Vehicular crowd-sensing (VCS) has become a widely adopted approach in smart city applications, with online ride-hailing system as a promising carrier. However, the spatially unbalanced demand of online ride-hailing services makes it challenging to collect urban information comprehensively. This paper explores the potential of vehicle-passenger matching strategy in VCS based on online ride-hailing. We propose a mixed-integer programming model that considers two factors: the total sensing coverage of the fleet and drivers' income. With a limited monetary incentive budget, the model can improve the spatial-temporal coverage of VCS by matching suitable passengers or providing targeted cruising directions for idle vehicles. To validate the effectiveness of the matching strategy, we conduct simulation tests based on two demand scenarios with different passenger distributions. Compared to an income-maximizing greedy algorithm as benchmark, the proposed method improves the sensing coverage by up to 13.3%, at a minor cost to the drivers' income, by up to 1.6%. The mixed-integer programming approach is also compared to a coverage-maximizing greedy heuristic, with coverage improvement by up to 5.8%. A sensitivity analysis is conducted for different penetration rates of the sensors, which reveals a general trend of both objectives that can be used to inform decision making on sensor investment. The proposed methods and results offer insights into the sensing potential of ride-hailing fleets.


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    Title :

    Optimizing Vehicle-Passenger Matching for Online Ride-Hailing with Vehicular Crowd-Sensing


    Contributors:
    Meng, Danyang (author) / Han, Ke (author)


    Publication date :

    2023-09-24


    Size :

    5400995 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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