Through the application and expansion of expressway ETC gantry transaction data, we propose a short-term traffic flow forecasting of expressway based on the Kalman Filtering (KF) and Random Forest (RF) model, which not only takes into account the basic external features and periodic features but also considers the spatio-temporal correlation relationship in the road section, so as to construct the spatial correlation features and temporal correlation features. In this paper, we use the ETC gantry transaction data of Fuzhou–Xiamen section of the expressway to forecast and verify in Fujian Province, China, the final results show that: When the rolling window is 20 min, compared with the results before and after Kalman Filtering algorithm processing traffic flow data, the performance indicators is greatly improved, which verifies the positive effect of Kalman Filtering algorithm; it is also verified that the constructed features have a great influence on traffic flow forecasting and play a positive role in improving forecasting accuracy; and it is also verified that the RF model has better forecasting effect than the baseline models.


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    Titel :

    Expressway Short-Term Traffic Flow Forecasting Considering Spatio-Temporal Features of ETC Gantry


    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) / Xu, Gen (Autor:in) / Zou, Fumin (Autor:in) / Tian, Junshan (Autor:in) / Guo, Feng (Autor:in) / Cai, Qiqin (Autor:in)


    Erscheinungsdatum :

    15.05.2023


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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