The alarming increase in road accidents in recent years has elevated them to a significant global issue., making them the ninth leading cause of death worldwide. It is unfortunate and completely unacceptable that there have been fatalities because of these accidents. As a result., it is critical that this problem be addressed thoroughly. With the aid of cutting-edge machine learning techniques., the proposed study will analyze traffic incidents in detail. The main goal is to pinpoint the major causes of traffic collisions and offer insightful suggestions for reducing this issue. The goal of the study is to divide accident severity into three categories: fatal injury., serious injury., and minor injury. To do this., it makes use of a variety of supervised learning techniques., such as Decision Trees., Support Vector Machines (SVM)., Multinomial Naive Bayes., K-Nearest Neighbors (KNN)., Random Forests., XGBoost., MLPClassifiers., and AdaBoost. Notably., the Random Forest model excels with outstanding performance., obtaining a stunning 90% accuracy rate in successfully forecasting accident severity.


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    Title :

    Machine Learning Applications in Traffic Safety: Assessing Accident Severity Automatically


    Contributors:
    Priyanka, S (author) / Jayadharshini, P (author) / Santhiya, S (author) / Divyadharshini, B (author) / Samyuktha, K (author) / Madan, P (author)


    Publication date :

    2023-11-22


    Size :

    649717 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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