Traffic congestion and road accidents have been important public challenges that impose a big burden on society. It is important to understand the factors that contribute to traffic congestions and road accidents so that effective strategies can be implemented to improve the road condition. The analysis on traffic congestion and road accidents is very complex as they not only affect each other but are also affected by many other factors. In this research, we use the US Accidents data from Kaggle that consists of 4.2 million accident records from February 2016 to December 2020 with 49 variables for the study. We propose to use statistical techniques and machine learning algorithms that include Logistic Regression, Tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train a large amount of data to obtain predictive models for traffic congestion and road accidents. The proposed predictive models are expected to be more accurate by incorporating the impact of multiple environmental parameters. The proposed models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.


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    Titel :

    Accidents Analysis and Severity Prediction Using Machine Learning Algorithms


    Weitere Titelangaben:

    Lecture Notes in Operations res.


    Beteiligte:
    Qiu, Robin (Herausgeber:in) / Lyons, Kelly (Herausgeber:in) / Chen, Weiwei (Herausgeber:in) / Shetty, Rahul Ramachandra (Autor:in) / Liu, Hongrui (Autor:in)

    Kongress:

    INFORMS International Conference on Service Science ; 2021 ; Beijing, China August 10, 2021 - August 12, 2021



    Erscheinungsdatum :

    12.11.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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