Investigating the cost-implications of road traffic collision factors is an important endeavour that has a direct impact on the economy, transport policies, cities and nations around the world. A Bayesian network framework model was developed using real-life road traffic collision data and expert knowledge to assess the cost of road traffic collisions. Findings of this study suggest that the framework is a promising approach for assessing the cost-implications associated with road traffic collisions. Moreover, adopting this framework with other computational intelligence approaches would have a positive impact towards achieving the Sustainable Development Goals in terms of road safety.


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    Titel :

    Bayesian Network-Based Framework for Cost-Implication Assessment of Road Traffic Collisions


    Weitere Titelangaben:

    Int. J. ITS Res.


    Beteiligte:
    Makaba, Tebogo (Autor:in) / Doorsamy, Wesley (Autor:in) / Paul, Babu Sena (Autor:in)


    Erscheinungsdatum :

    2021-04-01


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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