., trips that could have been made entirely on the administering transit agencies’ services but were not); and • TNC trips where transit was not an option either because of a lack of transit route between origin and destination, or because the trip was outside of transit service hours. As this question was worded, “transit” refers only to trips on the administering agencies’ services and not to trips exclusively on other transit agencies’ services within the same region (e.g., in the Bay Area, only trips that could have been taken on BART are considered “instead of transit,” and a BART-connecting TNC trip that might have been taken on a Muni bus was considered only as “connecting to transit”). The survey did not directly inquire about or count trips exclusively taking place with other transit agencies in a region. WMATA stands out from the other transit agencies for several reasons, most noticeably for its low proportion of TNC trips connecting to transit and for the high proportion of respondents whose last TNC trip was taken instead of transit. BART, on the other hand, had the highest 0% 5% 10% 15% 20% 25% 30% 35% Figure 13. Frequent use (weekly or more) of modes and combinations. Source: Shared Mobility Survey.14 Frequency (number of days) BART MARTA WMATA None 83% 86% 71% 1–2 14% 10% 25% 3–4 3% 3% 3% 5 0% 2% 1% 6–7 0% 2% 0% Note: No data for NJ Transit. Source: Four Agency Survey. Table 10. TNC use within the last week. 14Shared Mobility Survey, Question 7.

Transportation Network Company Rider Characteristics 27 proportion of connecting TNC trips and the lowest proportion in lieu of BART trips, although these do not necessarily imply a net positive impact on the system (see footnote in Table 11). MARTA and NJ Transit had largely similar breakdowns to one another, with lower levels of TNC usage overall, relatively low proportions of connecting trips, and about the same propor- tions substituting for or unable to use transit for their TNC trip. • Faster travel time and less wait time were overwhelmingly cited as the top reasons for choosing a TNC on the most recent ride. The proportions were higher among those who substituted TNCs for transit, ranging from 57% to 87%, compared with 40% to 61% for those who connected to transit. • Reliability was also cited as a major concern for transit riders who substituted TNCs for trips on WMATA; reliability was much less frequently cited in the other regions (7% on MARTA and 17% on BART; NJ Transit did not report on this question). Surveys also found that weekends and evenings are busiest for TNCs. The Four Agency Survey’s findings about travel day and time support the findings from both the Shared Mobility Survey and the TNC trip data. • Saturday appears to be the most common single day for TNC travel, ranging from 20% to 32% of total TNC trips across the week and entailing increasingly larger proportions in the movement from TNC trips connecting to transit (12% to 19%) through substituting trips (17% to 29%) to its greatest height in TNC trips where no transit was available (29% to 44%). Because the Four Agency Survey summaries did not break out responses by specific day beyond weekday and the two weekend days, it’s unclear how use varies over the course of the week. • Weekdays see a proportionally smaller amount of TNC use. Taken together, weekdays were the most common part of the week for TNC travel across the four survey regions but only because the five weekdays together make up a larger proportion of the week. Assuming, however, that each day of the week has an equal proportion of trips—that is, that demand does not vary by day—each day would be expected to have about 14% of the week’s trips, or across five days a cumulative percentage of 71%. Any percentage under 71% means that BART15 MARTA NJ Transit WMATA TNC connecting to transit 16% 6% 8% 3% TNC instead of transit 11% 16% 17% 39% Transit not an option (reason) 32% (26% hour, 6% route) 16% (8% hour, 8% route) 19% (no data for reason) 13% (4% hour, 9% route) Haven’t used TNC in region 41% 62% 56% 45% Source: Four Agency Survey. Table 11. Reason for most recent TNC trip versus transit trips. 15A subsequent reexamination of the survey data by BART found that the fact that TNC “connecting to transit” trips exceed TNC “instead of transit” trips does not imply a net positive impact on BART. Of the 16% of trips that were connecting, 17% (or 2.7% of all respondents) constituted a clearly positive impact on BART (i.e., they either would not have happened or were diverted from drive-alone trips) while the balance substituted access by TNCs for access by other modes (in order of frequency, primarily taxi, walk, or bus). In comparison, among the 11% of TNC trips taken “instead of” BART, less than half (or 5.3% of all respondents) actually said they would have taken BART if TNCs did not exist. The balance would mostly have taken taxis or buses. Together, the proportion of surveyed trips that added to BART use (2.7%) versus the proportion that decreased it (5.3%) showed a small, net negative impact on BART, not including impacts on other public transit in the region.

