高速公路连续瓶颈混合交通流可变限速与换道协同控制方法

邵敬波; 黄轲; 张兆磊; 高志波; 徐琥

doi:10.3963/j.jssn.1674-4861.2023.03.007

高速公路连续瓶颈混合交通流可变限速与换道协同控制方法

doi: 10.3963/j.jssn.1674-4861.2023.03.007

邵敬波^1,,
黄轲^2, ,,
张兆磊¹,
高志波¹,
徐琥³

1.
长沙理工大学交通运输工程学院长沙 410114
2.
武汉工程大学管理学院武汉 430205
3.
长沙智能驾驶研究院有限公司长沙 410114

基金项目:

国家自然科学基金项目 52172339

长沙市科技重大专项项目 kh2301004

湖南省自然科学基金青年项目 2021JJ40577

湖南省教育厅优秀青年项目 20B009

湖南省交通科技项目 202140

湖南省研究生科研创新项目 CX20220852

详细信息

作者简介:
邵敬波（1997—），硕士研究生. 研究方向：交通规划与管理. E-mail: jingbo_shao2020@163.com

通讯作者:
黄轲（1974—），博士，副教授. 研究方向：智能交通、车联网、信息管理. E-mail: huangke@wit.edu.cn

中图分类号: U491.5
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-10
- 网络出版日期: 2023-09-16

A Cooperative Control Method of Variable Speed Limit and Lane Change for Mixed Traffic Flow on Continuous Bottlenecks of Freeway

1.
School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
2.
School of Management, Wuhan Institute of Technology, Wuhan 430205, China
3.
Changsha Intelligent Driving Institute Ltd., Changsha 410114, China

摘要

摘要: 为缓解高速公路连续瓶颈区车辆强制换道造成的通行能力下降的问题，减轻瓶颈之间的相互干扰，提出了面向智能网联车辆（connected and automated vehicles，CAVs）与普通车辆混行情况的高速公路连续瓶颈可变限速与换道协同控制策略。对传统的细胞传输模型（cell transmission model，CTM）进行改进，使其更好地预测考虑了可变限速地混合交通流状态；基于实验模拟，得到了不同交通需求场景下合理的换道控制段长度，通过对瓶颈上游车流进行预先换道提醒，缓解因强制换道引发的通行能力下降现象，进而提高可变限速控制的效果，同时利用可变限速对高交通需求下的流量进行调控，为换道控制段内车辆能够完成预先换道提供保障；构建了连续瓶颈下协同控制框架，并以最小化总行程时间和速度差为目标，优化连续瓶颈的交通运行性能；分析了3种CAVs渗透率对协同控制的影响。结果表明：相比于无控制和可变限速控制，在协同控制下总行程时间分别降低了54.76%和33.05%，总速度差分别减少了86.84%和29.58%。此外，CAVs对协同控制性能和道路运行状况有着积极作用。当CAVs渗透率为0.5时最低限速值由渗透率为0时的30 km/h提高至60 km/h，当渗透率为1时限速值始终保持在自由流速度，随着CAVs渗透率的增加协同控制下系统的总行程时间可从239.64 h减少至158.86 h。研究可为高速路连续瓶颈和未来含CAVs的混合交通流主动管控提供参考。
- 交通工程 /
- 高速公路连续瓶颈 /
- 可变限速 /
- 换道控制 /
- 智能网联车辆
Abstract: A cooperative control method of variable speed limit and lane change for mixed traffic flow consisting traditional human-driven vehicles and connected and connected and automated vehicles (CAVs) is developed to mitigate the capacity drop due to mandatory lane change and mutual interference between bottlenecks in the freeway. The traditional cell transmission model (CTM) is modified to better predict the mixed traffic flow state under variable speed limit control. A reasonable length of lane change section under different traffic demands are then obtained using experimental simulation. The capacity drop due to mandatory lane change is alleviated by reminding the drivers of upstream vehicles. In this way, the performance for variable speed limit is improved. Meanwhile, variable speed limit is adopted to regulate high traffic demand for the vehicle to successfully change lane. The cooperative control framework is set up for continuous bottlenecks, where the traffic performance is optimized by minimizing the total travel time and speed difference. At last, different penetration rates of CAVs' impact on cooperative control is analyzed. Study results show that, compared to the results of no-control or only variable speed limit, the total travel time with cooperative control, respectively is reduced by 54.76% or 33.05%, and the total speed difference is reduced by 86.84% and 29.58%, respectively. In addition, study results also show that the penetration rate of CAVs has a positive impact on the performance of cooperative control and traffic operation: if the penetration rate increased from 0 to 0.5, the minimum speed limit can be increased from 30 km/h to 60 km/h, while the speed limit value is kept as the free speed when the penetration rate reaches 1; the total travel time reduces from 239.64 h to 158.86 h with cooperative control in place as the CAVs penetration rate increases. This study provides a reference for the active control of continuous bottlenecks of freeway under a mixed traffic environment.
- traffic engineering /
- continuous bottlenecks of freeway /
- variable speed limit /
- lane change control /
- connected and automated vehicle

