Spatial-Dynamic Matching Equilibrium Models of New York City Taxi and Uber Markets
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 9
Abstract
With the rapidly changing landscape for taxis, ride-hailing, and ride-sourcing services, public agencies have an urgent need to understand how such new services impact social welfare: impacts of technologies on matching customers to service providers, evaluating ride-sourcing operations, and evaluating surge pricing policy, among others. We conduct an empirical study to answer this question for Uber using a dynamic spatial equilibrium taxi-matching model. Given a matching function, the spatial distribution of demand activities, and service coverage, the model outputs equilibrium fleet sizes, matches, and social welfare by zone and time of day. Uber provides pickup data for a specific time period in New York City (NYC). Parameters from the model calibrated from medallion cab (Taxi) data are grafted onto the Uber model to supplement the missing information. The Uber model has a root-mean square error of 7.75 matches/zone/interval, which is approximately an 8.52% error. Spatial distribution of responses in demand to fare hikes or vehicle supply to demand surges measurably differ between NYC Taxi and Uber markets. Baseline estimations of welfare indicate that the NYC Taxi industry generates $495,900 in consumer surplus and $1,022,400 in Taxi profits for the 4-h interval, while for the Uber market, the model estimates $73,300 in consumer surplus and $151,300 in Uber profits during the same interval. Spatial-temporal dynamics resulting from fare hike and congestion fee scenarios are analyzed to determine requirements for allocating the congestion charge revenues toward public transit to maintain or improve upon the same consumer surplus.
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Data Availability Statement
Uber and medallion taxi trip records and Manhattan NTA area geospatial maps (shapefiles) that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was partially supported by the National Science Foundation Grant No. CMMI-1634973, the C2SMART Tier-1 University Transportation Center and the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) Ecuador. We also want to thank Alex Leon, a visiting summer student through the ARISE program, for his help in the data acquisition. The contents of this paper present views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents of the paper do not reflect any official views of any sponsoring organizations or agencies.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 9 • September 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 3, 2020
Accepted: Mar 4, 2021
Published online: Jul 7, 2021
Published in print: Sep 1, 2021
Discussion open until: Dec 7, 2021
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