Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data.


    Access

    Download


    Export, share and cite



    Title :

    Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics


    Contributors:
    Liping Zhao (author) / Feng Li (author) / Dongye Sun (author) / Fei Dai (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






    Spatio-temporal study of road traffic crash on a national highway of Bangladesh

    Newaz, Kazi Md Shifun / Hasanat-E-Rabbi, Shahnewaz / Miaji, Sohag | IEEE | 2017


    Traffic Violations and the Highway Crash: The Etiology of High Risk Drivers

    Nitz, L. H. / Kim, K. E. / Leong, E. et al. | British Library Conference Proceedings | 1996