The occurrence of traffic accidents in cities is often accompanied by property losses, environmental pollution, casualties, and congestion. Predicting the spatio-temporal range of accident-induced congestion can mitigate the negative effects by taking appropriate measures to respond to traffic accidents in a timely manner. Unlike most existing traffic accident spatial-temporal prediction strategies that depend on existing traffic models, this paper proposes a model-free method by using the macroscopic road network images, which relieves the restriction of precise modeling of traffic dynamics and the detailed traffic data. Specifically, we first design a digital twin road network to observe the traffic operation from a macro perspective. Then, after designing the structure of the Convolutional LSTM (Conv-LSTM) cell, we stack multiple Conv-LSTM layers to form an encoding-decoding structure to predict spatio-temporal congestion caused by accidents in urban road networks. Finally, the simulation results indicate that the proposed method improves the prediction accuracy compared with the model-based method and the LSTM network model. The proposed strategy provides a new approach to predict the spatio-temporal congestion caused by accidents from a macroscopic perspective.


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    Titel :

    Digital Twin Empowered Model Free Prediction of Accident-Induced Congestion in Urban Road Networks


    Beteiligte:
    Ji, Xingyi (Autor:in) / Yue, Wenwei (Autor:in) / Li, Changle (Autor:in) / Chen, Yue (Autor:in) / Xue, Nan (Autor:in) / Sha, Zifan (Autor:in)


    Erscheinungsdatum :

    2022-06-01


    Format / Umfang :

    1119662 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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