In the Melbourne metropolitan area in Australia, an average of 34 pedestrians were killed every year between 2004 and 2013 in traffic crashes, and vehicle–pedestrian crashes accounted for 24% of all fatal crashes. Mid-block crashes accounted for 46% of the total pedestrian crashes in the Melbourne metropolitan area and 49% of the pedestrian fatalities occurred at mid-blocks. This study developed three models using different decision trees (DTs) to identify the factors contributing to the severity of pedestrian crashes. To improve the accuracy, stability and robustness of the DTs, bagging and boosting techniques were used in this study. The results of this study showed that the boosting technique improved the accuracy of individual DT models by 46%. Moreover, the results of boosting DTs showed that neighbourhood social characteristics were as important as traffic and infrastructure variables in influencing the severity of pedestrian crashes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modelling pedestrian crash severity at mid-blocks


    Contributors:

    Published in:

    Publication date :

    2017-03-16


    Size :

    25 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    A Multinomial Logit Model of Pedestrian-Vehicle Crash Severity

    Tay, Richard / Choi, Jaisung / Kattan, Lina et al. | Taylor & Francis Verlag | 2011



    A Hierarchical Modeling Approach to Predict Pedestrian Crash Severity

    Jahangeer, Aafreen Asma / Anjana, Sai Suresh / Das, Vivek R. | Springer Verlag | 2019



    Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

    Siyab Ul Arifeen / Mujahid Ali / Elżbieta Macioszek | DOAJ | 2023

    Free access