The TrafficRiskClassifier model proposed in this study adopts an innovative approach integrating migration learning, image classification, and self-supervised learning, with the goal of significantly enhancing the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data from traffic accidents to achieve higher risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model efficiently extracts pivotal features from complex traffic scenarios and forms initial risk assessments. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through analyzing various polynomial functions, the model achieves improved accuracy in classifying different risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.


    Access

    Download


    Export, share and cite



    Title :

    Classification of traffic accidents’ factors using TrafficRiskClassifier




    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Classification of traffic accidents’ factors using TrafficRiskClassifier

    Sun, Wei / Abdullah, Lili Nurliyana / Khalid, Fatimah binti et al. | Elsevier | 2025

    Free access

    Factors which contribute to traffic accidents

    Wright, Paul H. / Baker, E. Jo | Taylor & Francis Verlag | 1976


    Traffic Accidents Classification and Injury Severity Prediction

    Cuenca, Laura Garcia / Puertas, Enrique / Aliane, Nourdine et al. | IEEE | 2018


    Modeling traffic accidents in Saudi Arabia using classification techniques

    Al-Turaiki, Isra / Aloumi, Maryam / Aloumi, Nour et al. | IEEE | 2016


    Risk factors in urban road traffic accidents

    Vorko-Jovi, Ariana | Online Contents | 2006