Traffic state information is critical for drivers’ route choice and traffic management, numerous studies targeted on traffic state evaluation for expressways exist. Few studies have conducted traffic state prediction for expressways, and the congestion mechanism has not been explored comprehensively. This study aims to conduct traffic state prediction and comprehensively explore the congestion mechanism. Section volume and space mean speed were collected by Closed Circuit Television. Before modeling, a variables selection method including random forest and correlation analysis was proposed to solve the huge dimension scale in modeling. An ordered logit model was built, which considers the traffic states’ sequence properties, to quantitatively analyze the impact of congestion contributing factors and predict the appearance probabilities of traffic states. The Support Vector Machine model was applied in traffic state prediction for comparison. The results revealed the appearance probabilities of traffic states are significantly influenced by traffic conditions and spatial–temporal variations.


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    Title :

    Real-Time Traffic State Prediction and Congestion Mechanism Analysis for Expressways


    Contributors:
    Wang, Kang (author) / Wang, Ling (author) / Ma, Wanjing (author)

    Conference:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Published in:

    CICTP 2022 ; 513-521


    Publication date :

    2022-09-08




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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