Comprehending the variation in traffic flow is critical to alleviating traffic congestion at expressway toll station exits. Despite the fact that various traffic flow forecasting models have been proposed, most of them make predictions based on the entry traffic in the area near the target toll station. For origin–destination data like from entry to exit, these methods can hardly capture information on vehicles in transit. In this work, we suggest for the first time predicting toll station exit flows based on expressway gantry data. Moreover, in order to obtain the contribution of multiple gantry series to the exit traffic flow, a recurrent neural network incorporating spatio-temporal attention mechanism is proposed. The proposal not only predicts effectively but also improves the interpretability of the model. Comparative experiments were conducted using data from the gantry system and toll station data of the expressway in Fujian Province, China. The experimental results show that the proposed model performs better than other baseline methods.


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    Titel :

    Traffic Flow Prediction of Expressway Toll Station Exit Based on ETC Gantry Data and Attention Mechanism


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Ni, Shaoquan (Herausgeber:in) / Wu, Tsu-Yang (Herausgeber:in) / Geng, Jingchun (Herausgeber:in) / Chu, Shu-Chuan (Herausgeber:in) / Tsihrintzis, George A. (Herausgeber:in) / Wang, Haolin (Autor:in) / Zou, Fumin (Autor:in) / Guo, Feng (Autor:in)


    Erscheinungsdatum :

    15.05.2023


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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