A structure optimization algorithm for developing an artificial neural network (ANN) for predicting crash injury severity has been proposed in this study to improve its generalization capacity Two-vehicle crash records in 2006 from the Florida Department of Highway Safety and Motor Vehicles (DHSMV) have been collected in order to demonstrate the proposed method and to compare it with a traditional statistical model - an ordered logit (OL) model. According to the results, both the trained and optimized ANN models outperform the OL model in terms of predictive accuracy, which again displays its great potential for modeling crash injury severity. Although the accuracy of the training and testing dataset are equivalent in the trained and optimized ANN models in the case study, 33 of 210 connections in the trained ANN model have been deleted by the optimization algorithm. The computational burden of prediction has been reduced, which indicates the optimized ANN model to be a good alternative for crash injury severity analysis.


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    Title :

    Developing an Optimized Artificial Neural Network to Predict Traffic Crash Injury Severity


    Contributors:
    Zeng, Qiang (author) / Huang, He-lai (author) / Xu, Peng-peng (author) / Ma, Ming (author)

    Conference:

    14th COTA International Conference of Transportation Professionals ; 2014 ; Changsha, China


    Published in:

    CICTP 2014 ; 2396-2407


    Publication date :

    2014-06-24




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English