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International Journal of Automotive Technology > Volume 26(1); 2025 > Article
International Journal of Automotive Technology 2025;26(1): 1-12.
doi: https://doi.org/10.1007/s12239-024-00070-2
Driver Behavior Analysis in Simulated Jaywalking and Accident Prediction Using Machine Learning Algorithms
Myeongkyu Lee1, Jihun Choi2, Songhui Kim2, Ji Hyun Yang3
1School of Industrial Engineering, Purdue University, West Lafayette, IN, 47906, USA
2Traffic Accident Division, National Forensic Service, Wonju, 26460, Korea
3Department of Automotive Engineering, Kookmin University, Seoul, 02707, Korea
PDF Links Corresponding Author.  Ji Hyun Yang , Email. yangjh@kookmin.ac.kr
Received: December 20, 2022; Revised: March 22, 2023   Accepted: January 15, 2024.  Published online: April 24, 2024.
ABSTRACT
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.
Key Words: Accident analysis · Classifi cation · Driver behavior characteristic · Prediction
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