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 |
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Received: December 20, 2022; Revised: March 22, 2023 Accepted: January 15, 2024. Published online: April 24, 2024. |
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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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