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http://dx.doi.org/10.12815/kits.2022.21.6.132

Classification and Prediction of Highway Accident Characteristics Using Vehicle Black Box Data  

Junhan Cho (Samsung Traffic Safety Research Institute)
Sungjun Lee (Dept. of Transportation and Logistics Eng., Hanyang University)
Seongmin Park (Dept. of Transportation and Logistics Eng., Hanyang University)
Juneyoung Park ( Dept. of Transportation and Logistics Eng.Smart City Engineering., Hanyang University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.6, 2022 , pp. 132-145 More about this Journal
Abstract
This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.
Keywords
Black box; Highway; Accident severity; Cluster analysis; Heterogeneity;
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