28 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles the five weekdays together represent less ridership than would be expected. On average, the weekdays comprised 61% to 69% of weekly TNC trips. The exception to this pattern was transit-connecting trips, which happened on weekdays 75% to 86% of the time. • Evening and late-night trips are most prevalent. The single busiest time across the Four Agency Survey results, except for MARTA riders, was 7 p.m. to midnight. In Atlanta, most trips happen during p.m. or a.m. peaks, respectively, most notably on trips connecting to transit. TNC use by respondents is associated with lower vehicle ownership and SOV trips, while impacts on other modes vary. In the Shared Mobility Survey, frequent TNC and transit users reported an average of less than one household vehicle (Figure 14),16 compared with an average of 1.2 cars across all respondents. For frequent solo drivers, the vehicle figure is nearly double (1.6) and respondents who frequently drive with family or friends report nearly as many (1.5). Lower than average car ownership is associated with both frequent transit and TNC use, and those who combine these modes report owning the fewest cars. In the Shared Mobility Survey, frequent TNC and transit users reported an average of less than one household vehicle (Figure 14),17 compared with an average of 1.2 cars across all respondents. For frequent solo drivers, the vehicle figure is nearly double (1.6) and respondents who frequently drive with family or friends report nearly as many (1.5). The lowest car ownership levels were found among respondents who frequently combine two or more non-personal auto modes, with the lowest ownership observed among frequent users of buses and TNCs and frequent users of buses, trains, and TNCs, with 0.72 and 0.69 average vehicles, respectively. The presence of transit in these combinations seems to be key to the lower vehicle ownership: those who both drove alone frequently and used TNCs frequently owned almost as many cars (1.4) as the drive-alone group overall. These combination groups represent small proportions of the overall responses, each with less than 5% of the total. 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 Figure 14. Average household vehicles, by frequently used mode (weekly+). Source: Shared Mobility Survey. 16Shared Mobility Survey, Question 7 cross tabulated with Question 19. 17Shared Mobility Survey, Question 7 cross tabulated with Question 19.

Transportation Network Company Rider Characteristics 29 Impacts on overall travel behavior vary. In responses to questions on the Four Agency Survey about alternative modes in the absence of or in addition to TNCs, and about TNCs’ impact on use of transportation overall, there was little consistency across regions, either in what mode choices were made for different use cases or in how frequently alternatives were used. However, the responses overall suggest that TNC trips provide direct substitutes for some transit trips. At the level of overall travel behavior, the Four Agency Survey asked about the effects of TNCs on car purchase decisions and on the use of transportation overall. Again, there were wide differences in the responses to these questions among regions, the reasons for which are unclear (but which are likely related to differing respondent samples between transit agencies). The Four Agency Survey included questions related to: • Personal car reductions among respondents. On decisions relating to purchasing a car, small numbers of respondents reported a net reduction in vehicle ownership attributable to TNCs—the combination of postponed purchase, deciding not to purchase, and selling a car without replacement. This outweighed the few people in each region who acquired a car to become a TNC driver. In Atlanta, the Bay Area, and Washington, D.C., 5%, 12%, and 21% of respondents, respectively, reported net vehicle reductions in the Four Agency Survey. (NJ Transit did not report on this question.) In the survey conducted for this study, overall approximately 30% of shared mobility users reported shedding a vehicle, though this was attributed to shared modes in general and not to TNCs, specifically. • Overall trip making. In terms of overall impacts on a variety of modes attributed to TNCs, the Four Agency Survey results were more mixed and showed more variation among regions (Table 12). – Impact on taxis and solo driving. It’s clear that taxis are being heavily, negatively impacted in the four regions included in this survey and solo driving also seems to be consistently down, but the effect on transit is less clear. – WMATA showed net decrease in use of all surveyed modes. The results for WMATA, in particular, are striking in that they show a large net decrease, attributed to TNCs, in usage of every alternative mode in the survey (personal driving, transit, taxis, and bicycling), and of roughly twice the magnitude of any of the other Four Agency Survey regions. – BART and MARTA’s findings suggest that people may be riding their transit agencies’ services about the same amount, but other transit in the region less. – WMATA and NJ Transit, which operate a wider range of transit service types, attribute larger negative effects to TNCs. This finding is in line with the Shared Mobility Survey, in which the roughly 10% of respondents whose single top shared mode was TNC had a net reduction of both their private auto use and transit use, while people who used a wider variety of modes were more likely to have reduced their auto use while increasing transit use. BART MARTA WMATA NJ Transit More % Less % Net % More % Less % Net % More % Less % Net % More % Less % Net % Drive own car 2 12 -10 2 6 -4 2 22 -20 -- -- -- Use agency trains/buses 7 6 1 6 5 1 4 32 -28 14 19 -5 Use other transit 4 18 -14 3 4 -1 2 24 -22 -- -- -- Use taxis 1 34 -33 4 8 -6 1 62 -61 -- -- -- Bicycle 2 6 -4 2 4 -2 2 4 -2 -- -- -- Source: Four Agency Survey. Table 12. Impacts of TNCs on the use of other modes.

