Urban traffic data plays an important role in urban transportation planning. Due to the scarcity of real-life urban traffic data, many transportation planning applications need to generate synthesized traffic flows based on the real-life trajectory datasets. However, those synthesized traffic flows can only fit the input trajectories, which are static and does not reflect the real traffic distributions. In this paper, we use a stochastic origin-destination (OD) matrix to represent the density of the dynamic traffic flows and then develop a dynamic traffic flow generator. We extract the stochastic OD matrix from the trajectory data, design an efficient neural network to the predict successive stochastic OD matrices, and deploy our model on a real-world road network. The proposed model surpasses the existing generative model in RMSE, MAE, VAR, KL indicators, and is significantly better than the existing model in the MAE indicator. Our traffic generator is able to dynamically adjust urban traffics to generate different simulation environments.


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    Title :

    Generating Dynamic Urban Traffic Based on Stochastic Origin-Destination Matrix


    Contributors:
    Wang, Zhichang (author) / Zhang, Binjie (author) / Xia, Nu (author) / Jin, Jiahui (author)


    Publication date :

    2022-05-04


    Size :

    2172329 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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