Accurate short-term passenger demand origin-destination (OD) matrix prediction contributes to the coordination of traffic supply and demand. This study proposes a novel generative adversarial network (GAN) named Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) to predict the network-wide ride-sourcing passenger demand OD matrix. The proposed CWGAN-GP model can not only capture internal spatiotemporal features of OD matrices, but also characterise external dependencies of OD matrices on conditional information, such as the traffic zone-based average traffic speeds, the traffic zone area, and time variables. Based on the ride-sourcing GPS trajectories from Didi Chuxing, Chengdu, China, and ride-sourcing data from the New York City, numerical results illustrate that the predicted OD matrices are in good agreement with the actual ones, and CWGAN-GP has good convergence performance by analysing the discriminator loss and the Wasserstein distance with respect to training epochs. Comparison results also validate the outperformance of CWGAN-GP compared with the other counterpart prediction methods and the reasonability of specific structures of CWGAN-GP. Thus, CWGAN-GP is concluded to be promising to predict network-wide ride-sourcing passenger demand OD matrices.


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    Title :

    Network-wide ride-sourcing passenger demand origin-destination matrix prediction with a generative adversarial network


    Additional title:

    TRANSPORTMETRICA A: TRANSPORT SCIENCE
    C. LI ET AL.


    Contributors:
    Li, Changlin (author) / Zheng, Liang (author) / Jia, Ning (author)


    Publication date :

    2024-01-02


    Size :

    28 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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