面向多类型交通冲突的道路条件及交通状况影响评估

钟昊; 马万经; 王玲

doi:10.3963/j.jssn.1674-4861.2023.06.013

面向多类型交通冲突的道路条件及交通状况影响评估

doi: 10.3963/j.jssn.1674-4861.2023.06.013

同济大学道路与交通工程教育部重点实验室上海 201804

基金项目:

国家自然科学基金项目 52325210

国家自然科学基金项目 52131204

国家自然科学基金项目 52372333

中央高校基本科研业务费专项资金项目 2023-4-YB-05

国家留学基金项目 202206260129

详细信息

作者简介:
钟昊(1997—)，博士研究生. 研究方向：车路协同技术、交通安全等.E-mail: 2110191@tongji.edu.cn

通讯作者:
王玲(1987—)，博士，副教授. 研究方向：主动交通控制、交通安全等. E-mail: wang_ling@tongji.edu.cn

中图分类号: U491.31
计量
- 文章访问数: 335
- HTML全文浏览量: 136
- PDF下载量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-13
- 网络出版日期: 2024-04-03

An Assessment of Road Conditions and Traffic Situations Impact on Multi-type Single and Chain Conflicts

Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

摘要

摘要: 交通冲突为交通事故发生前的潜在状态。探究静态路段属性和动态交通流特性等宏观特征对于交通冲突的影响至关重要，但现有研究主要关注2辆车之间的危险状态，对于涉及多个交通主体的冲突缺乏重视。为有效提取包括单一冲突和连锁冲突在内的多类型交通冲突，基于无人机采集的车辆轨迹数据首先识别车辆对之间的单一冲突，另通过关联匹配辨识连锁冲突，并基于聚类将连锁冲突划分为纵向风险下降模式、纵向风险增加模式和横纵向高风险持续模式。构建巢式Logit模型探究宏观交通属性及路段条件对多类型单一冲突和连锁冲突的影响，结果表明合流区和基本路段为单一冲突的高发区域，而分流区和交织区为连锁冲突高发区域，尤其会导致横纵向高风险持续模式的发生，但车道数的增加有助于减少严重连锁冲突的发生。此外，主线交通密度增大，连锁冲突发生概率增加；匝道与主线的流量比增大时，连锁冲突更易发生，其中纵向风险增加模式对交通流量最为敏感。将各类型冲突发生的交通流条件与宏观基本图结合分析，表明各类型交通冲突的发生次数存在峰值，且路段上交通冲突发生最多的临界密度要高于同一路段宏观基本图的临界密度。研究结论有助于理解多车连锁冲突发生的宏观原因，有效阻断其演化为连锁碰撞。
- 交通安全 /
- 多类型交通冲突 /
- 冲突影响因素 /
- 巢式Logit模型 /
- 路段属性 /
- 交通流特征
Abstract: Traffic conflict is the underlying state before a traffic crash occurs. Understanding the impact of static road attributes and dynamic characteristics of traffic flow on traffic crashes is crucial. However, existing research primarily focuses on the hazardous states between two vehicles, neglecting events involving multiple traffic entities. To effectively extract various types of traffic conflicts, including both single and chain conflicts, this study utilizes drone-acquired vehicle trajectory data to identify single conflicts between vehicles and subsequently identifies chain conflicts through association matching. In addition, a chain conflict can be divided into three patterns, i.e., Longitudinal Risk-Decrease Pattern, Longitudinal Risk-Increase Pattern, and Comprehensive High-Risk-Persistent Pattern. Subsequently, a nested Logit model is developed to explore the influence of macroscopic traffic attributes and road conditions on various types of single and chain conflicts. The findings reveal that merging segments and basic segments of roads are high-risk regions for single conflicts, while diverging segments and weaving segments are prone to happen chain conflicts, particularly those of comprehensive high-risk persistent pattern. Interestingly, an increase in the number of lanes helps mitigate severe chain conflicts. Additionally, as the traffic density in mainlines rises, the probability of chain conflicts increases. The volume ratio of ramp to mainline correlates positively with chain conflict occurrence in which the Longitudinal Risk Increase Pattern being the most sensitive. The traffic flow conditions under which each type of conflict occurs, combined with the analysis of macroscopic fundamental diagrams, indicate that conflict occurrences exhibit peaks. Moreover, the critical density at which conflicts are the most frequent on road segments exceeds the critical density indicated by the macroscopic fundamental diagram for the same segment. These conclusions hold substantial importance for understanding the macro causes of multi-vehicle chain conflicts, and for effectively preventing their evolution into chain collisions.
- traffic safety /
- multi-type traffic conflicts /
- influencing factors /
- nested logistic regression model /
- road attributes /
- traffic characteristics

