交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (6): 316-325.DOI: 10.16097/j.cnki.1009-6744.2024.06.028

• 工程应用与案例分析 • 上一篇    下一篇

基于驾驶风格的山区公路穿村镇段行车风险场灵敏度分析

戢晓峰a,b,王健a,b,徐迎豪a,b,卢梦媛a,b,覃文文*a,b   

  1. 昆明理工大学,a.交通工程学院;b.云南省现代物流工程研究中心,昆明650504
  • 收稿日期:2024-07-17 修回日期:2024-08-03 接受日期:2024-08-13 出版日期:2024-12-25 发布日期:2024-12-18
  • 作者简介:戢晓峰(1982- ),男,湖北随州人,教授,博士。
  • 基金资助:
    国家自然科学基金 (52062024);云南省交通运输厅科技创新及示范项目(2023-83(II));云南省基础研究计划面上项目 (202401AT070309)。

Driving Style-based Sensitivity Analysis of Driving Risk Field in Mountain Highway Sections Passing Through Villages and Towns

JI Xiaofenga,b,WANG Jiana,b,XU Yinghaoa,b,LU Mengyuana,b,QIN Wenwen*a,b   

  1. a. Faculty of Transportation Engineering; b. Yunnan Modern Logistics Engineering Research Center, Kunming University of Science and Technology, Kunming 650504, China
  • Received:2024-07-17 Revised:2024-08-03 Accepted:2024-08-13 Online:2024-12-25 Published:2024-12-18
  • Supported by:
    NationalNaturalScienceFoundation of China (52062024);Yunnan Province Transportation Science and Technology Innovation Demonstration Research Project (2023-83(II));YunnanProvincialBasicResearch Program General Project (202401AT070309)。

摘要: 针对山区公路穿村镇段交通事故多发的现实问题,本文提出一种考虑驾驶风格的行车风险识别方法,通过灵敏度分析揭示驾驶风格在风险评估中的影响,并以云南省典型山区公路穿村镇段为实例进行验证。首先,利用无人机采集车辆轨迹数据,建立轨迹分析数据库;其次,基于行车风险场理论并通过事故数据的相关性分析,刻画穿村镇段行车环境对车辆行驶的风险影响,进一步引入驾驶风格因子,建立考虑驾驶风格的行车风险场,实现多要素的综合考虑与行车风险量化;最后,基于Sobol全局灵敏度分析方法,分析考虑驾驶风格前后模型关键参数的全局灵敏度,并实现区域风险的可视化。研究结果表明:当选择速度均值、加速度均值和冲击度方差进行驾驶风格划分,K值为4时聚类效果最佳;Sobol法有效评估了参数灵敏度,当考虑驾驶人行为场后,总灵敏度分布更加均匀,模型考虑因素更为广泛,其中速度和冲击度标准差为最显著参数,总灵敏度分别为0.41和0.34;对多车共同作用产生的风险可视化显示,势场范围随道路线形变化而变化,在穿村镇交叉口风险值最显著,整体行驶速度降比为17.39%,将进一步发挥风险场理论在微观交通风险评估中的作用。

关键词: 交通工程, 风险场模型, 灵敏度分析, 穿村镇道路, 驾驶风格, 山区公路

Abstract: In response to the high frequency of traffic accidents on mountainous roads passing through villages and towns, this paper proposes a driving risk identification method considering driving style and reveals the influence of driving style in risk assessment through sensitivity analysis. The method is verified with a typical mountainous road passing through villages and towns in Yunnan Province as an example. The vehicle trajectory data is collected through drones and a trajectory database is established for the analysis. Based on the theory of driving risk field and through correlation analysis of accident data, the impact of driving environment on vehicle driving in the village and town section is characterized. Furthermore, driving style factors are introduced to establish a driving risk field that considers driving style, achieving comprehensive consideration of multiple factors and quantification of driving risk. Based on the Sobol global sensitivity analysis method, the global sensitivity of key parameters of the model before and after considering driving style is analyzed, and the visualization of regional risks is realized. The results indicate that when selecting the mean velocity, mean acceleration, and variance of impact for driving style classification, the clustering effect is best when the K value is 4. The Sobol method effectively evaluates parameter sensitivity. When considering the driver behavior field, the overall sensitivity distribution is more uniform, and the model considers a wider range of factors. Among them, the standard deviation of speed and impact are the most significant parameters, and the overall sensitivity is respectively 0.41 and 0.34. The visualization of the risks generated by the joint action of multiple vehicles shows that the potential field range varies with the shape of the road, and the risk value is most significant at the intersection through villages and towns, with an overall speed reduction ratio of 17.39%. This will further enhance the role of risk field theory in the field of micro traffic risk assessment.

Key words: traffic engineering, risk field model, sensitivity analysis, road through the village and town, driving style; mountain roads

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