Road safety can be improved if traffic accidents can be predicted and thus prevented. The use of driver-related variables to determine the possibility of an accident presents a new analysis paradigm. We used a driving simulator to create a jaywalking scenario and investigated how drivers responded to it. A total of 155 valid participants were identified across demographics (age group and gender) and participated in the experiment. We collected driver-related data on eight types of perception/reaction times, vehicle-control data, accident occurrence data, and maneuvers used for obstacle avoidance. From the statistical analysis, it was possible to derive six variables with significant differences based on whether a traffic accident occurred. Furthermore, we identified the data’s significant difference according to demographics. Artificial intelligence (AI)-classification models were used to predict whether an accident would occur with up to 90.6% accuracy. The data associated with the dangerous scenario obtained in this study were identified to predict the occurrence of traffic accidents.


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    Title :

    Driver Behavior Analysis in Simulated Jaywalking and Accident Prediction Using Machine Learning Algorithms


    Additional title:

    Int.J Automot. Technol.


    Contributors:
    Lee, Myeongkyu (author) / Choi, Jihun (author) / Kim, Songhui (author) / Yang, Ji Hyun (author)


    Publication date :

    2025-02-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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