Identification of Safety Risk in Freeway and Impact Factors Based on an Interpretable Machine Learning Framework

DU Jian; YANG Haiyi; LI Yang; GUO Miao; QI Hang; WEI Jinqiang; MA Hao; HU Dandan; LI Zhiyu

doi:10.3963/j.jssn.1674-4861.2023.05.003

Volume 41 Issue 5

Oct. 2023

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Article Navigation > Journal of Transport Information and Safety > 2023 > 41(5): 24-34

DU Jian, YANG Haiyi, LI Yang, GUO Miao, QI Hang, WEI Jinqiang, MA Hao, HU Dandan, LI Zhiyu. Identification of Safety Risk in Freeway and Impact Factors Based on an Interpretable Machine Learning Framework[J]. Journal of Transport Information and Safety, 2023, 41(5): 24-34. doi: 10.3963/j.jssn.1674-4861.2023.05.003

Citation:

DU Jian, YANG Haiyi, LI Yang, GUO Miao, QI Hang, WEI Jinqiang, MA Hao, HU Dandan, LI Zhiyu. Identification of Safety Risk in Freeway and Impact Factors Based on an Interpretable Machine Learning Framework[J]. Journal of Transport Information and Safety, 2023, 41(5): 24-34. doi: 10.3963/j.jssn.1674-4861.2023.05.003

Citation:

DU Jian, YANG Haiyi, LI Yang, GUO Miao, QI Hang, WEI Jinqiang, MA Hao, HU Dandan, LI Zhiyu. Identification of Safety Risk in Freeway and Impact Factors Based on an Interpretable Machine Learning Framework[J]. Journal of Transport Information and Safety, 2023, 41(5): 24-34. doi: 10.3963/j.jssn.1674-4861.2023.05.003

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Identification of Safety Risk in Freeway and Impact Factors Based on an Interpretable Machine Learning Framework

doi: 10.3963/j.jssn.1674-4861.2023.05.003

DU Jian^1
,,
YANG Haiyi²,
LI Yang^{3
,
,},
GUO Miao²,
QI Hang²,
WEI Jinqiang⁴,
MA Hao¹,
HU Dandan⁴,
LI Zhiyu⁴

1.
China Merchants New Intelligence Technology Co., Ltd., Beijing 100160, China
2.
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
3.
Department of Road Traffic Management, Beijing Police College, Beijing 102202, China
4.
Zhejiang Wenzhou Yongtaiwen Expressway Co., Ltd, Wenzhou 325036, Zhejiang, China

Received Date: 2022-05-30
Available Online: 2024-01-18

Abstract

Abstract

Traffic accidents, being random events with low-probability, pose challenges for traffic safety analysis in the comprehensively temporal and spatial perspective, which hinders the proactive effective prevention and control strategies before accidents occur. To this end, this paper aims to identify the safety risk and underlying mechanism under various factors. Specifically, data about aggressive driving behavior and speed variation coefficients are used to calculate traffic order index (TOI) to further form accident proxies. TOI are classified into three traffic safety risk levels by K-means clustering algorithm. The correlations of traffic flow characteristics, weather conditions, road conditions, and other factors with traffic safety risk are established using the Catboost algorithm. Based on the feature importance of Gini coefficient, elements contributing to safety risk of highway traffic are identified. Next, the partial dependency plots algorithm is utilized to analyze the dependency relationship and marginal effect between risk factors and traffic safety risk. The results indicate that: ① The Catboost algorithm exhibits high model fitness in identifying risk levels with accuracy, precision, and recall rates equaling 85.95%, 88.56%, and 86.75%, respectively, which confirms the robust correlation of TOI with external risk factors. ② Traffic flow and congestion can significantly influence risk identification, displaying a nonlinear relationship with traffic safety risk levels. Notably, when traffic flow exceeds 450 veh/h or the congestion index surpasses 1.5, traffic safety risk would substantially increase by 16.9% and 29.5%, respectively. ③ When there are 1 or 2 traffic signs within 1km of consecutive roadway, with a 38.1% likelihood of being identified as high-risk areas. Additionally, ramp entrances, exits, and roads inside the tunnel are identified as locations with the highest traffic safety risk. ④ The impact of lateral wind on traffic safety risk is relatively minor. However, as the wind level increases from 0 to 5, traffic safety risk increases by 4.99%.
- traffic safety,
- highways,
- risk identification and impact factor mining,
- partial dependence plot,
- machine learning models

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References(38)

References

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