Accurate traffic accident clearance times prediction can help road managers make effective decisions and reduce property damage. This paper aims to develop a framework for traffic accident clearance time prediction and find the best prediction model. We propose a multi-model prediction framework for traffic accident severity. This framework consists of three parts: preprocessing of imbalanced data, variable selection and establishment of hybrid models: RF-SVM, RFBPNN, and RF-BN. Four highways in Shandong Province's traffic accident data are used as a case study in this paper. Based on the data used in this paper and previous literature exploration, three mixed models are constructed. Comparing the outcomes, we discover that the RF-SVM model has the highest prediction accuracy, up to 0.98, for the oversampled data set. This framework can be used to forecast the clearance time for traffic accidents, allowing for prompt emergency response and a reduction in fatalities and property damage.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-model traffic accident clearance time prediction framework


    Contributors:
    Yao, Xinwei (editor) / Kumar, Neeraj (editor) / Zhang, Anyi (author) / Wang, Qianqian (author) / Huang, Zhejun (author) / Yin, Jiyao (author) / Yang, Lili (author)

    Conference:

    Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) ; 2024 ; Beijin, China


    Published in:

    Proc. SPIE ; 13160


    Publication date :

    2024-05-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Training method of traffic accident prediction model and traffic accident prediction method and device

    WANG NIANMING / CHEN YANG / ZHOU MINGKE et al. | European Patent Office | 2024

    Free access


    Traffic Accident Prediction based on CNN Model

    Thaduri, Amani / Polepally, Vijayakumar / Vodithala, Swathy | IEEE | 2021


    Tunnel traffic flow and accident prediction model

    ZHENG QI / LI ZHIYANG / WANG PENGHUI | European Patent Office | 2025

    Free access

    REAL-TIME TRAFFIC ACCIDENT PREDICTION AND RESPONSE METHOD

    KIM DUCK NYUNG / YEOM CHUN HO / PARK JUNE YOUNG | European Patent Office | 2024

    Free access