HTML全文

图 1 高速公路连续瓶颈示意图

Figure 1. Schematic diagram of continuous bottlenecks of highway

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图 2 通行能力下降基本图

Figure 2. Fundamental diagram of capacity drop

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图 3 高速公路细胞传输模型原理图

Figure 3. Diagram of highway cell transmission model

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图 4 不同交通需求下所需的换道控制长度

Figure 4. Lane change control length under different traffic demands

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图 5 不同情况下协同控制

Figure 5. Cooperative control under different conditions

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图 6 协同控制框架

Figure 6. Framework of cooperative control

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图 7 仿真路段示意图

Figure 7. Diagram of simulation section

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图 8 EGA算法优化收敛情况

Figure 8. Optimization process of the EGA algorithm

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图 9 CAV渗透率为0时不同控制下2个瓶颈的交通状况

Figure 9. Traffic conditions of two bottlenecks under different control when CAV penetration rate is 0

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图 10 CAV渗透率为0.5时不同控制下2个瓶颈的交通状况

Figure 10. Traffic conditions of two bottlenecks under different control when CAV penetration rate is 0.5

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图 11 CAV渗透率为1时不同控制下2个瓶颈的交通状况

Figure 11. Traffic conditions of two bottlenecks under different control when CAV penetration rate is 1

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图 12 不同CAV渗透率下控制段1的可变限速值变化

Figure 12. Variable speed limit of section 1 under different CAV penetration rates

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图 13 通行能力与渗透率关系

Figure 13. Relationship between capacity and penetration rates

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图 14 总行程时间与渗透率关系

Figure 14. Relationship between total travel time and penetration rates

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表 1 CTM基本参数值

Table 1. Basic parameter values of CTM

参数	瓶颈1	瓶颈2
自由流速度/（km/h）	110	110
最大通行能力/（veh/h）	5 920	4 320
下降后通行能力/（veh/h）	5 180	3 790
冲击波波速/（km/h）	15.80	17.53
临界密度/（veh/km）	53.82	39.27
阻塞密度/（veh/km）	428.57	285.71

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表 2 车辆运动模型基本参数

Table 2. Basic parameters of vehicle motion model

参数	CAV	HDV
a_max/（m/s²）	2.5	2.5
b₀/m	2	2
τ_h/s	0.5	1.2
b_max/（m/s²）	2	2
b_safe/（m/s²）	4.5	4.5
L/m	5	5
γ	4	4
κ	0.5	0.1
Δa_th/（m/s²）	0.3	0.3

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表 3 不同控制策略下评估结果

Table 3. Evaluation results under different control strategies

控制类型	无控制	仅VSL	协同控制
TTT/h	529.71	357.92	239.64
TSD/（km/h）	21 921.53	4 096.67	2 884.77
变道率/%	72.34	80.92	76.16
平均CO₂/g	960.25	751.59	646.73
平均NO_x/g	0.376 5	0.290 2	0.247 8
平均油耗/g	0.412 8	0.323 1	0.278 1

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