Existing 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, the research takes a novel approach that uses real-time geospatial analyzes. Using this approach, we estimate the extent to which ride-hailing services have contributed to the recent decline in public transit ridership.

With source data on ridehailing trips in Chicago, Illinois, we computed the real-time transit-equivalent trip for the 7,949,902 ridehailing trips in June 2019; the sheer size of this sample is incomparable to 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. We 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, the 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, we 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 fine-tuned series of real-time spatial analyzes that investigate the replaceability of RH trips for public transit. The results and discussion intend to provide perspective derived from real trips and encourage public transit agencies to look into possible opportunities to collaborate with ridehailing companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, we hope that this effort provides proof of the research benefits associated with the recording and release of ridehailing data.