Highlights A model is proposed for modular vehicle-based routing with platooning and transfers. A Steiner-tree inspired large neighborhood search heuristic is proposed. Experiments conducted on Anaheim network with up to 75 vehicles and 150 requests. Platooning can save up to 52% in vehicle travel cost and 41% passenger service time. Modular vehicles benefit most from serving “enclave pairs” as dynamic hub-and-spoke.

    Abstract Modular vehicles (MV) possess the ability to physically connect/disconnect with each other and travel in platoon with less energy consumption. A fleet of demand-responsive transit vehicles with such technology can serve passengers door to door or have vehicles deviate to platoon with each other to travel at lower cost and allow for en-route passenger transfers before splitting. A mixed integer linear programming (MILP) model is formulated to solve this “modular dial-a-ride problem” (MDARP). A heuristic algorithm based on Steiner-tree-inspired large neighborhood search is developed to solve the MDARP for practical scenarios. A set of small-scale synthetic numerical experiments are tested to evaluate the optimality gap and computation time between exact solutions of the MDARP using commercial software and the proposed heuristic. Large-scale experiments are conducted on the Anaheim network with up to 75 vehicles and 150 requests considering 378 candidate join/split nodes to further explore the potentials and identify the ideal operation scenarios of MVs. The results show that MV technology can save up to 52% in vehicle travel cost, 41% in passenger service time, and 29% in total cost against existing on-demand mobility services in the scenarios tested. Results suggest that MVs best benefit from platooning by serving “enclave pairs” as a hub-and-spoke service.


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    Title :

    Dial-a-ride problem with modular platooning and en-route transfers


    Contributors:


    Publication date :

    2023-05-26




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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