Understanding the non-linear relationship between traffic injury severity and factors in accuracy can help decrease accident occurrence and improve driving safety. This paper uses a GA-BP neural network to model the relationship and predict injury severity in traffic accidents classified into fatality, serious crash, and slight crash. And it validates the superior performance of GA-BP with crash data from the UK in 2015, compared to the BP neural network and the logistic regression model. A sensitivity analysis is applied to find out the contribution that input variables have on injury severity. This paper indicates that the GA-BP neural network provides a reference for injury severity prediction in traffic accident.


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    Title :

    A Model of Injury Severity Prediction in Traffic Accident Based on GA-BP Neural Network


    Contributors:
    Wang, Shuang (author) / Wei, Chong (author) / Wei, Yansha (author) / Wang, Wenzhe (author) / Wu, Fei (author)

    Conference:

    19th COTA International Conference of Transportation Professionals ; 2019 ; Nanjing, China


    Published in:

    CICTP 2019 ; 2470-2481


    Publication date :

    2019-07-02




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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