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Suggested Citation:"Chapter 2 Market 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 2 Market 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 2 Market 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.
×
Page 7
Page 8
Suggested Citation:"Chapter 2 Market 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.
×
Page 8
Page 9
Suggested Citation:"Chapter 2 Market 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.
×
Page 9
Page 10
Suggested Citation:"Chapter 2 Market 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.
×
Page 10
Page 11
Suggested Citation:"Chapter 2 Market 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.
×
Page 11
Page 12
Suggested Citation:"Chapter 2 Market 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.
×
Page 12

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5 Introduction A major transportation network company provided the researchers with data about trip origins and destinations in five different regions in the United States for the period of May 2016. While representing just a small sample of overall TNC activity nationwide, the data presents a window into TNC usage in a range of urban forms. Aggregated hourly data was provided for the following metropolitan areas: • Chicago, Illinois; • Los Angeles, California; • Nashville, Tennessee; • Seattle, Washington; and • Washington, D.C. The nature and format of the TNC trip data is described in Appendix A and summaries of the data for each of the five regions is provided in Appendix B. These metro areas represent a variety of land-use patterns, physical environments, and population densities at both the core city and metropolitan level (see Table 1). Though San Francisco was not represented in the TNC data provided to the researchers, similar data was collected and modeled at the municipal level in a 2017 study conducted by the SFCTA.2 San Francisco, has, therefore, been included in Table 1 and the discussion. As Table 1 shows, the transportation systems of the six regions range from being largely centered on private automobiles to having robust public transit systems and a variety of shared modes such as microtransit and TNC-based services available. Looking first at the underlying land use and transportation patterns, the densest cities (San Francisco, Chicago, and Washington, D.C.—all with over 10,000 persons per square mile in the core) have lower levels of solo car commuting, fewer cars per household, and greater levels of transit ridership per capita than the other regions included in this research. By comparison, Nashville, with the second-greatest municipal land area and the smallest municipal population, has by far the lowest population density, the highest proportion of car commuters, and the lowest per capita transit ridership. However, Nashville’s rates of both population and transit ridership growth are among the highest of the regions in this research. C H A P T E R 2 Market Characteristics 2San Francisco County Transportation Authority. 2017. TNCs Today: A Profile of San Francisco Transportation Network Company Activity (draft report). June 13. http://www.sfcta.org/sites/default/files/content/Planning/TNCs/TNCs_Today_ 061317.pdf.

