For-hire vehicle services such as ridesourcing are becoming increasingly prevalent across the globe, and taxis continue to operate, although now with considerably more competition. Further, TNCs and taxis are partnering with other mobility providers more than ever before. This session will explore the demographics of ridesourcing and taxi users, travel behavior impacts, and case studies in public-private partnerships.
Trends in Taxi Use and the Advent of Ridehailing, 1995–2017: Evidence from the U.S. National Household Travel Survey
Matthew Conway, Arizona State UniversityShow Abstract View Presentation
Deborah Salon, Arizona State University
David King, Arizona State University School of Sustainable Engineering and the Built Environment
The advent of ridehailing services such as Uber and Lyft has expanded for-hire vehicle travel. We use data from the 2017 National Household Travel Survey (NHTS) to investigate the extent of this expansion in the United States. We report changes in the for-hire vehicle market since ridehailing services became available, and statistically estimate the determinants of ridehailing use. From 2009-2017, for-hire vehicle market share has doubled. While for-hire vehicles still only account for 0.5% of all trips, the percent of all Americans who use ridehailing in any given month is nearly 10%. Within the for-hire vehicle market, growth has not been uniformly distributed across demographic groups or geographies; it has been greater in mid-sized and large cities, in denser neighborhoods, and among younger individuals and wealthier households. This uneven growth suggests the importance of understanding the equity implications of the rise of ridehailing. Multivariate analysis provides evidence that both transit and nonmotorized transport use are correlated with ridehailing use, and that ridehailing has a negative relationship with vehicle ownership. Given the rapid growth of ridehailing, it becomes important for cities to include for-hire vehicles in their planning going forward. These NHTS data provide a starting point, but more detailed and frequent data collection is needed.
Socioeconomic and Usage Characteristics of Transportation Network Company Riders
Rick Grahn, Carnegie Mellon UniversityShow Abstract
Corey D. Harper, Booz Allen Hamilton, Inc.
Chris Hendrickson, Carnegie Mellon University
Sean Qian, Carnegie Mellon University
Scott Matthews, Carnegie Mellon University
The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, and have higher levels of education compared to the general population. Urban residents are more likely to use TNC services compared to rural residents and higher population density regions see increased TNC ridership. The presence of heavy rail facilitates increased TNC use as these two modes of travel can be easily paired. TNC users use public transit at higher rates and own fewer vehicles compared to the general population
A Review of Partnerships Between Transportation Network Companies and Public Agencies in the United States
Joseph Schwieterman, DePaul UniversityShow Abstract
Mallory Livingston, DePaul University
This paper summarizes the status of twenty-nine partnerships between transportation network companies (TNCs) and public bodies around the United States designed to improve mobility. The analysis focuses on initiatives that involve (or have involved) ridesharing services and excludes partnerships with microtransit operators. For each of the 29 partnerships observed, the research team evaluated when the programs were active, how the programs were financially structured, and audits of program performance. The results show that 11 of the 50 largest transit agencies in the United States (measured by annual unlinked trips) have developed partnerships promoting synergy with ridesharing, primarily with Lyft and Uber. Two of the largest 10, Metropolitan Boston Transportation Authority and Philadelphia’s Southeastern Pennsylvania Transportation Authority, have provided direct financial support for certain TNC trips through specialized programs. The analysis suggests, however, that more cities and transit agencies will explore options for partnerships in the next several years, including those improving paratransit service. The integration of fares for trips involving transit/TNC connections is a logical next step in the development of partnerships. The lack of formal analysis of the performance of most initiatives, nevertheless, stands as a significant barrier to the refinement of the strategies being used.
Prevalence and Mechanisms of Discrimination: Evidence from the Ridehail and Taxi Industries
Anne Brown, Univeristy of OregonShow Abstract View Presentation
For decades, taxis have provided profoundly unequal levels of service based on rider race. In 2012, however, ridehail companies such as Uber and Lyft upended the taxi industry by connecting riders to drivers using smartphone applications. Yet despite potential equity implications for travelers, we do not know if discrimination manifests in these new services and how it compares to the status quo embodied by taxis. To fill this gap, this paper asks and answers two questions. First, is there evidence of service discrimination—manifested in longer wait times and higher rates of cancelled ride requests—by rider race, ethnicity, or gender on ridehail and taxi services? And second, can different hailing platforms combined with quantitative differences across riders yield insights into when discrimination occurs? To answer these questions, an audit study of ridehail and taxi services was conducted in Los Angeles. Audit results reveal stark discrimination against black taxi riders. Taxis failed to pick up black riders for more than one-quarter of their trip hails (26%), compared to about one-seventh (14%) of trips hailed by white riders. By contrast, ridehail services nearly eliminated the differences across rider characteristics. On taxis, black riders waited 6 to 15 minutes longer than white riders; by comparison, black ridehail users waited between 11 seconds and 1 minute 43 seconds longer than white users. Findings suggest that discrimination occurs when people infer rider characteristics and yield implications for how policymakers can use new technologies to deter discrimination and achieve equitable service for all travelers.