The proportion of rear-end crashes is the highest for expressways. An effective ways to reduce the rear-end crash risk is Active Traffic Management (ATM), and knowing the mechanism of how contributing factors affect crash risk in space and time is the foundation of ATM. However, the existing studies are mainly based on highly aggregated traffic data. It is hard to capture the evolution mechanisms of crash risk. Meanwhile, crash risk mechanisms might be heterogeneous in smooth and congestion states. This study explored the crash mechanisms in different traffic states with high-resolution trajectory data. First, an ordered clustering method is used to divide a four km expressway section into several segments as the spatial unit. Second, the spatial–temporal ranges are decided by the spatial–temporal correlations analyses between crash risk and potential contributing factors. Thirdly, three types of time-series models are established to quantitatively obtain the impacts of contributing factors on the crash risks. The results showed that crash risk is mainly decided by the upstream contributing factors under smooth states, and determined by the downstream factors under congestion conditions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Expressway rear-end crash risk evolution mechanism analysis under different traffic states


    Contributors:

    Published in:

    Publication date :

    2023-12-31


    Size :

    18 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Sensitivity Analysis of Expressway Traffic Safety Risk

    Wang, Jian-jun / Chen, Fa-an | ASCE | 2012


    Foggy day expressway traffic safety risk algorithm and foggy day expressway traffic safety risk system

    YIN BOHUA / YUAN YUEMING / HUAN HUAN | European Patent Office | 2023

    Free access



    A Hybrid Latent Class Analysis Modeling Approach to Analyze Urban Expressway Crash Risk

    Yu, Rongjie / Wang, Xuesong / Abdel-Aty, Mohamed | Elsevier | 2017