6 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles Metropolitan Statistical Area (MSA) Population, millions (% change, 2010-16): MSA; core city Land area, sq. mi. (population density): MSA; core city Solo car commute %; (average household vehicle count): MSA; core city Carshare and bikeshare operators Shared modes: TNC and microtransit services (launch year) Regional public transit: annual unlinked passenger trips, millions 2010/2016 (annual trips per capita 2010/2016) % change 2010 to 2016 Chicago- Naperville-Elgin, IL-IN-WI 9.5 (0.5%); 2.7 (0.4%) 7197 (1327); 228 (11,944) 71% (1.6) 50% (1.1) Zipcar (traditional), Getaround (p2p), Divvy bikeshare Lyft, Uber (2013); Via (2015); UberPool (2015), Lyft Line (2016), LyftShuttle (2017) Chicago Transit Authority: 517/498 (54.6/52.3) NE Ill. Regional Commuter Railroad (Metra): 71/72 (7.5/7.6) Pace Suburban Bus: 32/31 (3.4/3.3) Total: 620/601 (65.5/63.2) Change: –3.0% Los Angeles-Long Beach-Anaheim, CA 13.3 (3.6%); 4.0 (4.7%) 4849 (2751); 469 (8475) 74% (1.8) 70% (1.6) Zipcar (traditional), Metro Bike Share Lyft, Uber (2013); UberPool (2015) Lyft Line (2016) LA County Metropolitan Transportation Authority: 463/432 (36.1/32.5) Orange County Transportation Authority: 56/46 (4.3/3.5) Long Beach Transit: 29/26 (2.2/2.0) LA DOT: 31/22 (2.4/1.6) Santa Monica: 22/17 (1.7/1.2) Total: 601/543 (46.7/40.8) Change: –9.5% Nashville- Davidson- Murfreesboro- Franklin, TN 1.9 (11.3%); 0.7 (9.2%) 6302 (290); 476 (1375) 82% (1.9) 79% (1.7) Zipcar (traditional), Bicycle bikeshare Lyft (2013), Uber (2014) Metropolitan Transportation Authority: 9/10 (5.4/5.3) Regional Transportation Authority: 0.4/0.6 (0.2/0.3) Total: 9.4/10.6 (5.6/5.6) Change: 12.5% San Francisco- Oakland- Hayward, CA 4.7 (7.4%); 0.9 (9.3%) 2478 (1879); 47 (18,451) 60% (1.7) 35% (1.1) Zipcar (traditional); Getaround (p2p); Scoot (scooters); Ford GoBike bikeshare Lyft, Uber (2011); Chariot (2014); UberPool (2014), Lyft Line (2016), LyftShuttle (2017) San Francisco Municipal Railway (Muni): 217/232 (50.1/50.0); Bay Area Rapid Transit (BART): 108/138 (25.0/29.6) Alameda-Contra Costa: 62/55 (14.3/11.7); SamTrans: 15/14 (3.4/2.9); CalTrain: 12/19 (2.8/4.1) Total: 414/458 (95.6/98.3) Change: 10.5% Seattle-Tacoma- Bellevue, WA 3.7 (8.5%); 0.8 (14.0%) 5872 (636); 84 (8164) 70% (1.8) 49% (1.4) Zipcar (traditional), car2go (one-way); LimeBike and Spin bikeshare Lyft, Uber (2013); Chariot (2017), Lyft Line (2016) UberPool (2016) King County Metro Transit: 114/127 (33.0/34.1) Sound Transit: 23/43 (6.8/11.4) Total: 137/170 (39.8/45.5) Change: 24.1% Washington- Arlington- Alexandria, DC- VA-MD-WV 6.1 (8.2%); 0.7 (12.6%) 6246 (976); 61 (10,994) 66% (1.8) 34% (0.9) Zipcar (traditional), car2go (one-way); Getaround (p2p); Capital Bikeshare and dockless providers Lyft, Uber (2011); Via (2016); UberPool (2015) Lyft Line (2016) Washington Metropolitan Area Transit Authority: 418/379 (74.2/62.2) Change: –9.3% Sources: U.S. Census Bureau Annual Estimates of the Resident Population for Incorporated Places of 50,000 or More (MSA/city population); 2010 Census (MSA/city land area); American Community Survey 2010 and 2015 5-year estimates (commute mode, household vehicles, occupied housing units); National Transit Database 2010 and 2016 pro�iles (transit system data); SUMC Shared Mobility Database (shared mobility operators as of October 2017). Table 1. Summary of the mobility characteristics of the six regions from the study.

Market Characteristics 7 Seattle and Los Angeles fall in between, with fairly high levels of population density at the core, extensive metropolitan areas, and comparable levels of household car ownership, driving commutes, and transit ridership at the metro level. In Seattle’s compact core, however, commute mode split and car ownership are more like the three dense-core cities. Findings on TNC Usage The heaviest use of TNCs, across the regions for which TNC trip data was provided, occur during evening hours and weekends but usage does occur at other times. Actual TNC trip data across the five study regions, and modeled data from San Francisco, showed that the greatest levels of TNC use occurred on Friday and Saturday evenings. This reflects the findings in TCRP Research Report 188, which came to a similar conclusion based on surveys and modeled TNC data. Figure 1 presents data for each region, illustrating relative TNC trip volume by hour of the day and day of the week.3 Summaries of TNC usage patterns for each of the regions individually are presented in Appendices A and B. Figure 2 shows equivalent data for TNC trips within the city of San Francisco modeled by the SFCTA. This finding also corresponds to patterns established by responses to both the Shared Mobility Survey and the Four Agency Survey. The trips show distinct patterns based on time of day, day of week, and geographic distribution. Figure 1. Total TNC trip volume by hour and day, in the five study regions. Panels are organized by day (columns) and region (rows), with hours of each day on the bottom horizontal scale. Source: TNC trip data. 3Note that these data show relative TNC trip volumes derived from the normalized figures provided to the researchers; the total volume figures are not directly comparable across regions as they are a sum of indexed ride volumes, not actual trip counts. See Appendix A for a detailed description of the data and its transformation for this study.

