Work zones are a high priority issue in the field of road transportation because of their impacts on traffic safety. A better understanding of work zone crashes can help to identify the contributing factors and countermeasures to enhance roadway safety. This study investigates the prediction of work zone crash severity and the contributing factors by employing a parametric approach using the mixed logit modeling framework and a non-parametric machine learning approach using the support vector machine (SVM). The mixed logit model belongs to the class of random parameter models in which the effects of flexible variables across different observations are identified, that is, data heterogeneity is taken into account. The performance of the SVM model is enhanced by applying three metaheuristic algorithms: particle swarm optimization (PSO), harmony search (HS), and the whale optimization algorithm (WOA). Empirical findings indicate that SVM provides higher prediction accuracy and outperforms the mixed logit model. Estimation results reveal key factors that increase the likelihood of severe work zone crashes. Furthermore, the analysis illustrates the ability of the three metaheuristics to enhance the SVM and the superiority of the harmony search algorithm in improving the performance of the SVM model.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Improved Support Vector Machine Models for Work Zone Crash Injury Severity Prediction and Analysis


    Additional title:

    Transportation Research Record




    Publication date :

    2019-06-06




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Using support vector machine models for crash injury severity analysis

    Li, Zhibin / Liu, Pan / Wang, Wei et al. | Elsevier | 2011



    Crash Injury Severity Analysis Using Bayesian Ordered Probit Models

    Xie, Y. / Zhang, Y. / Liang, F. | British Library Online Contents | 2009


    A literature review of machine learning algorithms for crash injury severity prediction

    Santos, Kenny / Dias, João P. / Amado, Conceição | Elsevier | 2021