Although Sri Lanka has significant traffic-related fatalities and injuries, only a limited number of studies have focused on the road safety challenge. This study applies the random parameters logit model to police reported crash data to identify the factors contributing to the severity of crashes in Sri Lanka. We find that severe crashes are associated with roadways that are unlit, in rural areas and have traffic controls, dual purpose vehicles, heavy vehicles, head-on and hit pedestrian crashes, drivers who are unlicensed, and casualties who are older (65 years and older) and do not use safety equipment, while minor crashes are associated with rear-end crashes, and female or younger (aged 25 years or younger) casualties. Several engineering, enforcement, and education measures are recommended based on the risk factors identified.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Factors Affecting Crash Severity on Two Major Intercity Roads in Western Sri Lanka: A Random Parameter Logit Approach


    Beteiligte:
    Asiri P. Senasinghe (Autor:in) / Alex de Barros (Autor:in) / S. C. Wirasinghe (Autor:in) / Richard Tay (Autor:in)


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    A Multinomial Logit Model of Pedestrian-Vehicle Crash Severity

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



    Bicyclist injury severity classification using a random parameter logit model

    Subasish Das / Reuben Tamakloe / Hamsa Zubaidi et al. | DOAJ | 2023

    Freier Zugriff


    Marginal Effects for Random Parameters Logit Models: A Case Study of Crash Severity Analysis

    Hou, Qinzhong / Mu, Songcheng / Zhang, Mengzhu | TIBKAT | 2022