Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method for drivers and transportation companies to ensure driving safety. Conventionally, the relationships between ADBs and accident-related records are analyzed when assessing driving risk. However, such records typically overlook driver responsibility for driving risks and depend considerably on the person producing the data (e.g., police officers or insurance managers). Foremost, conventional approaches do not consider non-accident situations that comprise most driving scenarios. Thus, we propose a novel driving risk assessment method that uses only ADB data. In this method, interpretable latent risk factors are extracted from ADB data via sparse non-negative matrix factorization (NMF), and then the driving risk score is computed on a scale of 0–100. The proposed method was validated by adopting a real-world application to assess the driving risk of bus drivers in South Korea and by conducting an evaluation performed by transportation experts in conjunction with the Korea Transportation Safety Authority. Results revealed that the proposed method can discriminate between high- and low-risk driving, thus providing clear guidelines to improve driving. Then, the proposed driving risk score assessment method using NMF was compared with existing machine learning-based risk assessment methods. The proposed method outperformed the conventional methods in terms of driving risk discrimination and interpretability. This study can provide risk assessment guidelines based on driving behavior records and contribute to the application of machine learning in transportation safety management.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records


    Beteiligte:
    Seo, Hyunwoo (Autor:in) / Shin, Jongkyung (Autor:in) / Kim, Ki-Hun (Autor:in) / Lim, Chiehyeon (Autor:in) / Bae, Jungcheol (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-11-01


    Format / Umfang :

    2603575 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Driving behavior risk assessment system

    XING GUANNAN | Europäisches Patentamt | 2021

    Freier Zugriff

    Risk Behavior in Driving

    Miller, Robert S. / Rockwell, T. H. | SAE Technical Papers | 1980



    Risk Behavior in Driving

    Rockwell,T.H. / Miller,R.S. / Ohio State Univ.,US | Kraftfahrwesen | 1980


    Max-margin Non-negative Matrix Factorization

    Kumar, B. G. / Kotsia, I. / Patras, I. | British Library Online Contents | 2012