Transit Planning Optimization Under Ride-Hailing Competition and Traffic Congestion

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, the authors develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers' mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, the authors' optimized transit schedules consistently lead to 0.4%-3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers' preferences - by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win-win-win outcome.

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    • Abstracts reprinted with permission of INFORMS (Institute for Operations Research and the Management Sciences,
  • Authors:
    • Wei, Keji
    • Vaze, Vikrant
    • Jacquillat, Alexandre
  • Publication Date: 2022-5


  • English

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  • Accession Number: 01849400
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 24 2022 10:54AM