基于智能网联车的高速公路移动瓶颈实时检测方法

李凯; 孙佳; 陈非; 唐颜东; 曹鹏

doi:10.3963/j.jssn.1674-4861.2024.05.003

基于智能网联车的高速公路移动瓶颈实时检测方法

doi: 10.3963/j.jssn.1674-4861.2024.05.003

李凯^1,,
孙佳²,
陈非¹,
唐颜东^{1, 2},
曹鹏^2, ,

1.
四川智慧高速科技有限公司成都 610041
2.
西南交通大学交通运输与物流学院成都 611756

基金项目:

国家自然科学基金项目 61903313

国家自然科学基金项目 52172395

详细信息

作者简介:
李凯（1979—），高级工程师. 研究方向：智慧交通与交通信息化. E-mail：4760010@qq.com

通讯作者:
曹鹏（1988—），博士，副教授. 研究方向：智能交通系统. E-mail：caopeng@swjtu.edu.cn

中图分类号: U491.2
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- 文章访问数: 86
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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-02
- 网络出版日期: 2025-01-22

A Method for Real-time Detecting Freeway Moving Bottlenecks Using Intelligent Connected Vehicles

LI Kai^1
,,
SUN Jia²,
CHEN Fei¹,
TANG Yandong^{1, 2},
CAO Peng^{2
, ,}

1.
Sichuan Intelligent Highway Technology Co., Ltd., Chengdu 610041, China
2.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China

摘要

摘要: 针对基于定点的检测方法无法有效监测移动瓶颈的形成及演变态势问题，研究了1种基于智能网联车的高速公路移动瓶颈实时检测方法。结合智能车轨迹数据特性，提出基于小波分析的轨迹降噪方法。基于轨迹形态与交通状态之间的关系，识别智能车轨迹中体现交通状态突变的关键点。由于同1个时刻路段上存在多个交通拥堵，为确定关键点所属交通波，提出基于交通波时空分布特性的关键点分类算法。基于关键点计算交通波波速并估算排队长度，以对识别的移动瓶颈进行评价。基于SUMO仿真平台，以沪嘉高速为对象，对各种智能车占比下移动瓶颈位置和传播速度的检测效果以及排队延误情况开展了试验。结果表明：当高速路上智能车渗透率小于10%，轨迹降噪所带来交通波波速估计精度平均提升20%；当渗透率超过3%，对移动瓶颈传播速度的估计误差在0.42 m/s以下；当渗透率达到7%，移动瓶颈位置估计偏差大多在10 m以内，不超过25 m。本文方法可以实时地检测高速路上随机突发的移动瓶颈并评价其严重程度。
- 智能交通 /
- 高速路交通状态 /
- 移动瓶颈检测 /
- 小波分析 /
- 交通波 /
- 智能网联车
Abstract: Aiming at the problem that the fixed-point detection method cannot effectively monitor the formation and evolution of the mobile bottleneck, a real-time detection method of the mobile bottleneck on the expressway based on intelligent networked vehicles is studied. A wavelet analysis-based method is proposed to reduce the errors of trajectories collected by intelligent connected vehicles (ICVs). And then the key points that represent the change of traffic states are identified based on the relationship between the vehicle trajectories and the traffic states. Considering that multiple traffic congestions may simultaneously occur on a road segment, an algorithm is proposed to classify the key points based on the space-time characteristics of traffic shockwaves. Finally, the traffic shockwave speed is calculated, and moving bottlenecks are identified and evaluated. Based on SUMO simulation platform, experiments are carried out on the detection effect of mobile bottleneck location, propagation speed and queuing delay under the proportion of various intelligent vehicles in Hujia freeway. The results show that when the penetration rate of ICVs is less than 10%, the accuracy of traffic wave speed estimation improves by an average of 20% after trajectory denoising. When the penetration rate exceeds 3%, the estimation error of the moving bottleneck propagation speed is below 0.42 m/s. When the penetration rate reaches 7%, the estimated position of the moving bottleneck has a deviation mostly within 10 m, with a maximum of 25 m. The proposed method can detect the presence of freeway bottlenecks which occur randomly and evaluate their severity in real-time.
- intelligent transportation /
- freeway traffic state /
- moving bottleneck detection /
- wavelet analysis /
- traffic shockwave /
- intelligent connected vehicle

