With the rapid development of domestic expressway ETC system, the trend of intelligent and digital for expressway management began to mature, which provides a solid foundation for the fully connected vehicle–road-cloud intelligent perception and collaborative decision-making system. At present, as one of the largest Internet of Vehicles (IOV) in the world, ETC system can provides multi-dimensional data for expressway and covers the vehicle information of each road sections. Among them, the traffic congestion identification plays an important role for vehicle collaborative decision-making and it has tremendous research value. In order to improve the accuracy and stability of traffic congestion identification, this paper proposes a Fuzzy Comprehensive Evaluation Adaptive Matching Algorithm (FACM) by deeply mining the dimensional information of expressway ETC transaction data. This method introduces the Section Portrait into dimensional analysis, and uses Analytic Hierarchy Process (AHP) to weighted average the dimensions of each section, combined with Fuzzy Comprehensive Evaluation (FCE), the level division of section congestion is carried out. The experimental results show that the average congestion recognition accuracy of FCAM is 98.03%, which is 4.98% and 3.07% higher than FCE method and K-means method respectively. The proposed method has high stability and high recognition rate.


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    Title :

    A Method of Expressway Congestion Identification Based on the Electronic Toll Collection Data


    Additional title:

    Smart Innovation, Systems and Technologies


    Contributors:
    Ni, Shaoquan (editor) / Wu, Tsu-Yang (editor) / Geng, Jingchun (editor) / Chu, Shu-Chuan (editor) / Tsihrintzis, George A. (editor) / Lin, Ziyang (author) / Zou, Fumin (author) / Guo, Feng (author) / Yu, Xiang (author) / Li, Nan (author)


    Publication date :

    2023-05-15


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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