To explore the factors of traffic accident-induced congestion in depth and analyze the influence degree of each factor precisely, this study proposes a framework for analyzing the causes of interrelated traffic accidents and congestion. An evaluation of the influencing factors is conducted by comparing six algorithms, including logistic regression, support vector machines, decision trees, random forests, neural networks, and naive Bayes methods. The traffic accident data in the United States and the traffic collision data in California are selected and combined into a traffic accident-induced congestion data set with a total of 30,911 observations in California from 2017 to 2020. The influencing factors are identified and analyzed based on the feature importance. The results indicate that the random forest model yields the highest prediction accuracy, and the cause analysis framework is proposed based on this model. Theoretical suggestions for congestion mitigation and measures for preventing and resolving accident-related congestion are discussed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Machine Learning Framework for Analyzing the Causes of Traffic Accident-Induced Congestion


    Contributors:
    Xu, Pengfei (author) / Zhang, Buhao (author) / Zeng, Jintao (author) / Song, Li (author)

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 2046-2055


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Machine Learning Based Traffic Congestion and Accident Prevention Analysis

    Sofia, A. Sathya / Selvi, C. P. Thamil / Suganya, S. et al. | Springer Verlag | 2024


    Traffic congestion -- Its causes and consequences

    Brunner, C.T. | Engineering Index Backfile | 1960



    Analyzing Freeway Traffic under Congestion: Traffic Dynamics Approach

    Nam, D. H. / Drew, D. R. | British Library Online Contents | 1998


    SYSTEMS AND METHODS FOR ANALYZING TRAFFIC CONGESTION

    ZHANG SHENZHEN / WANG YUANPING | European Patent Office | 2020

    Free access