Non-myopic path-finding for shared-ride vehicles : A bi-criterion best-path approach considering travel time and proximity to demand

Shared-ride mobility-on-demand (MOD) services offered by transit agencies (e.g. flexible, demand-adaptive, and demand-responsive transit) and private companies (e.g. Uber Pool, Lyft Line, microtransit) have the potential to provide high-quality, convenient, and affordable on-demand mobility service to individual travelers, while simultaneously obtaining the societal benefits of decreased vehicle miles traveled, congestion, and vehicle emissions through increased vehicle occupancies. However, for shared-ride MOD services to capture these societal and individual mobility benefits, they need to be operated efficiently. Hence, this research project focuses on the efficient operation of shared-ride MOD services. Although several research studies address shared-ride MOD operational problems, this research project addresses a severely overlooked shared-ride MOD operational subproblem, namely, the assignment of individual shared-ride vehicles to network paths as they move between user pickup and drop-off locations. In practice, and in the academic literature, fleet controllers assign shared-ride vehicles (like non-shared-ride vehicles) to the shortest network path, in terms of travel time, between pickup and drop-off locations in their schedules. While this strategy/policy is intuitive, it is also myopic given the nature of shared-ride on-demand service and the (high) likelihood new users will request service as vehicles traverse network paths between pickup and drop-off locations. A non-myopic approach would anticipate the possibility of new requests and consider the proximity of network paths to future user requests (i.e. demand) when assigning shared-ride vehicles to network paths. The objective of this research is to support the efficient operation of shared-ride MOD services as a means to enhance mobility via developing a non-myopic algorithm to assign individual shared-ride vehicles to network paths considering proximity to future demand in addition to travel time. The PI’s hypothesis is that the consideration of proximity of network paths to future demand in the controller’s objective function will increase shared-ride opportunities and prevent some shared-ride vehicle detours from low-demand, high-speed areas back to high-demand, lower speed areas to pick up new requests. This should subsequently improve service quality, decrease operational costs, and decrease required fleet sizes for shared-ride MOD services.

    Language

    • English

    Project

    • Status: Active
    • Funding: $53,923.00
    • Sponsor Organizations:

      California Department of Transportation

      1227 O Street
      Sacramento, CA  United States  95843
    • Managing Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626
    • Project Managers:

      Brinkerhoff, Cort

    • Performing Organizations:

      University of California, Irvine

      Institute of Transportation Studies
      4000 Anteater Instruction and Research Building
      Irvine, CA  United States  92697
    • Principal Investigators:

      Hyland, Michael

    • Start Date: 20200101
    • Expected Completion Date: 20201231
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01732422
    • Record Type: Research project
    • Source Agency: METRANS Transportation Center
    • Files: RiP
    • Created Date: Feb 28 2020 4:17PM