HTML全文

图 1 MAGIC轨迹数据集部分实验路段

Figure 1. Selected road segments of the MAGIC dataset

下载: 全尺寸图片幻灯片

图 2 追尾冲突计算示意图

Figure 2. Schematic diagram of rear-end conflicts

下载: 全尺寸图片幻灯片

图 3 侧擦冲突替代指标计算示意图

Figure 3. Schematic diagram of sideswipe conflicts

下载: 全尺寸图片幻灯片

图 4 连锁冲突匹配流程

Figure 4. Chain conflicts matching process

下载: 全尺寸图片幻灯片

图 5 关于不同交通冲突模式的巢式Logit模型结构示意图

Figure 5. Nested Logit model structure for different traffic conflict patterns

下载: 全尺寸图片幻灯片

图 6 路段密度-流量和密度-交通冲突次数的关系

Figure 6. Density-volume relationship and density-conflict relationship of road segments

下载: 全尺寸图片幻灯片

表 1 不同连锁冲突演化模式特征均值

Table 1. Mean characteristics of different chain conflict evolution patterns

模式特征	连锁冲突演化模式
模式特征	纵向风险下降模式	纵向风险增加模式	横纵向高风险持续模式
冲突风险强度	0.20	0.56	0.45
风险变化趋势	-0.03	0.04	0.10
传播速度比	2.64	2.32	2.04
传播次数	2.57	4.07	6.55
传播方向	纵向	纵向	横纵向

下载: 导出CSV

表 2 影响因素及其参数

Table 2. Influence factors and their parameters

变量类型	变量名称	变量符号	参数符号
道路几何特征	车道数	β_L	β_L
道路几何特征	路段类别（是否为合流区、是否为分流区、是否为交织区）	$C_1, C_2, C_3$	$\beta_{C 1}, \beta_{C 2}, \beta_{C 3}$
交通流特征	道路主线速度	V	β_V
	道路主线密度	O	β_O
	道路主线流量	Q	β_Q
	匝道与主线的密度比(若有匝道)	D	β_D
	匝道与主线的流量比(若有匝道)	R	β_R

下载: 导出CSV

表 3 关于不同交通冲突模式的巢式Logit模型参数标定结果

Table 3. Parameter calibration results of nested Logit model for different traffic conflict patterns

参数	单一冲突	连锁交通冲突（纵向风险下降模式）	连锁交通冲突（纵向风险增加模式）	连锁交通冲突（横纵向高风险持续模式）
β_L	1.010（0.329）^**	0.151（0.045）^***		-0.194（0.033）^***
β_C1	1.120（0.071）^***	-0.381（0.027）^***	-0.425（0.027）^***	-0.318（0.024）^***
β_C2				0.227（0.052）^***
β_C3	-0.431（0.074）^***	0.053（0.025）^*	0.118（0.027）^***	0.260（0.038）^***
β_O	-5.880（0.133）^***	1.790（0.064）^***	1.970（0.045）^***	2.110（0.070）^***
β_Q	0.193（0.040）^***	-0.069（0.013）^***	-0.079（0.015）^***	-0.045（0.015）^**
β_D	0.183（0.111）.	-0.022（0.013）.
β_R	-0.330（0.100）^***	0.100（0.032）^**	0.116（0.034）^***	0.115（0.034）^***
ASC	0.957（0.186）^***	-0.301（0.077）^***	-0.281（0.079）^***	-0.375（0.061）^***
λ	1.000		13.200（4.090）^**
统计量	LL(β)	-16 559.560
	ρ²	0.232
	AIC	33 181.110
注：“*”-p＜0.001；“”-p＜0.010；“*”-p＜0.050；“.”-p＜0.100。

下载: 导出CSV

参考文献(28)

[1]	YASMIN S, ELURU N, WANG L, et al. A joint framework for static and real-time crash risk analysis[J]. Analytic Meth-ods in Accident Research, 2018(18): 45-56.
[2]	YANG B, LIU P, CHAN C Y, et al. Identifying the crash char-acteristics on freeway segments based on different ramp influ-ence areas[J]. Traffic Injury Prevention, 2019, 20(4): 386-391. doi: 10.1080/15389588.2019.1588965
[3]	ZHENG Q, XU C, LIU P, et al. Investigating the predictabili-ty of crashes on different freeway segments using the re-al-time crash risk models[J]. Accident Analysis & Prevention, 2021(159): 106213.
[4]	WANG L, ABDEL-ATY M, SHI Q, et al. Real-time crash pre-diction for expressway weaving segments[J]. Transportation Research Part C: Emerging Technologies, 2015(61): 1-10.
[5]	RIM H, ABDEL-ATY M, MAHMOUD N. Multi-vehicle safe-ty functions for freeway weaving segments using lane-level traffic data[J]. Accident Analysis & Prevention, 2023(188): 107113.
[6]	HOU Q, TARKO A P, MENG X. Investigating factors of crash frequency with random effects and random parameters models: new insights from Chinese freeway study[J]. Acci-dent Analysis & Prevention, 2018, 120: 1-12.
[7]	YUAN C, LI Y, HUANG H, et al. Using traffic flow charac-teristics to predict real-time conflict risk: a novel method for trajectory data analysis[J]. Analytic Methods in Accident Re-search, 2022, 35: 100217. doi: 10.1016/j.amar.2022.100217
[8]	LI D, FU C, SAYED T, et al. An integrated approach of ma-chine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction[J]. Accident Analysis & Prevention, 2023, 192: 107286.
[9]	ABDEL-ATY M, WANG L. Implementation of variable speed limits to improve safety of congested expressway weaving segments in microsimulation[J]. Transportation Research Pro-cedia, 2017, 27: 577-584. doi: 10.1016/j.trpro.2017.12.061
[10]	WANG L, ZOU L, ABDEL-ATY M, et al. Expressway rear-end crash risk evolution mechanism analysis under dif-ferent traffic states[J]. Transportmetrica B: Transport Dynam-ics, 2023, 11(1): 510-527. doi: 10.1080/21680566.2022.2101565
[11]	张吉光. 高速公路汽车连环追尾风险研究[D]. 长沙: 长沙理工大学, 2012. ZHANG J G. Risk study of multiple rear-end collisions on expressway[D]. Changsha: Changsha University of Science and Technology, 2012. (in Chinese)
[12]	SUGIYAMA N, NAGATANI T. Multiple-vehicle collision in-duced by a sudden stop in traffic flow[J]. Physics Letters A, 2012, 376(22): 1803-1806. doi: 10.1016/j.physleta.2012.04.024
[13]	NAGATANI T, YONEKURA S. Multiple-vehicle collision induced by lane changing in traffic flow[J]. Physica A: Statis-tical Mechanics and its Applications, 2014(404): 171-179.
[14]	NAGATANI T. Effect of vehicular size on chain-reaction crash[J]. Physica A: Statistical Mechanics and its Applica-tions, 2015, 438: 132-139. doi: 10.1016/j.physa.2015.06.045
[15]	NAGATANI T. Chain-reaction crash on a highway in high visibility[J]. Physica A: Statistical Mechanics and its Applica-tions, 2016, 450: 466-472. doi: 10.1016/j.physa.2016.01.031
[16]	李熙莹, 梁靖茹, 郝腾龙. 考虑连锁冲突的城市公交车行车风险量化分析方法[J]. 交通信息与安全, 2022, 40(3): 19-29. doi: 10.3963/j.jssn.1674-4861.2022.03.003?viewType=HTML LI X Y, LIANG J R, HAO T L. A method for quantitatively analyzing risks associated with the operation of urban buses considering chained conflicts[J]. Journal of Transport Infor-mation and Safety, 2022, 40(3): 19-29. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.03.003?viewType=HTML
