A Method for Predicting the Type and Severity of Freeway Accidents Based on XGBoost

GAO Xuelin; TANG Houjun; SHEN Jiaping; XU Chengcheng; ZHANG Yujie

doi:10.3963/j.jssn.1674-4861.2023.04.006

Volume 41 Issue 4

Aug. 2023

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Article Navigation > Journal of Transport Information and Safety > 2023 > 41(4): 55-63

GAO Xuelin, TANG Houjun, SHEN Jiaping, XU Chengcheng, ZHANG Yujie. A Method for Predicting the Type and Severity of Freeway Accidents Based on XGBoost[J]. Journal of Transport Information and Safety, 2023, 41(4): 55-63. doi: 10.3963/j.jssn.1674-4861.2023.04.006

Citation:

GAO Xuelin, TANG Houjun, SHEN Jiaping, XU Chengcheng, ZHANG Yujie. A Method for Predicting the Type and Severity of Freeway Accidents Based on XGBoost[J]. Journal of Transport Information and Safety, 2023, 41(4): 55-63. doi: 10.3963/j.jssn.1674-4861.2023.04.006

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A Method for Predicting the Type and Severity of Freeway Accidents Based on XGBoost

doi: 10.3963/j.jssn.1674-4861.2023.04.006

1.
School of Transportation, Southeast University, Nanjing 211189, China
2.
Intelligent Transportation Research Branch of Zhejiang Transportation Investment Group Co., Ltd., Hangzhou 310000, China
3.
Information center of Zhejiang Expressway Co., Ltd., Hangzhou 310000, China

Received Date: 2022-08-12
Available Online: 2023-11-23

Abstract

Abstract

Freeway accidents are frequent, and previous studies have failed to adequately reveal the effect of dynamic traffic flow on accident type and severity. This study focuses on a prediction method for types and severity of freeway accidents based on real-time traffic flow data. Traffic flow characteristics, including volume, density, and speed, are extracted from freeway gantry data. Simultaneously, temporal features and spatiotemporal non-uniformity features are considered. These data are then matched with accident data to constitute the full dataset for modeling. The model based on the extreme gradient boosting tree (XGBoost) algorithm is developed to predict the occurrence of accidents and accident types, and also to assess accident severity. Two types of accidents (i.e., rear-end collisions and other types of accidents) are considered and two levels of accident severity (i.e., injury or fatal accidents and proper-ty-damage-only accidents) are distinguished. The results indicate that: ①a higher risk of traffic accidents is associated with significant speed difference between upstream and downstream traffic, low speeds, high traffic volumes with frequent merging and diverging conditions; ②rear-end accidents are more likely to occur in situations with lower speeds, high traffic volumes with merging and diverging flows, and significant speed difference between upstream and downstream traffic; ③accidents involving rear-end collisions may result in higher severity when they occur on road segments with lower traffic volumes or occur during weekends or nighttime. The Area Under Curve (AUC) of the XGBoost-based models for accident types prediction and accident severity prediction reached 0.76 and 0.88 respectively. Compared with other commonly used algorithms such as Sequential Logistic, Gaussian Naive Bayes, Linear Support Vector Machine (SVM), Random Forest, and Neural Network, the XGBoost-based model demonstrates an average improvement of 0.08 and 0.24 in AUC values for predictions of accident types and accident severity. These results indicate that the XGBoost-based model exhibits better prediction performance. The research findings provide a reliable way for state warning of real-time traffic flow on freeway segments, which could be useful for improving driving safety.
- traffic safety,
- freeway,
- prediction of accident types,
- prediction of accident severity,
- XGBoost

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References(26)

References

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