30 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles • Broader impacts are unknown. TNCs’ net impact on vehicle ownership (i.e., including among people who are not transit riders or do not use other forms of shared mobility) and on vehicle miles traveled (VMT) are not addressed by this study. The survey data cannot be interpreted to draw conclusions about impacts and usage changes among the general population outside existing transit and shared mobility users nor can VMT impacts be imputed from the aggregated trip data available to the researchers (see Appendices A, C, and D for details on the methodol- ogy and data formats). Outcomes of TNC use among the broader traveling public, especially outside of major urban areas, remains an important area for future research. Demographics of Four Agency Survey respondents: TNC users tend to be younger and of higher income than TNC non-users. Some of the variation in results among regions may be attributable to the differing demo- graphics of respondents to the Four Agency Survey (Table 13). Each administering agency collected and aggregated respondent demographics in slightly different ways, so they might not be directly comparable. Several questions also appeared on only one agency’s survey; since each of these is informative in its own way, each is included in the table. Two items are notable: 1) the lower income and higher proportion of minority respondents in Atlanta and 2) the higher income of WMATA respondents. BART, MARTA, and NJ Transit broke out TNC users from non-users, while WMATA did not, and in the three cases where data was reported, the TNC users tended to be somewhat younger, of higher income, and more likely to be white or Asian than non-users. (BART did not report age or income data.) The BART and MARTA surveys also found that nearly all TNC users reported owning smartphones while a lesser (but still large) proportion of TNC non-users also reported smartphone ownership. These findings are mostly consistent with both the demographics of areas with concentrations of TNC use described at the beginning of this section, and with the Shared Mobility Survey’s findings that frequent users of TNCs, both alone and in combination with other modes, had lower average ages and higher average incomes than the overall respondent group. MARTA NJ Transit WMATA BART18 Users Non- users Users Non- users Overall Users Non- users Male (%) 54 53 51 46 50 49 54 Mean age (years) 31 38 36 43 41 -- -- Mean income ($) 35,900 30,200 104,000 77,000 125,900 -- -- Ethnicity: Hispanic (%) 10 6 16 18 6 16 14 Race: White (%) 24 13 57 52 73 58 54 Race: Black (%) 71 87 12 21 15 9 14 Race: Asian/PaciŒic Islander (%) 4 0 15 9 8 27 26 Race: Mixed (%) -- -- 6 6 4 8 7 Race: Other (%) 4 0 10 12 2 5 6 Has disability (%) -- -- -- -- 3 4 8 Has smartphone (%) 94 85 -- -- -- 98 85 Source: Four Agency Survey. Table 13. Demographics of Four Agency Survey respondents. 18BART data was weighted by known population characteristics (Age and Gender) from BART’s 2014 Customer Satisfaction Survey, and data was also weighted by weekly ridership trends. Age and income means are not reported for BART because the information was collected in broad age and income categories that could not be converted to means.

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TRB's Transit Cooperative Research Program (TCRP) Research Report 195: Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles explores the effects of app-based transportation network companies on the cities in which they operate, including on public transit ridership, single-occupancy vehicle trips, and traffic congestion. Built upon the findings of TCRP Research Report 188, this report explores how shared modes—and ridesourcing companies in particular—interact with the use of public transit and personal automobiles.

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