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Suggested Citation:"Summary." 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:"Summary." 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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Page 2
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Suggested Citation:"Summary." 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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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.

1 Urban mobility is rapidly evolving in the United States, particularly since the introduction of app-based transportation network companies (TNCs) such as Uber and Lyft. As these services become more widespread, many have begun to question what effect they are having on the cities where they operate, including on public transit ridership, single-occupancy vehicle (SOV) trips, and traffic congestion. In the face of widespread declines in public transit ridership after a decade or more of growth nationally, these questions have become especially pressing. Speculation has grown around whether TNCs are leading to real changes in how people use public transit and private automobiles, or if these fluctuations are caused by other factors. This report—an extension of TCRP Research Report 188: Shared Mobility and the Trans- formation of Public Transit (2016)—attempts to address these questions by further exploring evidence of how TNCs are affecting the use of public transit and personal automobiles in several regions. The report’s findings draw on several sources, including TNC trip origin-destination data for five regions provided by a major TNC and similar modeled information for the city of San Francisco provided by the San Francisco County Transportation Authority (SFCTA). These regions—Chicago, Los Angeles, Nashville, Seattle, and Washington, D.C.—represent a variety of demographic, transportation, and land-use characteristics. Additionally, the report references a survey of more than 10,000 transit and shared mobility users conducted by the researchers (referred to here as the “Shared Mobility Survey”), as well as rider surveys about TNC use administered by four large public transit agencies (the “Four Agency Survey”). Key findings from this research include: 1. The heaviest TNC use across the regions in this study is during evening hours and weekends. Reaffirming the findings from TCRP Research Report 188, TNC trip data for the five regions, along with modeled data for the city of San Francisco, shows that the greatest levels of TNC use are on Friday and Saturday evenings. The busiest time in most cities is between 7 p.m. and midnight. 2. Most TNC trips in the study regions are short and concentrated in downtown core neighborhoods. Across the five regions represented in the TNC trip data, the mean TNC trip was between 2 and 4 miles. Many took place within a single zip code tabulation area. Peak-hour usage was concentrated primarily in urban cores, along relatively short, contiguous corridors between dense neighborhoods. The only notable exceptions were airports, which were the highest non-core areas of TNC activity in most of the study regions. s u m m a r y Broadening Understanding of the Interplay Among Public Transit, Shared Mobility, and Personal Automobiles

2 Broadening understanding of the Interplay among Public Transit, shared mobility, and Personal automobiles 3. There is no clear relationship between the level of peak-hour TNC use and longer- term changes in the study regions’ public transit usage. From 2010 to 2016,1 Seattle, San Francisco, and Nashville—representing high, medium, and low peak-hour TNC usage, respectively—all saw transit ridership increase. Meanwhile transit ridership in Chicago and Los Angeles (high and medium peak-hour TNC use, respectively) decreased, and Washington, D.C.’s (high peak-hour TNC use) fell by the greatest percentage, accord- ing to National Transit Database reporting. The changes in transit ridership between 2010 and 2016 in these regions do not appear to be related to the regions’ levels of peak- hour TNC usage. 4. Among survey respondents, people who use transit or commute by driving solo do so as part of a routine; TNCs are used on a more occasional basis. Frequent TNC use (weekly or more often) is much less common than frequent transit use or frequent driving. This and other evidence from both the Shared Mobility Survey and the Four Agency Survey suggests that for most users, TNCs are one part of a transportation menu, filling gaps or serving specific needs, but not providing the central mode for most users. 5. Transit travel and wait times were top concerns of survey respondents who replaced transit trips with TNC trips. Faster travel and lower wait times were overwhelmingly cited by the Four Agency Survey respondents as the top reasons for choosing a TNC over transit on the occasions when they did so. The proportions were higher among people who substituted TNCs for transit, ranging from 57% to 87%, compared to 40% to 61% for those who connected to transit. Reliability was also cited as a major concern for riders who substituted TNCs for transit trips in Washington, D.C. Only among the heaviest users of TNCs was commuting a major reason for use; most TNC use took place for recreation. 6. TNC usage takes place in communities of all income levels. The TNC trip data shows that individual TNC trips were widespread across each of the study regions, suggesting that TNCs are used to some degree by people in communities across the socioeconomic spectrum. While urban core areas had the highest volume, TNC trips originated in nearly every zip code in the core counties of the study regions. 7. TNC use is associated with decreases in respondents’ vehicle ownership and SOV trips. Among respondents to the Shared Mobility Survey, the combination of postponed purchases, deciding not to purchase, and selling a car without a replacement outweighed the respondents in each region who acquired a car to become a TNC driver. Respondents also reported net decreases in solo driving. Frequent TNC users reported owning less than one car per household, in line with those of frequent transit users. Those who used a combination of transit and TNCs owned even fewer cars. Due to limitations of the data available to the researchers, the net impact of TNCs on vehicle ownership and vehicle miles traveled (VMT) are not addressed by this study. (See Chapter 4 for more information.) The report also provides a range of guidance to help inform public transit agencies and other public entities in large, midsized and smaller urban areas in their attempts to engage with TNC services. These include: • Transit agencies in large urban areas should continue to prioritize rail, bus rapid transit, bus-only lanes, and other transit-centered approaches that move large numbers of people efficiently and effectively. Recommended strategies for transit agencies that wish to engage with TNCs include designating curb space or other specific locations for TNC pickup/ dropoffs to minimize conflict near transit stops or stations, and pursuing cost savings through public-private partnerships on late-night, call-and-ride, and paratransit services. 1 According to National Transit Database and TNC trip data.

summary 3 • Transit agencies in midsized urban areas may want to explore first-mile/last-mile partnership opportunities with TNCs to help attract new riders and increase the utility of public transit in lower-density areas. Transit agencies in midsized areas may also be able to find ways to work with both TNCs and large employers on behavior change efforts to encourage area residents to their leave cars at home and make alternative transportation choices. Components of such efforts can include carpooling/guaranteed ride home pro- grams, parking policy changes and other transportation demand management strategies. • Transit agencies in smaller urban areas often have challenges beyond the fare box when it comes to providing frequent and full coverage of their service areas, and thus may be interested in partnering with TNCs to provide alternatives to unproductive routes or provide service across greater time spans or geographic areas. These efforts should focus on allowing transit agencies to concentrate their resources on key routes while also bringing new riders to transit through explicit linkages to service gaps in time or geography, such as late nights, weekends, and unserved areas. Transit agencies of all sizes might consider exploring opportunities for fare integration, co-marketing and other strategies that encourage multimodal lifestyles. Additionally, local and state governments should be encouraged to create a predictable framework within which a variety of private providers can operate in the public interest. This includes policies that encourage and prioritize TNC trips that are concurrently shared by multiple riders, thus reducing possible congestion and VMT impacts from additional private vehicles on the street. TNCs can be good partners by providing data, promoting their services in a way that complements the efforts of transit agencies, and working together with cities on efforts to increase mobility, reduce traffic congestion, mitigate carbon emissions, and increase access to underserved communities. Business initiatives that demonstrably serve the public good should also be encouraged.

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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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