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图 1 移动瓶颈示意图

Figure 1. Diagram of a moving bottleneck

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图 2 左、右车道内所有车辆的完整轨迹

Figure 2. Complete trajectories of all vehicles in the left and right lanes

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图 3 实时算法流程图

Figure 3. Flowchart of real-time algorithm

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图 4 关键点与交通波的对应关系

Figure 4. Correspondence between turning points and traffic shockwaves

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图 5 全样本轨迹热力图

Figure 5. Heatmap of full-sample vehicle trajectories

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图 6 智能车数据采集系统的系统架构

Figure 6. System framework of intelligent connected vehicle data acquisition system

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图 7 降噪前后交通波速度估计结果

Figure 7. Estimation results of traffic shockwave speed before and after denoising

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图 8 车辆排队延误估计相对偏差

Figure 8. Relative deviation of estimated vehicle queue delay

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图 9 移动瓶颈检测计算用时

Figure 9. Computing time for moving bottleneck detection

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表 1 高速公路上的车型组成及车辆参数

Table 1. Vehicle composition and vehicle parameters on freeways

车辆类型	车长/m	车宽/m	比例
小客车	5	1.8	0.66
中客车	6	2	0.15
大客车	12	2.5	0.07
轻型货车	5.7	2	0.04
中型货车	7	2.5	0.08

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表 2 参数说明及取值

Table 2. Parameter description and values

参数	参数说明	取值	参数	参数说明	取值
D_min /m	判断属于同一交通波的2个关键点之间的最小空间间隔	30	T_min/（m/s）	判断属于同一交通波的2个关键点之间的最小时间间隔	2
slope_ min/（m/s）	移动瓶颈传播速度范围的下限	3	slop_ max/(m/s)	移动瓶颈传播速度范围的上限	19
D_max /m	判断属于同一交通波的2个关键点之间的最大空间间隔	1 000	T_max /s	判断属于同一交通波的2个关键点之间的最大时间间隔	165
D_rea /m	判断属于同一交通波的2个关键点之间的合理空间间隔	200	T_rea /s	判断属于同一交通波的2个关键点之间的合理时间间隔	14s

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表 3 交通波波速估计结果

Table 3. Results of traffic shockwave speed estimation

波速类型	智能车渗透率/%	左车道交通波1速度/（m/s）	左车道交通波2速度/（m/s）	左车道交通波3速度/（m/s）	左车道估计波速的RMSE/（m/s）	右车道交通波1速度/（m/s）	右车道交通波1速度/（m/s）	右车道估计波速的RMSE/（m/s）
真实波速	0	7.87	-4.20	4.74		-4.86	4.64
估计波速	1		-6.54	3.00	2.06		3.40	1.24
估计波速	3	7.45	-6.25	3.65	1.36	-5.68	3.58	0.95
估计波速	5	8.10	-6.14	4.59	1.13	-6.13	4.73	0.90
估计波速	7	7.86	-5.72	5.29	0.93	-5.97	4.64	0.78
估计波速	9	7.86	-5.66	4.09	0.92	-5.78	4.97	0.70
估计波速	20	7.86	-5.62	4.70	0.82	-5.47	4.49	0.44
估计波速	50	7.68	-5.26	4.94	0.63	-5.09	4.94	0.27
估计波速	100	7.82	-5.04	4.69	0.49	-5.09	4.64	0.16

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表 4 智能车渗透率7%下移动瓶颈位置的实时检测结果

Table 4. Results of real-time detecting moving bottlenecks under 7% penetration rate of intelligent connected vehicles

关键点序号	时刻/s	真实位置/m	估计位置/m	估计误差/m
1	187.6	1 586.97	1 589.60	2.63
2	200.0	1 703.62	1 688.80	-14.82
3	201.8	1 704.89	1 703.20	-1.69
4	204.6	1 736.90	1 725.60	-11.3
5	210.8	1 786.11	1 775.20	-10.91
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
91	690.7	5 604.70	5 614.40	9.7
92	691.4	5 596.39	5 620.00	23.61
93	697.8	5 667.35	5 671.20	3.85
94	699.0	5 667.11	5 680.80	13.69

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