Crash risk evaluation studies mainly established the relationship between the macro traffic status and crashes. However, the impact of risky driving behavior, a significant factor in crashes, has not been thoroughly investigated due to the data collection limitations of fixed detectors. In this study, the risky driving behavior data generated by Connected Vehicle (CV) techniques was introduced along with traffic flow data to develop the crash risk evaluation model. An LSTM + Transformer approach was developed, in which the Transformer could extract the non-aggregated spatial-temporal features of risky driving behaviors and LSTM learn the temporal patterns of traffic flow. An ensemble layer was proposed to integrate the macro traffic status features and micro driving behavior, and automatically fit their weights to optimize crash risk evaluation performance. Data from a Chinese freeway was used for empirical analysis. The results show that the proposed LSTM + Transformer model achieved high model accuracy (77.7%), recall (68.6%), and AUC (0.785), with average improvement of between 5.34%, 15.69%, and 5.97%, respectively, compared to existing LSTM, XGBoost, SVM and Logistic Regression (LR) models. Moreover, utilizing risky driving behavior data by incorporating the macro traffic status has proved to capture the pre-crash traffic flow turbulence more precisely. The model results explained by SHapley Additive exPlanations (SHAP) reveal that higher frequency, longer duration and greater acceleration of risky braking behavior increase the number of road vehicles affected, thereby heightening the crash risks. These findings could help the deployment of proactive traffic management and target CV control strategies to reduce crashes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    LSTM + Transformer Real-Time Crash Risk Evaluation Using Traffic Flow and Risky Driving Behavior Data


    Contributors:
    Han, Lei (author) / Abdel-Aty, Mohamed (author) / Yu, Rongjie (author) / Wang, Chenzhu (author)

    Published in:

    Publication date :

    2024-11-01


    Size :

    1928101 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Investigating the impacts of driver’s risky driving behavior on traffic crash risk detection model

    Guo, Miao / Zhao, Xiaohua / Yao, Ying et al. | Taylor & Francis Verlag | 2024


    Research on risky driving behavior evaluation model based on CIDAS real data

    Hu, Lin / Bao, Xingqian / Lin, Miao et al. | SAGE Publications | 2021



    Identifying Young Driver Subtypes: Relationship to Risky Driving and Crash Involvement

    Wundersitz, L. / Burns, N. | British Library Conference Proceedings | 2005


    UMTRI Briefs Risky Driving Behavior

    British Library Online Contents | 2004