Study on Accident Risk Based on Driving Behavior and Traffic Operating Status

被引：1
|
作者：
Guo M. [1 ,2 ]
Zhao X. [1 ,2 ]
Yao Y. [1 ,2 ]
Wu D. [3 ]
Su Y. [4 ]
Bi C. [4 ]
机构：
[1] Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing
[2] Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing
[3] China Merchants New Intelligence Technology Co. Ltd., Beijing
[4] Future Transportation Research Center of Amap, AutoNavi Software Co. Ltd., Beijing
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2022年 / 50卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Accident risk detection; Driving behavior; Freeway; Traffic operating state; Traffic safety;
D O I：
10.12141/j.issn.1000-565X.210629
中图分类号：
学科分类号：
摘要：
Accurate identification of traffic accident risk and timely mastering the change of traffic crash risk are of great significance for proactive prevention and reduction of traffic accident. Most of the existing traffic crash identification studies are based on real-time and dynamic parameters such as traffic flow and traffic conflict. The application of risky driving behavior in traffic accident risk detection is limited by the constraints of previous data acquisition technologies. To more accurately identify the risk of road traffic crashes, this study introduced risky driving behavior and traffic operating status and other big data, and extracted sharp acceleration, deceleration, turns, merge into other lane, traffic volume, average speed, and congestion index as variables. And traffic accident identification models were constructed based on accident data. The traffic accident identification model was evaluated based on the logistic regression algorithm. On the one hand, the contribution of risky driving behavior in traffic accident identification was quantified; on the other hand, the trend of traffic accident occurrence probability before and after the accident was analyzed. The results show that the sensitivity and AUC values of the traffic accident identification model considering both traffic operation state and risky driving behavior are increased by 5.00% and 0.03, respectively. The false alarm rate and missing report rate are decreased by 1.78% and 5.00%, respectively, which shows better fitting effect of the model. In addition, before and after the occurrence of traffic accidents, the risk probability of traffic crashes shows an obvious trend of rise, which is the key period of traffic accident prevention and control. The measures should be taken in corresponding sections in time to curb the rising trend of traffic crash risk and avoid the occurrence of traffic crashes. This study can provide intuitive basis for traffic accident prevention, and active prevention and control. © 2022, Editorial Department, Journal of South China University of Technology. All right reserved.
引用
收藏
页码：29 / 38
页数：9
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