Version 1
: Received: 29 September 2020 / Approved: 30 September 2020 / Online: 30 September 2020 (14:50:53 CEST)
How to cite:
Breuer, H.; Du, J.; Rakha, H. Ride-hailing Impacts on Transit Ridership: Chicago Case Study. Preprints2020, 2020090753. https://doi.org/10.20944/preprints202009.0753.v1
Breuer, H.; Du, J.; Rakha, H. Ride-hailing Impacts on Transit Ridership: Chicago Case Study. Preprints 2020, 2020090753. https://doi.org/10.20944/preprints202009.0753.v1
Breuer, H.; Du, J.; Rakha, H. Ride-hailing Impacts on Transit Ridership: Chicago Case Study. Preprints2020, 2020090753. https://doi.org/10.20944/preprints202009.0753.v1
APA Style
Breuer, H., Du, J., & Rakha, H. (2020). Ride-hailing Impacts on Transit Ridership: Chicago Case Study. Preprints. https://doi.org/10.20944/preprints202009.0753.v1
Chicago/Turabian Style
Breuer, H., Jianhe Du and Hesham Rakha. 2020 "Ride-hailing Impacts on Transit Ridership: Chicago Case Study" Preprints. https://doi.org/10.20944/preprints202009.0753.v1
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)[1]. 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. [1] This value defines the replaceability of the trip, where a value ranging from 0 to 0.45 is considered not-replaceable (NR), and a value ranging from 0.55 to 1.0 is considered replaceable (R).
Keywords
public transit; utility; replacement; ride hailing; ridesharing; Uber; Lyft
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