8 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles There is no clear relationship at the regional level between peak-hour TNC use and longer-term changes in a region’s public transit usage during the period studied. At a broad level, patterns of TNC use in the six regions appear to cluster similarly to the urban forms and mobility characteristics. • Los Angeles and Nashville. Peak-hour TNC usage—which the researchers assume to be largely commute-related—is lowest relative to overall volume in the most automobile-dependent regions, Los Angeles and Nashville. These cities have a greater share of solo car commuting, fewer transit trips per capita, and more cars per household than most other regions. The weekend night-centered pattern of TNC use is most strongly visible in these regions. • Seattle. With greater downtown density, fewer vehicles per household, and fewer car com- muters than Los Angeles or Nashville, Seattle shows clear morning and evening commute-time TNC ridership, though this is still outpaced by Friday and Saturday night usage. • Chicago and Washington, D.C. The largest morning commute-time TNC usage is evident in Chicago and Washington, D.C., the study regions with the most dense and walkable cores, fewest cars per household, and largest transit mode shares. SFCTA’s modeled data (Figure 2), which was independently derived using a different methodology, also found high peak-hour TNC usage in the city of San Francisco. Although it was estimated using different sources and cannot be directly compared to the other regions in this study (for instance, it only covered trips entirely within the city limits), these data show that peak-hour usage, especially in the morning, makes up a sizeable proportion of the total TNC trips. Across the six regions, weekday TNC peak-hour trips (those taking place during the hours of 7 a.m. to 9 a.m. and 4 p.m. to 6 p.m.) range between about 20% and 27% of the total TNC trip volume for the week (see Table 2). In other words, roughly three quarters of TNC trips take place outside of peak travel hours. At the regional level, there is no clear relationship between peak-hour TNC usage and longer- term changes in a region’s public transit usage (Figure 3). In the period between 2010 and 2016,4 the following changes in transit ridership took place in the five study cities and San Francisco: • Transit ridership grew by more than 10% in San Francisco, Seattle, and Nashville. These cities represented almost the full range of peak-hour TNC usage, from lowest to highest. Seattle saw a 24% increase in transit usage, and also had nearly the highest level of peak-hour TNC usage. • Transit ridership declined by single digit percentages in Chicago, Los Angeles, and Washington, D.C. In peak-hour TNC usage, Chicago and Los Angeles ranked near the top and the bottom, respectively. Transit ridership fell by 9% in Washington, D.C., during a period Figure 2. TNC pickups by hour and day, San Francisco. Panels are organized by day, with hours of each day on the bottom horizontal scale. Source: SFCTA modeled data of intracity trips in the city of San Francisco. 4According to unlinked passenger trips in National Transit Database ridership reports.

