快速路车路协同场景交通流运行效率仿真评价

孙立山; 陈颖达; 孔德文; 张桐; 宋咏昌

doi:10.3963/j.jssn.1674-4861.2021.01.0018

快速路车路协同场景交通流运行效率仿真评价

doi: 10.3963/j.jssn.1674-4861.2021.01.0018

北京工业大学北京市交通工程重点实验室北京 100124

基金项目:

北京市科技计划课题项目 Z191100002519002

北京市教育委员会科技计划一般项目 KM202110005002

详细信息

作者简介:
孙立山(1980—)，博士，教授.研究方向：交通运输规划与管理.E-mail: lssun@bjut.edu.cn

通讯作者:
孔德文(1989—)，博士，讲师.研究方向：交通运输规划与管理.E-mail: kongdw@bjut.edu.cn

中图分类号: U491.2+6
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出版历程
- 收稿日期: 2020-12-18
- 刊出日期: 2021-02-28

A Simulation Evaluation of Traffic Flow Efficiency of Urban Expressways under Cooperative Vehicle-infrastructure Scenarios

Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China

摘要

摘要: 为研究车路协同系统在不同车间信息交互水平下对快速路交通流的影响，采集并提取北京市四方桥快速路段早高峰交通流轨迹，同时分析车路协同场景下快速路车辆运行特征，实现对常规驾驶场景和信息交互场景的车辆行驶模型标定。选取期望速度行驶车辆占比、横向车距收缩比、纵向车距收缩比、通行能力拓展比对车辆运行效率进行评价，提出车辆横向偏移距离缩小比用以评价车辆空间占用状况；搭建仿真模型并进行仿真试验，分析不同信息交互水平对交通的影响程度。结果显示，车辆运行效率随信息交互水平的提升而提升，其中通行能力的提升幅度最为显著，车路协同场景下信息交互水平从4级提高到1级，道路通行能力较常规驾驶场景分别拓展了19.42%，28.06%，46.48%，74.62%，仿真时段内其余指标值提升幅度较小。车间信息交互场景下车辆行驶的横向偏移幅度缩小，且在高水平信息交互下缩小比例达到17.33%，表明相同车道宽度下车辆行驶的横向安全性提升。
- 智能交通 /
- 车路协同 /
- 交通仿真 /
- 车间通信 /
- 快速路 /
- 评价指标
Abstract: This paper collects and extracts vehicle trajectory data on the road section of Sifang Bridge in Beijing during the morning peak hour to study impacts of the cooperative vehicle-infrastructure system(CVIS)on expressways under different levels of information interaction between vehicles. Driving behaviors of vehicles are analyzed under the vehicle-road collaboration scenario to calibrate parameters of the conventional driving scenes and information interaction scenes for driving models. The ratios of vehicles running at desired speed, transverse vehicle distance contraction, longitudinal vehicle distance contraction, and traffic capacity expansion are selected to evaluate operating efficiency of the vehicles, and the ratio of vehicle lateral offset distance reduction is selected as an index to evaluate space occupancy rate in the vehicle-road collaboration scenario. A simulation model is developed to analyze the impacts of different levels of information interaction on traffic flow. The results show that vehicle operation efficiency increases with an improved level of information interaction, and improvement of road capacity is the most significant. When the level of information interaction increased from level 4 to level 1, the traffic capacity is expanded by 19.42%, 28.06%, 46.48%, and 74.62%, respectively, and the rest of the index values are slightly improved during the simulation period. The vehicle lateral offset distance under the information interaction scenario is reduced, and the reduction ration is 17.33% under the scenario of level 1 information interaction. It indicating that the lateral safety of the vehicle under the same lane width is improved.
- intelligent transportation /
- CVIS /
- traffic simulation /
- vehicle-vehicle communication /
- urban expressway /
- evaluation indicator

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图 1 驾驶行为标定界面

Figure 1. Driving-behavior calibration interface

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图 2 北京市四方桥路段

Figure 2. Sifang Bridge section in Beijing

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图 3 仿真运行界面

Figure 3. Simulation operation interface

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图 4 仿真评价指标变化趋势

Figure 4. Variation trend of simulation evaluation indices

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表 1 各类驾驶行为在车路协同场景下的变化对比表

Table 1. Comparison of changes in various driving behaviors under vehicle-road collaboration scenarios

驾驶行为	无信息交互条件下的车辆运行状态	信息交互条件下的车辆运行状态
跟车行为	驾驶员反应时间较长、车头间距较大、车辆仅可获取可视范围内车辆信息	反应时间缩短，车头间距和车辆行驶速度更稳定(无震荡)、车辆可获取附近一定范围内的车辆信息
车道变换	驾驶员反应与决策时间较长、等待变道时间较长，容易出现急加急减速行为	驾驶员反应与决策时间较短、等待变道时间较短，车速变化平缓

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表 2 仿真模型参数取值

Table 2. Parameter values of the simulation model

模型	参数	1级	2级	3级	4级	现状
Wiedemann74跟驰模型	平均停车间距/m	1.0	1.5	2.0	2.5	3.0
	附加安全距离/m	1.0	1.5	2.0	2.5	3.0
	安全距离增加部分/m	2.0	2.5	3.0	3.5	3.0
	最大前视距离/m	250	200	150	100	100
	最大后视距离/m	150	120	90	60	80
	观察前方车辆数/pcu	1.0	2.0	3.0	4.0	2.0
	暂时走神持续时间/s	0.3	0.6	0.9	1.2	1.0
	暂时走神概率/%	1.0	2.0	3.0	4.0	4.0
换道模型	最大减速度/(m/s²)	2.0	3.0	4.0	5.0	5.0
	-1 m/s²的距离/m	100	80	60	40	40
	可接受的减速度/(m/s²)	1.0	1.5	2.0	2.5	3.0
	等待换道消失时间/s	45.0	60	75.0	90	60
	最小车头时距/m	0.5	0.8	1.0	1.25	1.5
	协调刹车的最大减速度/(m/s²)	3.0	2.6	2.2	1.8	1.6

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表 3 模型验证结果

Table 3. Results of model validation

标定指标	速度/(m/s)				位置/m
标定指标	ME	MAE	MARE	RMSE	ME	MAE	MARE	RMSE
计算值	-0.16	2.14	0.06	2.43	0.17	1.86	0.04	2.17

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表 4 仿真输出结果

Table 4. Simulation output result

影响因素	1级	2级	3级	4级	现状
最大饱和通行能力/[pcu/(h/ln)]	2 284	1 916	1 675	1 562	1 308
纵向车距/m	3.25	3.55	3.72	3.94	4.32
横向车距/m	1.83	1.96	2.16	2.31	2.48
最优速度样本量/pcu	921	903	831	798	541
车辆平均横向偏移距离/cm	21.94	22.86	24.28	25.34	26.54
注: 大型车换算系数取2.0^[26]。

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表 5 仿真评价指标计算值

Table 5. Calculated values of simulation evaluation indices %

对比参数	1级	2级	3级	4级	现状
期望速度行驶车辆占比	30.70	30.10	27.70	26.6	18.0
横向车距收缩比	26.21	20.97	12.90	6.85
纵向车距收缩比	24.77	17.82	13.89	8.80
车辆横向偏移距离缩小比	17.33	13.87	8.53	4.53
通行能力拓展比	74.62	46.48	28.06	19.42

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