收稿日期: 2021-09-28
网络出版日期: 2022-05-03
基金资助
国家自然科学基金资助项目(61672067)
Study on Accident Risk Based on Driving Behavior and Traffic Operating Status
Received date: 2021-09-28
Online published: 2022-05-03
Supported by
the National Natural Science Foundation of China(61672067)
准确识别交通事故风险和及时掌握交通事故风险的变化对于交通事故的主动防控和减少交通事故的发生具有重要意义。现有的交通事故风险识别研究大多基于交通流、交通冲突等实时、动态参数，同时受以往数据采集技术的制约，风险驾驶行为在交通事故风险识别研究中的应用受到限制。为了更加准确的识别道路交通事故风险，本研究引入风险驾驶行为和交通流等大数据，提取急加速、急减速、急转弯、急并道以及交通流量、平均速度、拥堵指数等变量，结合事故数据构建交通事故风险识别模型。基于逻辑回归算法计算交通事故发生概率，对交通事故识别模型进行评价，一方面量化风险驾驶行为在交通事故风险识别中的贡献，另一方面分析事故发生前后，交通事故发生概率的变化趋势。研究结果表明，同时考虑交通运行状态和风险驾驶行为的交通事故风险识别模型的敏感度和AUC值分别提高5.00%和0.03，误报率和漏报率分别降低1.78%和5.00%，模型的拟合效果更好。此外，在交通事故发生前后，交通事故风险概率呈现明显上升趋势，是交通事故防控的重点时段，应在相应的路段及时采取防控措施降低交通事故风险的概率，避免发生交通事故。本研究可为交通事故的预防预警以及主动防控提供直观的依据。
郭淼, 赵晓华, 姚莹, 等 . 基于驾驶行为和交通运行状态的事故风险研究[J]. 华南理工大学学报(自然科学版), 2022 , 50(9) : 29 -38 . DOI: 10.12141/j.issn.1000-565X.210629
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.
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