Ride-hailing Impacts on Transit Ridership: Chicago Case Study

TL;DR

This study uses real-time geospatial analysis of Chicago ride-hailing data to assess how many trips could be replaced by transit, revealing significant insights into ride-hailing's impact on transit ridership.

Contribution

It introduces a novel real-time geospatial analysis method to evaluate ride-hailing trip replaceability with transit, based on a large dataset and detailed probabilistic modeling.

Findings

31% of ride-hailing trips are replaceable by transit

61% of trips are not replaceable by transit

Probability of replaceability is most sensitive to transit travel time

Abstract

Existing literature on the relationship between ride-hailing (RH) and transit services is limited to empirical studies that lack real-time spatial contexts. To fill this gap, we took a novel real-time geospatial analysis approach. With source data on ride-hailing trips in Chicago, Illinois, we computed real-time transit-equivalent trips for all 7,949,902 ride-hailing trips in June 2019; the sheer size of our sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ride-hailer selecting a transit alternative to serve the specific O-D pair, P(Transit|CTA). We find that 31% of ride-hailing trips are replaceable, whereas 61% of trips are not replaceable. The remaining 8% lie within a buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis and performed a two-tailed t-test. Our results indicate that of the four sensitivity parameters, the probability was most sensitive to the total travel time of a transit trip. The main contribution of our research is our thorough approach and fine-tuned series of real-time spatiotemporal analyses that investigate the replaceability of ride-hailing trips for public transit. The results and discussion intend to provide perspective derived from real trips and we anticipate that this paper will demonstrate the research benefits associated with the recording and release of ride-hailing data.