| 引用本文: | 潘恒彦,王永岗,李德林,张枭,陈俊先.弯坡组合路段追尾事故风险评估与影响因素分析[J].哈尔滨工业大学学报,2023,55(11):36.DOI:10.11918/202203049 |
| PAN Hengyan,WANG Yonggang,LI Delin,ZHANG Xiao,CHEN Junxian.Risk assessment and influence factors analysis of rear-end collision on curved slope combination section[J].Journal of Harbin Institute of Technology,2023,55(11):36.DOI:10.11918/202203049 |
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| 摘要: |
| 为解决山区路段追尾事故频发的问题,基于交通冲突技术对弯坡组合路段车辆追尾事故风险进行评估,并对追尾冲突的影响因素进行识别。通过无人机航拍、雷达测速等调查手段对车辆轨迹与交通流数据进行采集。传统碰撞时间（time to collision,TTC）计算方法尚未对弯坡组合路段线形特征充分考虑,根据弯坡组合路段各组成部分（圆曲线、缓和曲线与直线路段）的道路线形与车辆运行特征,对车辆追尾TTC进行修正,根据冲突时间累积分布曲线得出严重、一般、轻微与潜在追尾冲突的划分阈值,分别为1.23、2.59、3.50、4.0 s。分别构建圆曲线、缓和曲线与直线路段追尾冲突影响因素识别树结构,并通过有序Logistic模型分析各因素对追尾冲突严重性的影响。结果表明:交通量的增加、大型车辆的混入、车辆行驶速度与加速度的提高会促进冲突的产生以及严重程度的增加；交通流特征指标对各路段追尾冲突的影响存在效果差异；圆曲线路段车辆入弯与出弯方向及内侧与外侧弯道之间的追尾冲突无显著差异；而在缓和曲线与直线路段存在差异。研究结果能够帮助追尾事故的主动安全防控、实时预测,并改善弯道路段的行车安全。 |
| 关键词: 交通工程 交通冲突技术 弯坡组合路段 追尾风险评估 CHAID决策树 有序Logistic模型 |
| DOI：10.11918/202203049 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2019YFB1600500) |
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| Risk assessment and influence factors analysis of rear-end collision on curved slope combination section |
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PAN Hengyan1,WANG Yonggang1,LI Delin1,ZHANG Xiao2,CHEN Junxian1
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(1.College of Transportation Engineering, Chang′an University, Xi′an 710064, China; 2.Shanghai Urban Construction Design & Research Institute(Group) Co., Ltd., Shanghai 200125, China)
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| Abstract: |
| To address the issue of frequent rear-end collisions on mountainous road sections, the risk of vehicle rear-end collisions on curved slope combination sections is assessed based on traffic conflict technology, and the influencing factors of rear-end collisions are identified. Vehicle trajectory and traffic flow data are collected through aerial photography by UAV (Unmanned aerial vehicle) and radar speed measurement. The traditional time to collision (TTC) calculation method has not fully considered the alignment characteristics of the curved slope combination section. According to the characteristics of road alignment and vehicle operation in each component (circle curve section, gentle curve section and straight curve section) of the curved slope combination road section, the vehicle rear-end collision time TTC is modified, and the classification thresholds of severe, moderate, slight and potential rear-end conflicts are 1.23 s, 2.59 s, 3.50 s and 4.0 s according to the cumulative distribution curve of conflict time. The tree structures for identifying the influence factors of rear-end conflicts on circular curve, gentle curve and straight curve road sections were constructed respectively, and the influence of each factor on the severity of rear-end conflicts was investigated by ordered logistic models. The results indicate that: the increase of traffic volume, the mixing of large vehicles, and the increase of vehicle travel speed and acceleration will promote the occurrence of conflicts as well as the severity of conflicts; There are differences in the effects of traffic flow characteristics indexes on rear-end conflicts in each road section; No significant differences in rear-end conflicts were found between vehicles approaching or exiting the direction of curves, and between inside and outside curves on circle sections; however, such differences existed on gently curved and straight sections. The results of this research help active safety prevention and control of rear-end collisions, as well as real-time forecasting to improve safety of travelling on curved road sections. |
| Key words: traffic engineering technology of traffic conflict assessment for risk of rear end collision curved slope combination section CHAID decision tree ordered Logistic model |
