基于多目标雷达数据的单点交通信号控制方法

doi:10.13229/j.cnki.jdxbgxb20210313

摘要/Abstract

摘要：

针对多目标雷达检测器可以获取区域交通状态的特点，提出了一种基于多目标雷达数据的单点交叉口信号控制方法。首先，根据进口道检测区域排队长度情况，设计了单点信号控制策略，将单点信号控制划分为两个阶段。其次，分别确定两个阶段的信号控制方法。第一阶段主要解决常规车辆放行问题，建立了信号相位的切换规则。第二阶段主要解决进口道车辆排队过长以及出口道排队溢出问题，建立了相应的控制逻辑规则。在此基础上，确定了相关控制参数的取值与优化方法。最后，以实际典型交叉口为例对本文构建的控制方法进行仿真验证。结果表明，本文控制方法可以有效提升交叉口运行效率，平均车辆延误降低了9%，高交通需求下车辆排队长度降低了15.5%。

关键词: 交通信息工程及控制, 多目标雷达, 交通信号控制, 交通仿真

Abstract:

Aiming at the characteristics that the multi-target radar detectors can obtain the regional traffic performance， a signal control method at isolated intersections was r proposed based on multi-target radar data. First， according to the queue length of the detection area at the approach， a signal control strategy at isolated intersections is designed， and the signal control method is divided into two stages. Secondly， the signal control methods of the two stages are determined respectively. The first stage mainly solves the problem of conventional vehicle release， and establishes the signal phase transfer rule. The second stage mainly solves the problem of excessively long queues of vehicles at the approach and overflow of queues at the exit. The corresponding control logic rules are established. On this basis， the value and optimization method of related control parameters are determined. Finally， the actual typical intersection is taken as an example to simulate and verify the proposed signal control method. The results show that the proposed signal control method in this paper can effectively improve the operation efficiency of the intersection， the average vehicle delay is reduced by 9%， and the vehicle queue length under high traffic demand is reduced by 15.5%.

Key words: traffic information engineering and control, multi-target radar, traffic signal control, traffic simulation

中图分类号:

U491

刘东波,沈莉潇,代磊磊,陆建. 基于多目标雷达数据的单点交通信号控制方法[J]. 吉林大学学报(工学版), 2022, 52(10): 2456-2465.

Dong-bo LIU,Li-xiao SHEN,Lei-lei DAI,Jian LU. Traffic signal control method at isolated intersections based on multi-target radar data[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(10): 2456-2465.

