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The Ridesharing Routing Problem with Flexible Pickup and Drop-off Points

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  • Dessouky, Maged
  • Mahtab, Zuhayer

Abstract

In major metropolitan areas such as Los Angeles County, ride-sharing systems can help reduce traffic congestion and increase the efficiency of the transportation system. This research project proposes three different solution approaches for solving the ride share routing problem with flexible pickup and drop-off points. The first is a dynamic programming-based route enumeration procedure that can be used to solve small-sized problems; the other two are branch and price-based heuristics for solving large problems. The researchers first provide a mixed integer nonlinear model for routing and pickup and drop-off points selection which they later decompose into a master and subproblem for solving. To validate the performance of their approaches and gather valuable insights about the ridesharing system, the researchers perform numerical experiments on a San Francisco Taxicab dataset. Results show that the approaches are efficient, solving instances with up to 300 nodes within 130 CPU seconds. For these datasets, incorporating flexible meeting points (i.e., pickup and drop-off points) can reduce the total travel time of the rideshare system by 18%. Sensitivity analysis shows that it can also decrease the time passengers wait time for rides by 43%. The methodologies in this study can help transportation planners design more efficient rideshare systems with less waiting, better passenger service, and less travel time. View the NCST Project Webpage

Suggested Citation

  • Dessouky, Maged & Mahtab, Zuhayer, 2022. "The Ridesharing Routing Problem with Flexible Pickup and Drop-off Points," Institute of Transportation Studies, Working Paper Series qt3107w642, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt3107w642
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    References listed on IDEAS

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    1. Martin Desrochers & Jacques Desrosiers & Marius Solomon, 1992. "A New Optimization Algorithm for the Vehicle Routing Problem with Time Windows," Operations Research, INFORMS, vol. 40(2), pages 342-354, April.
    2. Zhi (Aaron) Cheng & Min-Seok Pang & Paul A. Pavlou, 2020. "Mitigating Traffic Congestion: The Role of Intelligent Transportation Systems," Information Systems Research, INFORMS, vol. 31(3), pages 653-674, September.
    3. Marilène Cherkesly & Guy Desaulniers & Gilbert Laporte, 2015. "Branch-Price-and-Cut Algorithms for the Pickup and Delivery Problem with Time Windows and Last-in-First-Out Loading," Transportation Science, INFORMS, vol. 49(4), pages 752-766, November.
    4. Roberto Baldacci & Vittorio Maniezzo & Aristide Mingozzi, 2004. "An Exact Method for the Car Pooling Problem Based on Lagrangean Column Generation," Operations Research, INFORMS, vol. 52(3), pages 422-439, June.
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    More about this item

    Keywords

    Engineering; Dynamic programming; Mixed integer programming; Origin and destination; Ridesharing; Routing; Travel time; Waiting time;
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