Passenger request prediction is essential for operations planning, control, and management in ride-hailing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) flow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network (GNN) framework along with the Attention Mechanism to predict the OD flow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originating from different locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.


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    Titel :

    GNN-based passenger request prediction


    Weitere Titelangaben:

    A. A. MAKHDOMI AND I. A. GILLANI
    TRANSPORTATION LETTERS


    Beteiligte:

    Erschienen in:

    Transportation Letters ; 16 , 10 ; 1237-1251


    Erscheinungsdatum :

    25.11.2024


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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