Road travel accounts for most traffic accidents that have caused injuries, death, and property damage world-wide. Improvements in road traffic safety education, recent advancements in-vehicle technology, and other environmental factors have decreased the number of road traffic accidents in developed nations. Many provincial and local governments envision the possibility of zero fatalities from road traffic accidents in the near future. Developing a proper accident prediction model to support such a vision is crucial. This study explores determinants of road collisions, emphasizing harsh winter weather. It then compares classical and Machine Learning models for collision prediction. Furthermore, it introduces the most influential factors in crashes concerning severe winter weather. All study parts are performed on the collisions data in Calgary, Alberta, Canada, between 2017 to 2020. It is shown that all the weather attributes are correlated to collisions. It shows the importance of considering weather attributes in accident analysis and prediction. Based on the nature of the collision dataset, which is tabular and heterogeneous, Neural Networks showed higher performances than the other investigated models, with 92% accuracy. The developed models would allow transportation planners to apply these models for evidence-based policy implementation, including new speed limit recommendations.


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    Titel :

    Predicting Road Accidents Using Machine Learning Models


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    487886 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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