A Study of the Impact of Ride-hailing on Public Transit Ridership

University Transportation Centers Program2021PDFTech ReportBreuer, HelenaDu, JianheRakha, HeshamUrban Mobility & Equity CenterVirginia Tech Transportation InstituteMorgan State UniversityUniversity of Maryland (College Park, Md.)United States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyUrban Mobility & Equity CenterMorgan State UniversityChicago (Illinois)United StatesGeospatial dataImpactsMode choiceMultinomial logitsOrigin and destinationPublic transitRidershipRidesourcingSpatial analysisSensitivity analysisRidehailingUMEC-02969A43551747123Final May 2020 - April 2021Existing literature on the relationship between ridehailing (RH) and transit services is limited to empirical studies that rely on self-reported answers and lack spatial and temporal contexts. To fill this gap, this research takes a novel approach that uses real-time geospatial analyses. Using this approach, the authors estimate the extent to which RH services have contributed to the recent decline in public transit ridership. With source data on RH trips in Chicago, Illinois, the authors computed the real-time transit-equivalent trips for the 7,949,902 RH trips taken in June 2019. The sheer size of this sample far exceeds the samples studied in existing literature. An existing multinomial nested logit model was used to determine the probability of a ridehailer selecting a transit alternative to serve the specific origin-destination pair, P(Transit|CTA). The study found that 31% of RH trips are replaceable, 61% are not replaceable, and 8% lie within the buffer zone. The authors measured the robustness of this probability using a parametric sensitivity analysis, and performed a two-tailed t-test, with a 95% confidence interval. In combination with a summation of probabilities, the results indicate that the total travel time for a transit trip has the greatest influence on the probability of using transit, whereas the airport pass price has the least influence. Further, walk time, number of stops in the origin and destination census tracts, and household income also have significant impacts on the probability of using transit. Lastly, the authors performed a time value analysis to explore the cost and trip duration difference between RH trips and their transit-equivalent trips on the probability of switching to transit. The findings demonstrated that approximately 90% of RH trips taken had a transit-equivalent trip that was less expensive, but slower. The main contribution of this study is its thorough approach and the fine-tuned series of real-time spatial analyses that investigate the replaceability of RH trips with public transit. The results and discussion intend to provide a perspective derived from real trips and to encourage public transit agencies to investigate possible opportunities to collaborate with RH companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, the authors hope that this effort provides proof of the research benefits associated with the recording and release of RH data.125