Dynamic Control Method for Intersection Space Resources in
Mixed Traffic Environment

doi:10.16097/j.cnki.1009-6744.2023.06.007

Abstract

Abstract: Due to the essential difference of trajectory controllability between Connected-automated Vehicle (CAV) and Connected Human-driven Vehicle (CHV), the proposed signal control optimization methods for mixed-traffic of CAVs and CHVs at intersections do not consider the dynamic adjustment of approach lane utilization due to the change of CAV penetration rate. This paper proposes a dynamic CAV-dedicated lane allocation method to avoid using transitional or inefficient CAV- dedicated lanes. In addition, a collaborative optimization algorithm for CAV trajectory and signal control parameters is developed to save the start-up loss time and maximize the utilization of green time. The simulation results show that the proposed method can reduce the average delay per vehicle at intersections by 17.3% or more compared with that of a fully actuated signal control scheme. And it is necessary to drive the CAVs in one or more CAV-dedicated lanes when the CAV penetration rate exceeds 0.33. Compared with the optimization strategy by a previous study (Niroumand et al.), the proposed method is more suitable for multi-lane signalized intersection with high saturation and high CAV penetration rate. Further analysis shows that the length of road segment, CAV penetration rate and maximum speed are sensitive to the optimization results of the proposed method.

Key words: intelligent transportation, dynamic CAV-dedicated lane allocation, conversion index, approach lane, trajectory control, signal timing optimization

摘要： 因CAV(Connected-automated Vehicle)与CHV(Connected Human-driven Vehicle)行驶轨迹可控性的本质差异，当前人机混合驾驶环境下信号交叉口优化控制方法鲜有考虑CAV渗透率变化对进口车道资源利用动态调节的需求。鉴于此，本文提出一种CAV专用车道的动态分配方法，以避免过渡或低效使用CAV专用车道。此外，还建立了CAV轨迹与信号控制参数的协同优化算法，以节省启动损失时间，最大限度地利用绿灯时间。案例分析表明：与全感应信号控制策略相比，本文提出的优化策略能使交叉口车均延误下降17.3%以上，当CAV渗透率超过0.33时，有必要配置一条或多条CAV专用车道来分离CAVs与CHVs的通行，以提高交叉口的通行效率；同时，与Niroumand等的优化策略相比，本文提出的方法更适合高饱和度、高CAV渗透率的多车道信号交叉口。进一步分析表明，路段长度、CAV渗透率、最大速度对本文提出方法的优化结果敏感性强。

关键词: 智能交通, CAV专用车道动态分配, 转换指数, 进口车道, 轨迹控制, 配时优化

CLC Number:

U491

JIANG Xian-cai, XU Hui-zhi. Dynamic Control Method for Intersection Space Resources in Mixed Traffic Environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 63-73.

蒋贤才, 徐慧智. 混合驾驶环境下交叉口空间资源动态控制方法[J]. 交通运输系统工程与信息, 2023, 23(6): 63-73.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2023.06.007

http://www.tseit.org.cn/EN/Y2023/V23/I6/63

References

[1] GUO Y, MA J, XIONG C, et al. Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach[J]. Transportation Research Part C: Emerging Technologies, 2019, 98: 54-72.

[2] POURMEHRAB M, ELEFTERIADOU L, RANKA S, et al. Optimizing signalized intersections performance under conventional and automated vehicles traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21: 2864-2873.

[3] YAO Z, ZHAO B, YUAN T, et al. Reducing gasoline consumption in mixed connected automated vehicles environment: A joint optimization framework for traffic signals and vehicle trajectory[J]. Journal of Cleaner Production, 2020, 265: 121836.

[4] 李柏. 复杂约束下自动驾驶车辆运动规划的计算最优控制方法研究 [D]. 杭州: 浙江大学, 2018. [LI B. Research on computational optimal control methods for automated vehicle motion planning problems with complicated constraints[D]. Hangzhou: Zhejiang University, 2018.]

[5] 王润民, 张心睿, 赵祥模, 等. 混行环境下网联信号交叉口车路协同控制方法[J]. 交通运输工程学报, 2022, 22(3): 139-151. [WANG R M, ZHANG X R, ZHAO X M, et al. Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 139-151.]

[6] 刘天天, 莫磊, 陈思祺, 等. 智能网联环境下交叉口混行车队通行模型构建[J]. 交通运输研究, 2020, 6(6): 46-54. [LIU T T, MO L, CHEN S Q, et al. Construction of traffic model for mixed platoon at intersection under intelligent network environment[J]. Transport Research, 2020, 6(6): 46-54.]

[7] 刘春禹, 刘永红, 罗霞, 等. 混合交通流环境下单交叉口自动驾驶车辆轨迹优化[J]. 交通运输系统工程与信息, 2022, 22(2)et al. Trajectory optimization of connected vehicles at isolated intersection in mixed traffic environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 154-162.]

[8] 庞明宝, 柴紫欣, 巩丹阳. 混合交通下智能网联车借道公交专用车道控制[J]. 交通运输系统工程与信息, 2021, 21(4): 118-124. [PANG M B, CHAI Z X, GONG D Y. Control of connected and automated vehicles driving on dedicated bus lane under mixed traffic[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 118-124.]

