基于自然驾驶轨迹数据的城市快速路小型车辆换道特性分析

李阳钊; 陈海华; 黄申春; 曹光; 曹博; 梁之遥; 雷剑; 贺宜

doi:10.3963/j.jssn.1674-4861.2024.05.004

基于自然驾驶轨迹数据的城市快速路小型车辆换道特性分析

doi: 10.3963/j.jssn.1674-4861.2024.05.004

李阳钊^{1, 2,},
陈海华^3, ,,
黄申春³,
曹光⁴,
曹博^{1, 2},
梁之遥⁵,
雷剑⁶,
贺宜^{1, 2}

1.
武汉理工大学智能交通系统研究中心武汉 430063
2.
交通信息与安全教育部工程研究中心武汉 430063
3.
广东省交通集团有限公司广州 510550
4.
广东省高速公路营运管理协会广州 510550
5.
澳门科技大学创新工程学院澳门 999078
6.
中国电信（澳门）有限公司澳门 999078

基金项目:

国家自然科学基金项目 52322217

国家自然科学基金项目 52072292

详细信息

作者简介:
李阳钊（1999—），博士研究生. 研究方向：自动驾驶决策与控制. E-mail: 319821@whut.edu.cn

通讯作者:
陈海华（1978—），硕士，高级工程师. 研究方向：交通流量预测与调度. E-mail: 11198510@qq.com

中图分类号: U491.5+4
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-04
- 网络出版日期: 2025-01-22

Analysis of Small Vehicle Lane-Changing Characteristics of Urban Expressway Based on Naturalistic Driving Trajectory Data

LI Yangzhao^{1, 2
,},
CHEN Haihua^{3
, ,},
HUANG Shenchun³,
CAO Guang⁴,
CAO Bo^{1, 2},
LIANG Zhiyao⁵,
LEI Jian⁶,
HE Yi^{1, 2}

1.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2.
Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan 430063, China
3.
Guangdong Provincial Transportation Group Co., Ltd., Guangzhou 510550, China
4.
Guangdong Expressway Operation and Management Association, Guangzhou 510550, China
5.
Faculty of Innovative Engineering, Macau University of Science and Technology, Macau 999078, China
6.
China Telecom(Macau)Corp Ltd, Macau 999078, China

摘要

摘要: 跟驰和换道是交通流理论重要的研究方向，换道行为涉及因素较跟驰行为更为复杂。当前基于国外公开轨迹数据集的换道特性分析很难涵盖符合中国驾驶人特性的换道行为特性，同时国内外数据集采集来源多集中在高速公路上，未考虑不同道路类型对换道行为特性的影响。为研究中国典型城市道路车辆换道行为特性，采用无人机对武汉城市快速路直行路段交通流进行拍摄，获取符合中国城市道路特性与驾驶人特性的自然驾驶数据，并对数据集进行换道识别与参数提取，在此基础上进行了换道行为特性分析。无人机所采集视频包含小型车辆8 609辆，依据车辆所在车道编号是否发生变化以及变化次数作为换道车辆识别标准，共提取6 897辆跟驰车辆轨迹数据（车辆所在车道编号无变化）以及1 712辆单次换道车辆轨迹数据（车辆所在车道编号仅发生一次变化）。基于所提取跟驰车辆轨迹数据获取道路交通流平均速度与车辆平均跟车间距等指标，从而对交通流实时运行状态进行分析；基于所提取的车辆单次换道轨迹数据，采用固定时间窗口作为判断换道起终点的依据，在此基础上获取车辆换道纵向位移与换道启动时与周边车的时距，并结合交通流实时运行状态进行换道行为安全分析。通过对所获取的跟驰与换道交通特征参数进行分布拟合与统计分析，结果显示道路交通流速度均值为19.257 1 m/s，车辆跟车间距均值为45.910 7 m，车辆换道纵向位移均值为115.515 m，车辆换道启动时与周边车时距分布均符合对数正态分布。其中换道车辆与目标车道前车时距均值显著高于初始车道前车时距均值。同时发现，在与目标车道后车时距较小时，仍有一部分驾驶人选择换道，这体现了部分驾驶人激进的驾驶行驶。本研究可为分析中国城市快速路上的换道特性以及开发适用于中国交通特点的换道行为模型提供参考。
- 交通工程 /
- 城市快速路 /
- 换道特性分析 /
- 交通流
Abstract: Car following and lane changing are important research directions in traffic flow theory, and the factors involved in lane changing behavior are more complex than following. The current analysis of lane-changing characteristics based on foreign public trajectory datasets can hardly cover the lane-changing behavior characteristics in line with Chinese drivers, and at the same time, most of the domestic and foreign dataset collection sources are concentrated on highways, which does not consider the influence of different road types on the characteristics of lane-changing behavior. In order to study the characteristics of vehicle lane-changing behavior on typical urban roads in China, an unmanned aerial vehicle(UAV)was used to photograph the traffic flow on the straight section of the urban expressway in Wuhan, to obtain the natural driving data in line with the characteristics of urban roads and drivers in China, and to perform lane-changing identification and parameter extraction on the dataset. The video captured by the UAV contains 8 609 small vehicles, and based on whether the lane number where the vehicle is located changes and the number of changes as the recognition standard for lane-changing vehicles, a total of 6 897 vehicle trajectory data are extracted from the following vehicles(no change in the lane number where the vehicle is located), and 1 712 single lane-changing vehicle trajectory data are extracted(the lane number where the vehicle is located changes only once). Based on the extracted trajectory data of the following vehicles, obtain the average speed of the road traffic flow and the average distance between the following vehicles, so as to analyze the real-time operation state of the traffic flow; based on the extracted trajectory data of the single lane-changing vehicles, adopt a fixed time window as the basis for judging the starting and ending points of the lane-changing, and on this basis, obtain the longitudinal displacement of the vehicle changing the lane and the distance between it and the neighbor vehicles when the lane-changing is started, and the safety of lane-changing behavior is analyzed by combining with the real-time operation state of the traffic flow. The safety analysis of lane-changing behavior is carried out by combining the real-time operation status of traffic flow. Through the distribution fitting and statistical analysis of the obtained traffic parameters of following and lane changing, the results show that the average value of road traffic speed is 19.257 1 m/s, the average value of vehicle following distance is 45.910 7 m, the average value of vehicle longitudinal displacement is 115.515 m, and the distribution of the distance between the vehicle and peripheral cars at the time of lane changing is in line with the lognormal distribution. Among them, the average value of the lane change vehicle time distance from the vehicle in front of the target lane is significantly higher than the average value of the vehicle time distance from the vehicle in front of the initial lane. It is also found that some drivers still choose to change lanes when the distance from the rear vehicle in the target lane is small, which reflects the aggressive driving of some drivers. This study can provide a reference for analyzing the characteristics of lane-changing on urban expressways in China and developing a lane-changing behavior model suitable for Chinese traffic characteristics.
- Traffic engineering /
- Urban expressway /
- Lane-changing characteristics analysis /
- Traffic flow theory

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图 1 典型换道场景

Figure 1. Typical lane change scenario

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图 2 路段卫星图

Figure 2. Satellite view of the road section

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图 3 跟驰车辆平均速度统计与拟合

Figure 3. Average speed statistics and fitting of following vehicles

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图 4 跟驰车辆平均间距统计与拟合

Figure 4. Statistics and fitting of average distance between following vehicles

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图 5 换道纵向位移统计与拟合

Figure 5. Longitudinal displacement statistics and fitting for lane change

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图 6 换道启动时EV与IPV时距统计与拟合

Figure 6. Statistics and fitting of EV and IPV time distance at lane change initiation

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图 7 换道启动时EV与TPV时距统计与拟合

Figure 7. Statistics and fitting of EV and TPV time distance at lane change initiation

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图 8 换道启动时EV与TFV时距统计与拟合

Figure 8. Statistics and fitting of EV and TFV time distance at lane change initiation

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表 1 数据说明

Table 1. Data description

数据集名称	道路类型
03	单向4车道（含匝道）
04	双向6车道
05	双向7车道
06	双向7车道
08	双向8车道
09	双向8车道

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表 2 换道率统计

Table 2. Switching rate statistics

数据集	样本量	单次换道	跟驰	换道率/%
03	487	93	394	19.10
04	1 452	189	1 263	13.02
05	2 104	497	1 607	23.62
06	2 181	505	1 676	23.15
08	1 241	246	995	19.82
09	1 144	182	962	15.91

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表 3 相关特征参数描述

Table 3. Description of relevant characteristic parameters

特征参数	描述
DHW	前车跟驰时距
X^CL	换道的纵向距离长度

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表 4 跟驰车辆平均速度拟合结果

Table 4. Fitting results of average speed of following vehicles

分布参数	均值	方差	对数似然值
正态分布	19.257 1	4.554 8	-6 371.68
对数正态分布	19.260 7	4.945 7	-6 463.08

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表 5 跟驰车辆平均间距拟合结果

Table 5. Fitting results of average distance between following vehicles

分布参数	均值	方差	对数似然值
正态分布	45.261 1	478.214	-33 009.1
对数正态分布	45.910 7	611.924	-32 501.6

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表 6 换道纵向位移拟合结果

Table 6. Longitudinal displacement fitting results for lane change

分布参数	均值	方差	对数似然值
正态分布	115.515	494.296	-4 420.55
对数正态分布	115.604	557.605	-4 449.47

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表 7 换道纵向位移均值对比

Table 7. Comparison of mean values of longitudinal displacements for lane change

编号	作者	均值	道路类型
1	本文	115.515	城市快速道路
2	马小龙等^[6]	148.08	环城高速公路

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表 8 换道启动时EV与IPV时距拟合结果

Table 8. Fitting results of EV and IPV time distance at lane change initiation

分布参数	均值	方差	对数似然值
正态分布	31.436 9	1 739.57	-3 341.61
对数正态分布	28.904 9	901.271	-2 764.83

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表 9 换道启动时EV与TPV时距拟合结果

Table 9. EV and TPV time-distance fitting results at lane change initiation

分布参数	均值	方差	对数似然值
正态分布	50.685 9	1 996.08	-1 658.95
对数正态分布	50.112	1 930.8	-1 515.49

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表 10 换道启动时EV与TFV时距拟合结果

Table 10. Results of fitting EV to TFV time distance at lane change initiation

分布参数	均值	方差	对数似然值
正态分布	34.438 5	1 929.61	-862.945
对数正态分布	32.639 9	1 521.91	-730.072

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