In order to study the severity of highway accidents and deeply explore their influencing factors. Collected typical highway accident data and traffic flow data in Henan Province, constructed Logit and mixed Logit models based on single vehicle accidents and multi vehicle accidents, and selected the optimal model; Establish random forest and Gradient Boosting Decision Trees, build a prediction model of expressway accident severity, screen the characteristics of expressway accident severity to obtain the importance ranking of influencing factors, and select the machine learning algorithm suitable for this study through the model prediction accuracy test; Compare and analyze the two optimal models using the K-fold cross test method, and analyze the core influencing factors selected from the optimal models based on the backward elimination. The results indicate that the mixed Logit model to some extent solves the limitations of the Logit model in analyzing factors affecting the severity of accidents; Machine learning algorithms have higher prediction accuracy.


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    Titel :

    Analysis of Factors Influencing the Severity of Expressway Traffic Accidents and Research on Improvement Measures


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Guo, Hongwei (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Shi, Jian (Herausgeber:in) / Sun, Dongxian (Herausgeber:in) / Guo, Wanjiang (Autor:in) / Yao, Jingxuan (Autor:in) / Li, Mei (Autor:in) / Zhao, Jianyou (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2022 ; Qinghuangdao, China September 16, 2022 - September 18, 2022



    Erscheinungsdatum :

    29.09.2024


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch