基于自然驾驶数据的高密度立交出入口车辆轨迹特征研究

徐进; 杨雪敏; 张雪榆; 张杰; 孔繁星; 矫成武

doi:10.3963/j.jssn.1674-4861.2023.06.003

基于自然驾驶数据的高密度立交出入口车辆轨迹特征研究

doi: 10.3963/j.jssn.1674-4861.2023.06.003

徐进^1, ,,
杨雪敏¹,
张雪榆²,
张杰³,
孔繁星⁴,
矫成武⁵

1.
重庆交通大学交通运输学院重庆 400074
2.
重庆长安汽车股份有限公司重庆 400020
3.
中信科智联科技有限公司重庆 404100
4.
中铁二院工程集团有限责任公司成都 610031
5.
交通运输部公路科学研究院北京 100088

基金项目:

国家自然科学基金面上项目 52172340

重庆市高校创新研究群体项目 CXQT21022

详细信息

通讯作者:
徐进（1977—），博士，教授. 研究方向：道路安全性设计、人车路协同. E-mail: yhnl_996699@163.com

中图分类号: U412.3+U491.2
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出版历程
- 收稿日期: 2023-06-14
- 网络出版日期: 2024-04-03

An Investigation on Vehicle Trajectory Characteristics at Exit and Entrance of High-density Interchanges Based on Naturalistic Driving Data

1.
College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
2.
Chongqing Chang'an Automobile Co., Ltd., Chongqing 400020, China
3.
CICT Connected and Intelligent Technologies Co., Ltd., Chongqing 404100, China
4.
China Railway Eryuan Engineering Group Co. Ltd., Chengdu 610031, China
5.
Research Institute of Highway Ministry of Transport, Beijing 100088, China

摘要

摘要: 互通式立体交叉是道路交通网络重要的节点，而随着相邻立交之间的间距不断缩小，逐渐形成了高密度立交群，容易造成交通拥堵，加大驾驶负荷和事故风险。为明确在高密度立交群出入口区段的运行风险和安全隐患，在重庆内环路选取了1簇高密度立交群作为研究对象，开展了实车驾驶实验。使用车载仪器采集自然驾驶状态下车辆轨迹数据，包含速度、实时行驶位置以及车辆中心与两侧车道线之间的横向距离；基于对实测数据的深度分析，明确立交出入口的车辆轨迹形态以及车道选择行为特征和驾驶人性别对轨迹形态的影响关系，挖掘车辆驶离（汇入）主线过程中的换道行为特征和驾驶风险影响因素。结果表明：①出入口类型对车道选择和轨迹形态有明显影响，相比于平行式出口，直接式出口的轨迹更顺畅，换道次数更少；②驾驶人在净距较近的2座立交驶入驶出时，进入主线路段更倾向选择辅助车道或者最外侧车道行驶，以减少换道次数；③出入口附近的主线车道数变化会影响驾驶人的车道选择行为；④驶离主线时，平行式出口的换道持续时间要高于直接式出口，而入口类型对于换道时间没有显著影响，78%驾驶人的换道时间为5~10 s；⑤出口区段的运行风险高于入口区段，可在出口区段最右车道左侧设置白色实线禁止跨越同向车道线，长度范围以渐变段起点向前50 m一直覆盖至分流点处。
- 交通安全 /
- 高密度立交 /
- 自然驾驶数据 /
- 小净距立交 /
- 出入口 /
- 行驶轨迹
Abstract: Interchange is an important node of the road traffic network, and as the spacing between neighboring interchanges continues to shrink, a high-density cluster of interchanges is gradually formed, which is prone to traffic congestion, increasing driving load and accident risk. To clarify the operational risks and safety hazards in the entrance and exit sections of high-density interchanges, the field driving test is conducted on the Inner Ring Road of Chongqing. This test takes a cluster of high-density interchanges as the research object. Using onboard instruments, vehicle trajectory data are collected under natural driving conditions. The vehicle trajectory data includes speed, real-time driving position, and lateral distance between the vehicle center and the lane markings on both sides. By analyzing the measured data, the vehicle trajectory pattern of the interchange entrances and exits as well as the relation-ship between the behavioral characteristics of lane selection and the influence of the driver's gender on the trajectory pattern are clarified. The lane-changing behavioral characteristics and driving risk influencing factors in the process of vehicles leaving (merging into) the mainline are explored. The results reveal the following conclusions: ①The type of entrance or exit has a significant influence on lane selection and trajectory shape. Compared to parallel-type exits, direct-type exits have smoother trajectories and fewer lane changing numbers. ②When entering and exiting two adjacent interchanges with a short clearance, drivers tend to choose the auxiliary lane or the outermost lane on the mainline to reduce the number of lane changing. ③The number of mainline lanes near the entrance and exit affect drivers' lane selection behavior. ④When drivers leave the mainline, the lane-changing duration of the parallel-type exit is higher than that of the direct-type exit. The entrance type does not have a significant impact on lane-changing time. The lane-changing time for 78% of drivers is between 5 to 10 seconds. ⑤The operational risks in the exit section are higher than in the entrance section. It is recommended to use solid white lane line to prohibit crossing same-direction lane markings on the left side of the rightmost lane of the exit section. The length should cover from the beginning of the taper section to the diverging point, extending 50 meters forward from the start of the taper section.
- traffic safety /
- high-density interchanges /
- naturalistic driving data /
- small spacing interchanges /
- interchange entrance and exit /
- trajectory

HTML全文

图 1 实车驾驶路线与互通立交出入口

Figure 1. Route for field driving test and entrance and exit of interchange

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图 2 实验车辆和车载仪器

Figure 2. Experimental vehicles and on-board instruments

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图 3 车辆轨迹线提取示意图

Figure 3. Illustration of vehicle trajectory line extraction

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图 4 出口和入口段的车辆轨迹形态

Figure 4. Vehicle trajectory shape at exit and entrance

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图 5 不同性别驾驶人在入口段的换道轨迹分布

Figure 5. Lane changing trajectory of different genders in the entrance of interchange

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图 6 出入口换道过程和轨迹特征点

Figure 6. Lane change process and trajectory feature points at entrance and ex

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图 7 换道时间频数分布及累计频率图

Figure 7. Frequency distribution and cumulative frequency curves of channel changing time

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图 8 车辆换道风险示意图

Figure 8. Illustration of risk during vehicle lane change

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图 9 出/入口区段换道起点和轨迹流出点位置的累计频率曲线

Figure 9. Cumulative frequency curves of trajectory outflow point and lane change starting point at entrance and exit

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图 10 出入口区段换道横向位移累计频率曲线

Figure 10. Cumulative frequency curves of lateral displacement during lane changing at entrance and exit

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图 11 出入口区段换道纵向位移与横向位移的数据分布散点图

Figure 11. Scatter chart of distribution between lateral displacement and longitudinal displacement during lane changing at entrance and exit

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表 1 高密度立交出入口的基本信息

Table 1. Basic information of entrance and exit of high-density interchanges

立交名称	出入口名称	出入口类型	主线车道数	主线限速/（km/h）	匝道车道数	匝道限速/（km/h）
五童立交	出口1	平行式	3	70	1	40
东环立交	出口2	平行式	4	80	2	40
东环立交	出口3	直接式	3	80	3	40
五童立交	出口4	直接式	3	80	2	40
五童立交	入口1	平行式	3	80	2	40
东环立交	入口2	平行式	3	80	3	40
东环立交	入口3	直接式	3	80	3	40
五桂立交	入口4	直接式	3	80	2	40

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表 2 不同性别驾驶人在出入口区段的换道起点统计

Table 2. Statistics on the starting point of lane changing for drivers of different genders at exit and entrance

驾驶人性别	出口段换道起点/m				入口段换道起点/m
驾驶人性别	出口1	出口2	出口3	出口4	入口1	入口2	入口3	入口4
男性	71.611	77.895	66.645	66.647	61.895	78.655	73.516	70.132
女性	63.684	79.765	68.733	69.065	63.737	86.500	64.467	64.944

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表 3 出入口不同性别驾驶人的换道频次统计

Table 3. Statistics on lane changing frequency of drivers of different genders at exit and entrance

驾驶人性别	出口段换道频次及其占比/%								入口段换道频次及其占比/%
	出口1		出口2		出口3		出口4		入口1		入口2		入口3		入口4
	1	2	1	2	1	2	1	2	1	2	1	2	1	2	1	2
男性	44	56	59	41	93.5	6.5	96.9	3.1	41	59	23	77	100	0	72	28
女性	63	37	28	72	100	0	100	0	40	60	71	29	100	0	53	47

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表 4 出/入口换道起点和轨迹流出点的分布范围

Table 4. Distribution range of starting point and trajectory outflow point of lane change at exit and entrance

位置	换道起点范围/m	轨迹流出点范围/m	轨迹流出点与分/合流点距离/m	出口开口处/m
出口1	-113~-57	-56~9	-9~56	-60
出口2	-191~-102	-135~-10	10~135	-160
出口3	-100~-50	-50~0	0~50	-80
出口4	-125~-69	-71~-16	16~71	-80
入口1	-35~6	9~101	9~101	—
入口2	-80~-11	-29~84	29~84	—
入口3	-4~41	43~97	43~97	—
入口4	-26~37	2~88	2~88	—
注：表中所列分布范围的原点：出口处为分流点，入口处则为合流点。

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表 5 换道起点（x₁，y₁）与航向偏移角θ相关性分析结果

Table 5. The correlation analysis results of (x₁, y₁) and θ

位置	因素	系数	显著性	位置	因素	系数	显著性
出口1	y₁，θ	0.138	0.018	入口1	y₁，θ	0.132	0.465
出口1	x₁，θ	0.521	＜0.001	入口1	x₁，θ	－0.689	＜0.001
出口2	y₁，θ	0.027	0.433	入口2	y₁，θ	0.016	0.745
出口2	x₁，θ	0.653	＜0.001	入口2	x₁，θ	－0.684	＜0.001
出口3	y₁，θ	0.106	0.509	入口3	y₁，θ	0.265	0.541
出口3	x₁，θ	0.708	0.000	入口3	x₁，θ	－0.769	＜0.001
出口4	y₁，θ	0.031	0.095	入口4	y₁，θ	0.231	0.632
出口4	x₁，θ	0.610	＜0.001	入口4	x₁，θ	－0.744	＜0.001

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参考文献(27)

[1]	中华人民共和国住房和城乡建设部. 城市快速路设计规程: CJJ129—2009[S]. 2009. Ministry of Housing and Urban-Rural Development of the People's Republic of China. Specification for design of urban expressway: CJJ129—2009[S]. 2009. (in Chinese)
[2]	中华人民共和国交通运输部. 公路立体交叉设计细则: JTGT D21—2014[S]. 2014. Ministry of Transport of the People's Republic of China. Guidelines for design of highway grade-separated intersections: JTGT D21—2014[S]. 2014. (in Chinese)
[3]	潘存书. 基于实车驾驶的城市立交区汽车行驶轨迹特性研究[D]. 重庆: 重庆交通大学, 2021 PAN C S. Study on trajectory of vehicle in urban interchange based on field operational test data[D]. Chongqing: Chongqing Jiaotong University, 2021. (in Chinese)
[4]	ZAKI M H, SAYED T. A framework for automated road-users classification using movement trajectories[J]. Transportation Research Part C: Emerging Technologies, 2013, 33: 50-73. doi: 10.1016/j.trc.2013.04.007
[5]	LI X C, LUO X, HE M C, et al. An improved car-following model considering the influence of space gap to the response[J]. Physica A: Statistical Mechanics and its Applications, 2018, 509: 536-545. doi: 10.1016/j.physa.2018.06.069
[6]	陆键, 王可, 蒋愚明. 基于车辆行驶轨迹的道路不良驾驶行为实时辨识方法[J]. 交通运输工程学报, 2020, 20(6): 227-235. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202006023.htm LU J, WANG K, JIANG Y M. Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory[J]. Journal of Traffic and Transportation Engineering, 2020, 20 (6): 227-235. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202006023.htm
[7]	张婉婷, 杨明明, 张誓童. 基于轨迹数据的交叉口进口道车辆换道行为特性分析[J]. 内蒙古公路与运输, 2023(3): 53-57, 62. https://www.cnki.com.cn/Article/CJFDTOTAL-NMGY202303012.htm ZHANG W T, YANG M M, ZHANG S T. Analysis of lane-changing behavior characteristics of vehicles at intersection entrance based on trajectory data[J]. Highways & Transportation in Inner Mongolia, 2023(3): 53-57, 62. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NMGY202303012.htm
[8]	COIFMAN B, BEYMER D, MCLAUCHLAN P, et al. A real-time computer vision system for vehicle tracking and traffic surveillance[J]. Transportation Research Part C: Emerging Technologies, 1998, 6 (4), 271-288. doi: 10.1016/S0968-090X(98)00019-9
[9]	ZHAO P, LEE C. Assessing rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure[J]. AccidentAnalysis & Prevention, 2018, 113: 149-158.
[10]	陈景旭, 程文昱, 万剑, 等. 基于NGSIM微观轨迹数据的车辆跟驰行为安全评价[J]. 重庆交通大学学报(自然科学版), 2022, 41 (1): 1-6, 21. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202201001.htm CHEN J X, CHENG W Y, WAN J, et al. Safety evaluation of car-following behavior based on NGSIM micro trajectory data[J]. Journal of Chongqing Jiaotong University(Natural Science), 2022, 41 (1): 1-6, 21. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202201001.htm
[11]	CHEN S Y, CHENG K, YANG J H, et. al. Driving behavior risk measurement and cluster analysis driven by vehicle trajectory data[J]. Applied Sciences, 2023, 13: 5675. doi: 10.3390/app13095675
[12]	HU Y P, LI Y, HUANG H L, et. al. A high-resolution trajectory data driven method for real-time evaluation of traffic safety[J]. Accident Analysis and Prevention, 2022, 165: 106503. doi: 10.1016/j.aap.2021.106503
[13]	邓天民, 罗骁, 邵毅明, 等. 山区公路曲线路段汽车轨迹模式与切弯行为[J]. 东南大学学报(自然科学版), 2019, 49 (2): 388-396. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201902027.htm DENG T M, LUO X, SHAO Y M, et al. Track patterns and curve cutting behavior on curved sections of mountain roads, Journal of Southeast University(Natural Science Edition), 2019, 49 (2): 388-396. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201902027.htm
[14]	徐进, 罗骁, 张凯, 等. 基于自然驾驶试验的山区公路汽车行驶轨迹特性研究[J]. 中国公路学报, 2016, 29 (7): 38-51. XU J, LUO X, ZHANG K, et al. Investigation on characteristics of vehicle travelling tracks on mountain highways based on natural driving tests[J]. China Journal of Highway and Transport, 2016, 29 (7): 38-51. (in Chinese)
[15]	徐进, 陈莹, 张晓波, 等, 回头曲线路段的轨迹曲率特性和汽车过弯方式[J]. 西南交通大学学报, 2021, 56(6): 1143-1152. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106002.htm XU J, CHEN Y, ZHANG X B, et al. Track curvature behavior and negotiating patterns and on hairpin curves[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1143-1152. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106002.htm
[16]	陈莹, 王晓辉, 张晓波, 等. 山区公路回头曲线的车道偏移行为与自由行驶轨迹模型[J]. 交通运输工程学报, 2022, 22 (4): 382-395. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202204029.htm CHEN Y, WANG X H, ZHANG X B, et al. Lane offset behavior and free driving trajectory model of hairpin curves of mountain roads[J]. Journal of Traffic and Transportation Engineering, 2022, 22 (4): 382-395. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202204029.htm
[17]	张苇冲, 杨涛, 吕能超. 基于车辆位置与速度特征的驾驶行为模式分类方法[J]. 交通信息与安全, 2023, 41 (1): 85-94. doi: 10.3963/j.jssn.1674-4861.2023.01.009 ZHANG W C, YANG T, LYU N C. A method for classifying driving behavior based on vehicle position and speed[J]. Journal of Transport Information and Safety, 2023, 41(1): 85-94. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.01.009
[18]	LYU N, CHAO Y, WU C, et al. The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data[J]. Accident Analysis & Prevention, 2018, 121: 82-93.
[19]	陈秋香. 基于匝道区数据的恶劣天气下换道行为研究[D]. 成都: 西南交通大学, 2014. CHEN Q X. Study on lane-changing behavior in bad weather based on data of ramp section[D]. Chengdu: Southwest Jiaotong University, 2014. (in Chinese)
[20]	黄利华. 城市快速路立交桥出口指路标志影响分析及效用评估研究[D]. 北京: 北京工业大学, 2015. HUANG L H. Research on influence analysis and effectiveness evaluation of interchange exit guide signs in urban expressway[D]. Beijing: Beijing University of Technology, 2015. (in Chinese)
[21]	杨登松. 高密度互通立交区域标志标线组合精细化设计研究[J]. 智能城市, 2021, 7 (4): 1-3. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNCS202104002.htm YANG D S. Refined design of sign marking combinations in high-density interchange areas[J]. Intelligent City, 2021, 7 (4): 1-3. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZNCS202104002.htm
[22]	徐进, 孙子秋, 王思棋, 等. 高密度互通立交出口匝道驾驶人视觉搜索行为特征[J]. 东南大学学报(自然科学版), 2022, 52 (6): 1189-1198. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX202206020.htm XU J, SONG Z Q, WANG S Q, et al. Characteristics of driver's visual search behavior in exit ramp of high-density interchanges[J]. Journal of Southeast University (Natural Science Edition), 2022, 52 (6): 1189-1198. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX202206020.htm
[23]	裴玉龙, 金英群, 陈贺飞. 基于脑电信号分析的不同年龄驾驶人疲劳特性[J]. 中国公路学报, 2018, 31 (4): 59-65, 77. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804009.htm PEI Y L, JIN Y Q, CHEN H F. Fatigue characteristics in drivers of different ages based on analysis of EEG[J]. China Journal of Highway and Transport, 2018, 31(4): 59-65, 77. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804009.htm
[24]	刘智, 马强强, 邵海鹏, 等. 高速公路单车道出口匝道车辆驶出特性及安全性影响研究[J]. 公路, 2023, 68(7): 333-342. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL202307054.htm LIU Z, MA Q Q, SHAO H P, et al. Research on safety and vehicle driving characteristics on single-laned exit ramp of expressway[J]. Highway, 2023, 68(7): 333-342. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL202307054.htm
[25]	中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 道路交通标志和标线第3部分: 道路交通标线: GB 5768.3—2009. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Certification and Accreditation Administration. Road traffic signs and markings - Part 3: Road traffic markings: GB 5768. 3—2009. 2009. (in Chinese)
[26]	沈航先. 车辆换道意图识别和轨迹预测模型研究[D]. 哈尔滨: 东北林业大学, 2021. SHEN H X. Research on vehicle lane change intention recognition and trajectory prediction model[D]. Harbin: Northeast Forestry University, 2021. (in Chinese)
[27]	马小龙, 余强, 刘建蓓, 等. 基于无人机视频拍摄的高速公路小型车换道行为特性[J]. 中国公路学报, 2020, 33 (6): 95-105. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202006010.htm MA X L, YU Q, LIU J B, et al. Analysis of lane change behavior of passenger cars on the freeway using UAVs[J]. China Journal of Highway and Transport, 2020, 33(6): 95-105. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202006010.htm