Traffic Conflict Risk Identification in Multi-vehicle Linked U-turn Areas

doi:10.16097/j.cnki.1009-6744.2025.05.016

Abstract

Abstract: In order to quantitatively identify the motor vehicle conflict risk points in urban multi-vehicle linked turnaround areas and reduce the accident rate, this paper proposes a conflict risk identification process for multi-vehicle linked turnaround areas: firstly, continuous and high-precision multi-vehicle trajectory video is acquired by UAV aerial photography, and the displacement, speed and other states of each vehicle are tracked and extracted at frame level with the help of Tracker software; then, the risk quantification idea based on the time-to-collision (TTC) risk quantification idea, improve the traditional TTC algorithm (ETTC) for the geometric characteristics of the U-turn area, and draw cumulative distribution curves based on the collected ETTC and TTC data, from which the key tertiles representing minor, general and serious conflicts are selected as the thresholds; finally, spatially map the conflict events to the twenty subsections in the study area, and combine the frequency and severity of conflicts in the various sections to determine the risk quantification of each vehicle. Finally, the conflict events were spatially mapped to the twenty subsectors of the study area, and the risk level of each subsector was graded by combining the frequency and severity of the conflict. It was found that the highest severity rate of 25.51% was found in zone 15 of horizontal conflicts, and the highest severity rate of 21.95% was found in zones 2 and 3 of vertical conflicts, and the high-risk areas were concentrated in the middle two lanes of the roadway and at the 4th parking space. The research results can provide a scientific basis for the traffic management department to optimize the traffic safety management of multi-vehicle linked U-turn area.

Key words: urban traffic, conflict risk identification, improved TTC, multi-vehicle linked U-turn, spatial distribution of conflicts

摘要： 为定量识别城市多车联动式掉头区域的机动车冲突风险点，降低事故发生率，本文提出了一个针对多车联动掉头区域的冲突风险识别流程：首先，通过无人机航拍获取连续、高精度的多车轨迹视频，并借助Tracker软件对各车辆的位移、速度等状态进行帧级跟踪与提取；接着，引入基于时间-碰撞(TTC)的风险量化思路，针对掉头区域的几何特征改进传统TTC算法(ETTC)，并根据采集到的ETTC与TTC数据绘制累积分布曲线，从中选取代表轻微、一般和严重冲突的关键分位点作为阈值；最后，将冲突事件空间化映射到研究区的二十个子区段，结合各区段的冲突频次与严重程度，对其风险等级进行分级评定。研究发现，横向冲突第15区段严重率最高，为25.51%，纵向冲突第2和第3区段严重率最高，为21.95%，高风险区域集中在道路中间两车道及第4车位处。研究成果可为交通管理部门提供科学依据，优化多车联动式掉头区域的交通安全管理。

关键词: 城市交通, 冲突风险识别, 改进TTC, 多车联动式掉头, 冲突空间分布

CLC Number:

U491

HU Liwei, GONG Qi, ZHAO Xueting, ZHOU Zeyu, CHEN Jiale, PAN Jiangxiong, YANG Can, MA Siyue. Traffic Conflict Risk Identification in Multi-vehicle Linked U-turn Areas[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 179-192.

胡立伟, 龚麒, 赵雪亭, 周泽禹, 陈家乐, 潘江雄, 杨灿, 马思月. 多车联动式掉头区域交通冲突风险识别研究[J]. 交通运输系统工程与信息, 2025, 25(5): 179-192.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2025.05.016

http://www.tseit.org.cn/EN/Y2025/V25/I5/179

References

[1] ZHU S, JIANG R, WANG H, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway and Transport, 2020, 33(2): 15-33.

[2] JOHNSSON C, LAURESHYN A, CEUNYNCK T D. In search of surrogate safety indicators for vulnerable road users: A review of surrogate safety indicators[J]. Transport Reviews, 2018, 38(6): 765-785.

[3] WERNER B, THORSTEN M. Capacity and delays at intersections without traffic signals[C]. Washington, DC: Transportation Research Board 84th Annual Meeting Compendium of Papers, 2005.

[4] SAYED T, BROWN G, NAVIN F. Simulation of traffic conflicts at unsignalized intersections with TSC-Sim[J]. Accident Analysis & Prevention, 1994, 26(5): 593-607.

[5] 吴文静,陈润超,贾洪飞,等.车路协同环境下路段掉头区域车辆协同控制[J]. 吉林大学学报(工学版), 2019, 490(4): 1100-1106. [WU W J, CHEN R C, JIA H F, et al. Cooperative control of vehicles in roadway turnaround areas in a vehicle-circuit cooperative environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(4): 1100-1106.]

[6] OH C, PARK S, RITCHIE S G. A method for identifying rear-end collision risks using inductive loop detectors[J]. Accident Analysis & Prevention, 2006, 38(2): 295-301.

[7] 孟祥海,徐汉清,王浩,等.基于TTC及DRAC的高速公路施工区追尾冲突研究[J]. 交通信息与安全, 2012, 30(6): 6-10. [MENG X H, XU H Q, WANG H, et al. A study of tailgating conflicts in highway construction zones based on TTC and DRAC[J]. Journal of Transport Information and Safety, 2012, 30(6): 6-10.]

[8] 马艳丽,祁首铭,吴昊天,等.基于PET算法的匝道合流区交通冲突识别模型[J]. 交通运输系统工程与信息, 2018, 18(2): 142–148. [MA Y L, QI S M, WU H T, et al. A traffic conflict identification model for ramp merging area based on PET algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 142–148.]

[9] 胡立伟, 张瑞杰, 赵雪亭, 等. 道路环形交叉口机动车冲突风险区识别模型研究[J]. 交通运输系统工程与信息, 2024, 24(1): 168–178. [HU L W, ZHANG R J, ZHAO X T, et al. A study on the identification model of motor vehicle conflict risk zones at roadway roundabouts [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 168–178.]

[10] 曲昭伟, 李霖, 陈永恒, 等. 长区间掉头车辆特性分析及其安全评价[J]. 吉林大学学报(工学院), 2024, 54 (8): 2206–2213. [QU Z W, LI L, CHEN Y H, et al. Characterization of long interval U-turn vehicles and their safety evaluation[J]. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(8): 2206–2213.]

[11] 宋浪, 白明举, 叶青, 等. 交叉口多车串联掉头几何设计与信号配时优化[J]. 公路交通技术, 2022, 38(6): 142–148. [SONG L, BAI M J, YE Q, et al. Geometric design and signal timing optimization of multi-vehicle tandem U-turns at intersections[J]. Technology of Highway and Transport, 2022, 38(6): 142–148.]

[12] 陆毅忱, 邹亚杰,程凯,等.基于风险域的城市道路交叉口交通冲突分析方法[J]. 同济大学学报(自然科学版), 2021, 49(7): 941–948. [LU Y C, ZOU Y J, CHENG K, et al. A risk-domain based approach for traffic conflict analysis at urban road intersections[J]. Journal of Tongji University (Natural Science), 2021, 49(7): 941–948.]

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