[9] REY D, LEVIN M W. Blue phase: Optimal network traffic control for legacy and autonomous vehicles[J]. Transportation Research Part B: Methodological, 2019, 130: 105-129.

[10] NIROUMAND R, TAJALLI M, HAJIBABAI L, et al. Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream[J]. Transportation Research Part C: Emerging Technologies, 2020, 116: 102659.

[11] 宗芳, 李宇暄, 贺正冰, 等. 基于混行跟驰仿真的CAV专用进口道动态设置方法[J]. 中国公路学报, 2022, 35 (7): 251-260. [ZONG F, LI Y X, HE Z B, et al. Dynamic layout design of connected and automated vehicle entrance lanes based on mixed-traffic car-following simulations[J]. China Journal of Highway and Transport, 2022, 35(7): 251-260.]

[12] BAHRAMI S, ROORDA M J. Optimal traffic management policies for mixed human and automated traffic flows[J]. Transportation Research Part A: Policy and Practice, 2020, 135: 130-143.

[13] BRETHERTON R D, BODGER M, COWLING J. SCOOT-managing congestion, control and communications[J]. Traffic Engineering & Control, 2006, 47(3): 88-92.

[14] DION F, REKHA H, KANG Y S. Comparison of delay estimates at under-saturated and over-saturated pretimed signalized intersections[J]. Transportation Research Part B: Methodological, 2002, 38: 99-122.

[15] YU X, SUN L, YAN Y, et al. A short-term traffic flow prediction method based on spatial-temporal correlation using edge computing[J]. Computers and Electrical Engineering, 2021, 93: 107219.

[16] TAN H, WU Y, SHEN B, et al. Short-term traffic prediction based on dynamic tensor completion[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8): 1-11.

[17] PANWAI S, DIA H. Comparative evaluation of microscopic car-following behaviour[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(3): 314-325.

[18] YAO H, LI X. Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads[J]. Transportation Research Part C: Emerging Technologies, 2021, 129: 103182.

Dynamic Control Method for Intersection Space Resources in Mixed Traffic Environment

混合驾驶环境下交叉口空间资源动态控制方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	CHEN Zheng, ZHAO Wenlong, GUO Fengxiang, ZHAO Zhigang, LIU Yu, LIU Yonggang. Lane Change Trajectory Planning of Intelligent Vehicle Considering Safety and Comfort [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 55-65.
[2]	PEI Yulong, FU Bohan, WANG Ziqi, ZHANG Jie. Comprehensive Competitiveness-based Autonomous Driving Human-imitative Lane-changing Model Under Gravity Theory [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 66-80.
[3]	LI Chuanyao, ZHANG Fan, WANG Tao, HUANG Dexin, TANG Tieqiao. Signalized Intersection Eco-driving Strategy Based on Deep Reinforcement Learning [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 81-92.
[4]	WANG Dan, LIN Ye. Mechanisms of Effect of Warning Stimuli on Driver Takeover Performance [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 106-114.
[5]	LI Yan, WANG Taizhou, XU Jinhua, CHEN Jianghui, WANG Fan. Traffic Demand Prediction Method Based on Deep Learning for Dynamic Traffic Assignment [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 115-123.
[6]	REN Qiliang, XU Tao, LIU Yuan, CHENG Longchun. Anomaly Data Detection Algorithm of Improvement Isolated Forest for Floating Car Data Collection Considering Passenger Carrying Status [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 124-131.
[7]	YUAN Jian, LIU Fuqiang, AN Kun, ZHENG Zhe, MA Wanjing, YU Qiutian. Lane-level Traffic Flow Tracing Method Based on Traffic Shockwave Features [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 159-167.
[8]	QU Da-yi, MENG Yi-ming, WANG Tao, SONG Hui, CHEN Yi-cheng. Car-following Model and Safety Characteristics of Connected Autonomous Vehicle Based on Molecular Force Field [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 33-41.
[9]	GUO Feng-xiang, HUANG Jin-tao, CHEN Yu-guang, GUO Yan-yong, LIU Pan. Combined Approximate Method for Traffic Data Imputation Under Multiple Missing Modes [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 42-50.
[10]	LI Tai-guo, ZHANG Tian-ce, LI Chao, ZHOU Xing-hong. Driver Fatigue Detection Based on Facial Inverted Pendulum Model and Information Entropy [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 24-32.
[11]	JIANG Xian-cai, CHENG Guo-zhu. Dynamic Configuration Method for Approach Lanes at Intersections in Connected Traffic Environment [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 55-66.
[12]	LI Peng-cheng, DONG Bao-tian, LI Si-xian. Intersection Traffic State Recognition and Correlation Degree Study Based on Temporal Graph Attention [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 67-74.
[13]	CAI Hao, LI Lin-feng, LI Han, LI Xin, ZHOU Teng. Short-term Traffic Flow Prediction Based on ASO-ELM Hybrid Optimization Model [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 75-82.
[14]	LONG Ke-jun, ZHANG Zhong-gen, LIU Yang, GAO Zhi-bo. Cooperative Optimization Model for Emergency Vehicles Using Reverse Left-turn Lanes and Traffic Signals [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 194-201.
[15]	FANG Hua-zhen, LIU Li, XIAO Xiao-feng, GU Qing, MENG Yu. Vehicle Trajectory Prediction Based on Mixed Teaching Force Long Short-term Memory [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(4): 80-87.