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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multi-model traffic accident clearance time prediction framework


    Beteiligte:
    Yao, Xinwei (Herausgeber:in) / Kumar, Neeraj (Herausgeber:in) / Zhang, Anyi (Autor:in) / Wang, Qianqian (Autor:in) / Huang, Zhejun (Autor:in) / Yin, Jiyao (Autor:in) / Yang, Lili (Autor:in)

    Kongress:

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


    Erschienen in:

    Proc. SPIE ; 13160


    Erscheinungsdatum :

    16.05.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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

    WANG NIANMING / CHEN YANG / ZHOU MINGKE et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    A Two-Stage Sequential Framework for Traffic Accident Post-Impact Prediction Utilizing Real-Time Traffic, Weather, and Accident Data

    Amirhossein Abdi / Seyedehsan Seyedabrishami / Steve O’Hern | DOAJ | 2023

    Freier Zugriff

    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 | Europäisches Patentamt | 2025

    Freier Zugriff

    REAL-TIME TRAFFIC ACCIDENT PREDICTION AND RESPONSE METHOD

    KIM DUCK NYUNG / YEOM CHUN HO / PARK JUNE YOUNG | Europäisches Patentamt | 2024

    Freier Zugriff