The objective of the study is to predict traffic flow under unusual conditions by using a deep learning model. Conventionally, machine-learning-based traffic prediction is frequently carried out. Model learning requires large amounts of training data; however, collecting sufficient samples is a challenge in the event of traffic incidents. To address this challenge, large amounts of traffic data were generated by performing traffic simulations under various traffic incidents. These data were used as training data, and a deep learning model with graph convolution and input of traffic incident information features was proposed. Subsequently, the prediction accuracy was compared with other models such as long short-term memory, which is typically used in traffic prediction. The results demonstrated the superiority of the proposed model in representing phenomena with strong spatio-temporal dependencies, such as traffic flow, and its effectiveness in traffic prediction.


    Access

    Access via TIB


    Export, share and cite



    Title :

    Short-term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation


    Contributors:

    Published in:

    Publication date :

    2020-05-29


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Short-term traffic flow prediction method based on graph convolution recurrent neural network

    GU JUNHUA / GUO RUIZHE / HE WENYING et al. | European Patent Office | 2024

    Free access

    Short-term traffic flow prediction method based on integrated graph convolutional neural network

    LIU LUYANG / LYU SHUAIQI / BAO XU | European Patent Office | 2024

    Free access

    Short-term traffic flow prediction with LSTM recurrent neural network

    Kang, Danqing / Lv, Yisheng / Chen, Yuan-yuan | IEEE | 2017


    Short-Term Traffic Flow Prediction Based on Graph Convolutional Network Embedded LSTM

    Huang, Yanguo / Zhang, Shuo / Wen, Junlin et al. | TIBKAT | 2020