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参考文献 24

1	王殿海, 金盛, 马东方, 等. 城市交通控制理论与方法[M]. 北京: 电子工业出版社, 2019.
2	曹阳. 基于模拟退火的交叉口自适应信号控制优化研究[J]. 交通运输工程与信息学报, 2018, 16(1): 49-55, 60.
	Cao Yang. Optimization of adaptive signal control using simulated annealing algorithm[J]. Journal of Transportation Engineering and Information, 2018, 16(1): 49-55, 60.
3	李祥尘, 李进龙, 何梦辰. 基于相位优化的干线双向绿波协调控制方法[J]. 交通运输工程与信息学报, 2018, 16(1): 115-121.
	Li Xiang-chen, Li Jin-long, He Meng-chen. A phase optimization based two-way green wave control strategy for urban arterials[J]. Journal of Transportation Engineering and Information, 2018, 16(1): 115-121.
4	Miller A J. A computer control system for traffic network[C]∥Proc of the 2nd International Symposium on Theory of Road Traffic Flow, London, UK,1963:201-220.
5	Gartner N H. OPAC: a demand-responsive strategy for traffic signal control[J]. Transportation Research Record, 1983, 906: 75-84.
6	Vincent R A, Young C P. Self-optimizing traffic signal control using microprocessors—the TRRL' Mova' strategy for isolated intersections[J]. Traffic Engineering& Control, 1986, 27(7/8): 385-387.
7	Crabtree M R, Henderson I R. MOVA traffic control manual application guide [M]. Hongkong: TRI Limited, 2005.
8	徐洪峰, 柳爽, 张栋, 等. 单点全感应式信号控制方法的参数取值[J]. 吉林大学学报: 工学版, 2019, 49(1): 45-52.
	Xu Hong-feng, Liu Shuang, Zhang Dong, et al. Configuring parameters of fully actuated control at isolated signalized intersections[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(1): 45-52.
9	徐洪峰, 耿现彩. 面向Ｔ形交叉口的机动车相位固定最小绿灯时间计算[J]. 吉林大学学报: 工学版, 2011, 42(3): 600-605.
	Xu Hong-feng, Geng Xian-cai. Absolute minimum green time calculation for vehicles at 3-leg intersection[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 42(3): 600-605.
10	Wang X B, Yin K, Liu H. Vehicle actuated signal performance under general traffic at an isolated intersection[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 582-598.
11	罗小芹, 王殿海, 金盛. 面向混合交通的感应式交通信号控制方法[J]. 吉林大学学报: 工学版, 2019, 49(3): 695-704.
	Luo Xiao-qin, Wang Dian-hai, Jin Sheng. Traffic signal actuated control at isolated intersections for heterogeneous traffic[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(3): 695-704.
12	Feng Y H, Head K L, Khoshmagham S, et al. A real-time adaptive signal control in a connected vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2015, 55: 460-473.
13	Guo Q, Li L, Ban X. Urban traffic signal control with connected and automated vehicles: a survey[J]. Transportation Research Part C: Emerging Technologies, 2019, 101: 313-334.
14	Kamal M, Hayakawa T, Imura J. Development and evaluation of an adaptive traffic signal control scheme under a mixed-automated traffic scenario[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(2): 590-602.
15	Feng Y, Zheng J, Liu H. Real-time detector-free adaptive signal control with low penetration of connected vehicles[J]. Transportation Research Record: Journal of the Transportation Research Board, 2018, 2672(18): 35-44.
16	Yao Z H, Jiang Y S, Zhao B, et al. A dynamic optimization method for adaptive signal control in a connected vehicle environment[J]. Journal of Intelligent Transportation Systems, 2019, 24(2): 184-200.
17	Li L, Lv Y, Wang F Y, Traffic signal timing via deep reinforcement learning[J]. Traffic Signal Timing Via Deep Reinforcement Learning, 2016, 3(3): 247-254.
18	孙浩, 陈春林, 刘琼, 等. 基于深度强化学习的交通信号控制方法[J]. 计算机科学, 2020, 47(2): 169-174.
	Sun Hao, Chen Chun-lin, Liu Qiong, et al. Traffic signal control method based on deep reinforcement learning[J]. Computer Science, 2020, 47(2): 169-174.
19	Chu T S, Wang J, Codeca L, et al. Multi-agent deep reinforcement learning for large-scale traffic signal control[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(3): 1-10.
20	Tan K L, Poddar S, Sarkar S, et al. Deep reinforcement learning for adaptive traffic signal control[C]∥ ASME Dyn Syst Control Conf, Columbus,USA,2019.
21	Genders W, Razavi S. Asynchronous N-step Q-learning adaptive traffic signal control[J]. Journal of Intelligent Transportation Systems, 2019, 23(4): 319-331.
22	唐杰祯. 基于广域雷达的单点信号控制配时优化研究[D]. 南京:东南大学交通学院, 2019.
	Tang Jie-zhen. Research on timing optimization of single-point signal control based on wide area radar[D]. Nanjing: College of Transportation,Southeast University, 2019.
23	安娜. 基于多目标跟踪雷达的单点全感应式信号控制方法[D]. 大连:大连理工大学交通运输学院, 2020.
	An Na. Fully-actuated signal control at isolated intersections based on multi-target tracking radar[D]. Dalian: School of Transportation, Dalian University of Technology, 2020.
24	别一鸣, 姜凯, 汤茹茹, 等. 考虑方案过渡影响的单点交通控制时段划分方法[J]. 吉林大学学报:工学版, 2019, 49(6): 1844-1851.
	Bie Yi-ming, Jiang Kai, Tang Ru-ru, et al. Time of interval partition for traffic control at isolated intersection considering impacts of plan transition[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(6): 1844-1851.

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信号参数		取值
α/（veh·km^-1）		70
β/（veh·km^-1）		40 veh/km
ξ_p		0.7
Δt/s		1
P1/P5（东西直行）	最小绿/s	20
P1/P5（东西直行）	最大绿/s	45
P2/P6（东西左转）	最小绿/s	10
P2/P6（东西左转）	最大绿/s	30
P3/P7（南北直行）	最小绿/s	20
P3/P7（南北直行）	最大绿/s	40
P4/P8（南北左转）	最小绿/s	10
P4/P8（南北左转）	最大绿/s	30

	P1	P2	P3	P4	P5	P6	P7	P8
P1		2	4	1			0	6
P2	3		1	0			4	5
P3	2	4		3	4	5
P4	4	4	2		0	4
P5			0	4		3	4	0
P6			1	4	3		0	0
P7	6	2			2	6		3
P8	0	0			5	5	3

参数	取值
车辆组成类型	95% Car，5% Bus
驾驶行为模型	Wiedemann 74
驾驶期望车速	50（45~55）km/h
仿真运行时段	0~11400 sim.sec
数据采样时段	600~11400 sim.sec
数据采样间隔	300 sim.sec
仿真运行步长	5 time steps/sim.sec

项目		均值	标准差	延误降低	P值	显著性
低需求	新方法	27.94	4.12	8.69%	0.000	显著
低需求	老方法	30.60	4.35	8.69%	0.000	显著
中需求	新方法	41.27	6.32	9.73%	0.002	显著
中需求	老方法	45.72	6.91	9.73%	0.002	显著
高需求	新方法	69.51	11.81	8.82%	0.000	显著
高需求	老方法	76.23	12.04	8.82%	0.000	显著

项目		平均排队长度/m	排队减少
低需求	新方法	31.34	3.33%
低需求	老方法	32.42	3.33%
中需求	新方法	53.21	10.47%
中需求	老方法	59.43	10.47%
高需求	新方法	73.32	15.54%
高需求	老方法	86.81	15.54%