Market Characteristics 9 that coincided with major maintenance-related track closures across the Washington Metro- politan Area Transit Authority (WMATA) rail system in 2015 and 2016. The region had the highest levels of peak-hour TNC usage among the cities observed. Varying the base year of the analysis makes little difference—no association was found between current peak-hour TNC usage and changes in regional transit ridership for any period between 2010 and 2016. Taken together, these divergent trends suggest little association between longer-term changes in transit ridership and the proportion of peak-hour TNC usage. TNCs have been operating in all six markets since at least 2013, and in San Francisco and Washington since 2011. However, it is possible that impacts of broadening and maturing TNC markets were not yet visible throughout this period. The recent, shorter-term transit ridership decreases that have been observed in cities across the country might be related to these or other economic factors, including rebounding auto sales after the end of the 2007–2009 recession, historically low gas prices after several years of high prices, and cuts and interruptions to transit Region Peak-Hour Trips as Percentage of Total Chicago 26.8 Washington, DC 27.2 Los Angeles 23.2 Nashville 19.8 Seattle 27.1 San Francisco* 24.1* Note: Signiicant observations only (see Appendix A). *San Francisco data is from SFCTA modeling, and those volume igures refer to modeled pickup counts for trip entirely within the City of San Francisco, while the other cities cover larger portions of their metro areas. Sources: TNC trip data, SFCTA modeled TNC data. Table 2. Peak-hour TNC trip volumes as a percentage of total TNC volume in each region. Chicago Washington, D.C. Los Angeles Nashville Seattle San Francisco* –15 –10 –5 0 5 10 15 20 25 30 18 20 22 24 26 28 30 T ra ns it rid er sh ip c ha ng e (% ) Peak hour TNC volume as percentage of total Figure 3. Peak-hour TNC use versus transit ridership change 2010–2016. Sources: TNC trip data, SFCTA modeled TNC data, National Transit Database unlinked passenger trip counts 2010–2016.

10 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles service in some cities. As TNCs continue to evolve and the economy reaches other points in the business cycle, this will be a crucial area for continued research. Peak-hour TNC usage is concentrated in downtown areas. Looking at peak-hour TNC patterns in Chicago and Washington, D.C., two of the study cities with the greatest peak-hour TNC usage, activity is concentrated in the urban cores, along relatively short, contiguous corridors between dense neighborhoods, or within dense suburbs adjacent to the city. This concentration of TNC ride volumes in a few core areas is typical of TNC usage patterns more broadly, at commute times and other times of the day. In Washington, D.C. (Figure 4), these areas fall along an east-west belt of zip code tabulation areas (ZCTAs) across the broadest central section of the District from the Arboretum area, which includes Union Station, west to Georgetown and across the Potomac to Arlington. In Chicago (Figure 5) the heaviest peak-hour TNC trips are in the downtown Loop or in nearby neighborhoods to the north and northwest, from the South Loop north to Ravenswood, and west to Wicker Park, with an outlying single-zip enclave in Evanston, a walkable and transit- connected suburb on the city’s northern border. All these areas are well served by public transit, although specific transit journeys may vary in travel time and reliability, depending on corridors and station areas—for instance, a crosstown journey from Chicago’s Wicker Park east to the lakefront is served only by local bus routes on Figure 4. Major peak-hour flows in Washington, D.C. (hourly volume greater than 25% of the highest volume flow for the region). Note: Arrows show flows between ZCTAs (brighter = greater volume) and colored areas indicate ZCTAs with internal single-zip flows (darker = greater volume). Source: TNC trip data.

Figure 5. Major peak-hour flows in Chicago (hourly volume greater than 25% of the highest volume flow for the region). Note: Arrows show flows between ZCTAs (brighter = greater volume) and colored areas indicate ZCTAs with internal single-zip flows (darker = greater volume). Source: TNC trip data.

12 Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles highly congested surface streets, while a trip from Ravenswood south to the Loop has multiple rail and express bus options. The areas with the heaviest peak-hour TNC trip volumes include many of the highest-income downtown residential areas in their respective regions. They are by no means exclusively wealthy areas. However, sizeable swathes of lower income northeast Washington, D.C., and Chicago’s West Side fall within these zones as well. Chapter 4 contains more information on the demographic characteristics of the areas of heaviest TNC usage. It is impossible to distinguish between last-mile TNC trips from transit hubs and trips between home and work. Because of the geographic scale of the data, it is impossible to tell what proportion of TNC trips might constitute last-mile trips from transit hubs versus trips between home and work, since the ZCTAs that contain the major commuter rail terminuses in both Chicago and Washington, D.C., also contain major employment concentrations. In both regions, the heaviest peak-hour TNC trip flows are under 5 miles, with median lengths around 1.5 miles; these are the high end of typical lengths for TNC peak-hour trip flows for all five study regions. However, the transit agency survey (see Chapter 4) suggests that transit- connecting trips are an important part of TNC usage, comprising a share of recent TNC trips that ranges from 3% on WMATA to 16% on BART.

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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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