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Real-world meeting points for shared demand-responsive transportation systems

Author

Listed:
  • Paul Czioska

    (Leibniz Universität Hannover)

  • Ronny Kutadinata

    (The University of Melbourne)

  • Aleksandar Trifunović

    (Technische Universität Braunschweig)

  • Stephan Winter

    (The University of Melbourne)

  • Monika Sester

    (Leibniz Universität Hannover)

  • Bernhard Friedrich

    (Technische Universität Braunschweig)

Abstract

While shared demand-responsive transportation (SDRT) systems mostly operate on a door-to-door policy, the usage of meeting points for customer pick-up and drop-off can offer several benefits, such as fewer stops and less total travelled kilometers. Moreover, real-world meeting points offer a possibility to select only feasible and well-defined locations where safe boarding and alighting are possible. This paper investigates the impact of using such meeting points for the SDRT problem with meeting points (SDRT-MP). A three-step procedure is applied to solve the SDRT-MP. Firstly, the customers are clustered into temporary and spatially similar groups and then the alternative meeting points, for boarding and alighting, are determined for each cluster. Finally, a neighbourhood search algorithm is used to obtain the vehicle routes that pass through all the used meeting points while respecting passengers’ time constraints. The goal is to examine the differences of a real-world meeting point-based system in contrast to a door-to-door service by a simulation with realistic meeting point locations derived from the map data. Although the average passenger travel time is higher due to increased walking and waiting times, the experiment highlights a reduction of operator resources required to serve all customers.

Suggested Citation

  • Paul Czioska & Ronny Kutadinata & Aleksandar Trifunović & Stephan Winter & Monika Sester & Bernhard Friedrich, 2019. "Real-world meeting points for shared demand-responsive transportation systems," Public Transport, Springer, vol. 11(2), pages 341-377, August.
    • Handle: RePEc:spr:pubtra:v:11:y:2019:i:2:d:10.1007_s12469-019-00207-y
    DOI: 10.1007/s12469-019-00207-y
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    References listed on IDEAS

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    3. Fudong Xie & Ce Wang & Housheng Duan, 2024. "Optimizing Fleet Size in Point-to-Point Shared Demand Responsive Transportation Service: A Network Decomposition Approach," Mathematics, MDPI, vol. 12(19), pages 1-20, September.
    4. Queiroz, Michell & Lucas, Flavien & Sörensen, Kenneth, 2024. "Instance generation tool for on-demand transportation problems," European Journal of Operational Research, Elsevier, vol. 317(3), pages 696-717.
    5. Si, Jinhua & He, Fang & Lin, Xi & Tang, Xindi, 2024. "Vehicle dispatching and routing of on-demand intercity ride-pooling services: A multi-agent hierarchical reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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