基于车速引导和感应控制的干线协调优化方法

doi:10.16182/j.issn1004731x.joss.23-0315

摘要/Abstract

摘要：

干线信号协调通常基于固定带速和时段性统计流量，但实际运行中，车辆的行驶车速和交通流量往往呈波动变化，导致信号方案与实际最优带速和交通流需求不相匹配，影响交叉口的通行效率。基于车路协同环境，运用Maxband模型，以绿波带宽最大、干线车辆延误、干线停车次数与次路方向延误最小为优化目标，建立了干线信号协调多目标优化模型，并采用改进的多目标粒子群算法对模型求解，获得协调路口全局控制方案参数；根据交叉口进口道车辆位置和信号状态提出了车速引导模型；根据干线交叉口进口道的车辆饱和度情况，应用感应控制策略在全局协调的基础上对各路口绿灯时间进行实时调整。仿真结果表明：优化模型将车速引导和信号协调相结合，考虑主线路口负荷度情况，动态调整绿灯时间，降低了交叉口处的延误和停车次数，有效提升了干线协调控制效率。

关键词: 车速引导, 多目标优化, 信号交叉口, 干线信号协调, 交通仿真

Abstract:

Arterial signal coordination is usually based on fixed belt speeds and time-of-day statistical flows. Actually, vehicle speeds and traffic flows are fluctuating, which causes to the mismatch between the signal scheme and the actual optimal belt speeds and traffic flow demands, and affects the intersection's traffic efficiency. Based on the vehicle infrastructure cooperation, by applying Maxband model and the maximum green wave bandwidth, the minimum number of arterial vehicle delays, arterial stops and the minor direction delays being the optimization objectives, a multi-objective optimization model for arterial signal coordination is established. Through using an improved multi-objective particle swarm algorithm the model is solved to obtain the parameters of the coordinated intersection global control scheme. A vehicle speed guidance model is proposed based on the intersection inlet lane vehicle location and signal state. According to the vehicle saturation of the inlet lane of the arterial intersection, an inductive control strategy is applied to adjust the green light timing of each intersection in real time on the basis of global coordination. The results show that the optimization model combines the speed guidance and signal coordination, considers the mainline intersection loading degree situation, dynamically adjusts the green time, reduces the number of delays and stops at intersections, and can effectively improve the efficiency of arterial coordination control.

Key words: speed guidance, multi-objective optimization, signal intersections, arterial signal coordination, traffic simulation

中图分类号:

TP391.9

邓明君,胡辛瑕,李响等 . 基于车速引导和感应控制的干线协调优化方法[J]. 系统仿真学报, 2024, 36(6): 1309-1321.

Deng Mingjun,Hu Xinxia,Li Xiang,et al . Arterial Coordination Optimization Method Based on Vehicle Speed Guidance and Inductive Control[J]. Journal of System Simulation, 2024, 36(6): 1309-1321.

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交叉口	相位	绿灯时间	红灯时间	周期
丽景路	西进口左转	15	3	100
	南北直行	65	0
	南直行左转	14	3
怡园路	西进口左转	15	3	100
	南北直行	65	0
	南直行左转	14	3
世贸路	西进口左转	18	3	100
	南北直行	60	0
	南直行左转	16	3

交叉口	西进口		南进口		北进口
交叉口	左转	右转	左转	直行	直行	右转
丽景路	157	331	133	1 994	1 182	113
怡园路	285	176	151	2 287	1 257	132
世贸路	131	159	229	1 995	618	123

方案	绿波带宽/s		平均行程时间/s	干线车均延误/(s/pcu)	干线平均停车次数/(次/pcu)	次路方向车均延误/(s/pcu)
方案	上行	下行	平均行程时间/s	干线车均延误/(s/pcu)	干线平均停车次数/(次/pcu)	次路方向车均延误/(s/pcu)
原方案	39	22	156.56	40.12	1.14	89.49
Maxband方案	52	26	129.87	27.61	0.74	55.23
仅车速引导方案	47	29	125.39	21.83	0.61	41.40
本文优化方案 (车速引导+绿灯调整)	63	33	113.56	17.88	0.43	38.03

结果对比	平均行程时间	干线车均延误	干线平均停车次数	次路方向车均延误
本文相对于原方案	-27.5	-55.4	-62.3	-57.5
本文相对于Maxband方案	-12.6	-35.2	-41.9	-31.1
本文相对于仅车速引导方案	-9.4	-18.1	-29.5	-8.1

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