[17]	朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002002.htm ZHU S Y, JIANG R X, WANG H, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway and Transport, 2020, 33(2): 15. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002002.htm
[18]	MA W, ZHONG H, WANG L, et al. MAGIC dataset: multi-ple conditions unmanned aerial vehicle group-based high-fi-delity comprehensive vehicle trajectory dataset[J]. Transpor-tation Research Record, 2022, 2676(5): 793-805. doi: 10.1177/03611981211070549
[19]	OZBAY K, YANG H, BARTIN B, et al. Derivation and vali-dation of new simulation-based surrogate safety measure[J]. Transportation Research Record, 2008, 2083(1): 105-113. doi: 10.3141/2083-12
[20]	ZHONG H, WANG L, SU Z, et al. Characteristics identification and evolution patterns analyses of road chain con-flicts[J]. Accident Analysis & Prevention, 2024, 195: 107395.
[21]	JOO Y J, KIM E J, KIM D K, et al. A generalized driving risk assessment on high-speed highways using field theo-ry[J]. Analytic Methods in Accident Research, 2023, 40: 100303. doi: 10.1016/j.amar.2023.100303
[22]	JEONG E, OH C, LEE S. Is vehicle automation enough to prevent crashes? Role of traffic operations in automated driv-ing environments for traffic safety[J]. Accident Analysis & Prevention, 2017, 104: 115-124.
[23]	YUAN C, LI Y, HUANG H, et al. Using traffic flow charac-teristics to predict real-time conflict risk: a novel method for trajectory data analysis[J]. Analytic Methods in Accident Re-search, 2022, 35: 100217. doi: 10.1016/j.amar.2022.100217
[24]	MANAN M M A, VARHELYI A, ÇELIK A K, et al. Road characteristics and environment factors associated with mo-torcycle fatal crashes in Malaysia[J]. IATSS Research, 2018, 42(4): 207-220. doi: 10.1016/j.iatssr.2017.11.001
[25]	蒋晓丹, 范厚明, 张琰雪, 等. 港口与运输方式及陆港联合选择的巢式Logit模型[J]. 交通运输系统工程与信息, 2018, 18(5): 32-37. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201805006.htm JIANG X D, FAN H M, ZHANG Y X. Nested Logit model for the joint choice of seaport, inland mode and dry port[J]. Journal of Transportation Systems Engineering and Informa-tion Technology, 2018, 18(5): 32-37. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201805006.htm
[26]	张戎, 李璐, 简文良. 城市货运车辆选择行为模型及应用[J]. 交通运输系统工程与信息, 2018, 18(4): 135-141. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201804021.htm ZHANG R, LI L, JIAN W L. Urban Freight vehicle type choice model and application[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(4): 135-141. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201804021.htm
[27]	HOU Q, TARKO A P, MENG X. Investigating factors of crash frequency with random effects and random parameters models: new insights from Chinese freeway study[J]. Acci-dent Analysis & Prevention, 2018, 120: 1-12.
[28]	MENG F, XU P, SONG C, et al. Influential factors associat-ed with consecutive crash severity: a two-level logistic mod-eling approach[J]. International Journal of Environmental Re-search and Public Health, 2020, 17(15): 5623. doi: 10.3390/ijerph17155623