Real-time traffic crash prediction is very important to traffic areas because it can help take active intervention actions before traffic accidents happen, and hence reduce accident rates. This study aims to use ensemble learning methods to improve the real-time traffic crash prediction accuracy by applying them to build models based on traffic flow data with crash occurrence labels collected from the Shanghai urban expressway in April and May of 2014. We applied four ensemble methods to build models, and used AUC, F-measure, and other evaluating indicators to measure the classification result of these models. It was concluded that the best ensemble learning model based on decision tree and logistic regression are random forest and LogitBoost, respectively. Their AUC measure can reach to 0.703 and 0.733, which are 7.99% and 4.12% higher than individual learners.


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    Titel :

    Utilizing Ensemble Learning Methods in Real-Time Traffic Crash Prediction


    Beteiligte:
    Xue, Mengdi (Autor:in) / Huang, Jie (Autor:in) / Gao, Zhen (Autor:in) / Sun, Ping (Autor:in)

    Kongress:

    18th COTA International Conference of Transportation Professionals ; 2018 ; Beijing, China


    Erschienen in:

    CICTP 2018 ; 2179-2185


    Erscheinungsdatum :

    02.07.2018




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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