Highlights Traffic accidents are a common danger everyone faces. Different ML models with different creation methods are used to predict accidents. Generic model with good performance and easy data collection. Emergency responders benefit from shorter response times. Local governments benefit from prescribed roadway changes.

    Abstract Given the ever present threat of vehicular accident occurrence endangering the lives of most people, preventative measures need to be taken to combat vehicle accident occurrence. From dangerous weather to hazardous roadway conditions, there are a high number of factors to consider when studying accident occurrence. To combat this issue, we propose a method using a multilayer perceptron model to predict where accident hotspots are for any given day in the city of Chattanooga, TN. This model analyzes accidents and their associated weather and roadway geometrics to understand the causes of accident occurrence. The model is offered as a live service to local law enforcement and emergency response services to better allocate resources and reduce response times for accident occurrence. Multiple models were made, each having different variables present, and each yielding varying results.


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    Titel :

    Modeling and predicting vehicle accident occurrence in Chattanooga, Tennessee


    Beteiligte:
    Roland, Jeremiah (Autor:in) / Way, Peter D. (Autor:in) / Firat, Connor (Autor:in) / Doan, Thanh-Nam (Autor:in) / Sartipi, Mina (Autor:in)


    Erscheinungsdatum :

    2020-10-29




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch