考虑动态车速的双周期干线信号协调控制多目标优化

张靖思; 李振龙; 邢冠仰

doi:10.3963/j.jssn.1674-4861.2021.03.008

考虑动态车速的双周期干线信号协调控制多目标优化

doi: 10.3963/j.jssn.1674-4861.2021.03.008

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:

北京市交通行业科技项目 2020-kjc-02-180

详细信息

作者简介:
张靖思（1993—），硕士研究生.研究方向：交通控制.E-mail：zhangjingsi6514@163.com

通讯作者:
李振龙（1976—），博士，副教授.研究方向：智能交通.E-mail：lzl@bjut.edu.cn

中图分类号: U491.54
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出版历程
- 收稿日期: 2020-12-14

Multi-Objective Optimization for Coordinated Control of Double-cycling Arterial Signals Considering Dynamic Vehicle Speeds

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

摘要

摘要: 干线协调控制通常以干线方向通行效率最大为目标，导致一些小型交叉口次路方向延误较大。针对该问题，基于车路协同环境，研究了车速引导下的双周期干线多目标优化方法。针对上游交叉口饱和交通流与非饱和交通流2种情况，提出了考虑排队消散和相位差的动态车速引导模型。以干线延误、通行能力、停车次数，双周期交叉口次路方向延误为优化目标，构建了车速引导下的双周期干线多目标优化模型，采用遗传算法对模型进行求解。基于COM接口，采用Python和Vissim搭建车路协同仿真环境，以北京市两广路的3个路口为例进行仿真验证。对比了本文模型与原配时方案、无车速引导下双周期干线多目标优化模型的效果，结果表明，本文模型相比于原配时方案和无车速引导下多目标优化模型，干线平均延误分别减少19.6%，8.3%，通行能力分别提升5.6%，8.4%，平均停车次数分别减少11.2%，24.2%，双周期交叉口次路方向平均延误分别减少33.9%，5.8%，表明本文模型将速度引导与多目标优化相结合，提高了双周期干线的通行效率，降低了双周期交叉口次路方向的延误，达到了干线和双周期交叉口共同优化的目的。
- 交通控制 /
- 双周期干线协调 /
- 车速引导 /
- 多目标优化 /
- 遗传算法
Abstract: Arterial coordination control usually aims at maximizing the traffic efficiency in the main direction, which leads to a large delay in the cross street of some minor intersections.Based on the cooperative vehicle infrastructure, the work studies the multi-objective optimization method of double-cycling arterials under speed guidance.Aiming at the saturated and unsaturated traffic flow at the upstream intersection, a dynamic speed guidance model considering queue dissipation and offset is proposed.Furthermore, a double-cycling arterials multi-objective optimization model is constructed taking the average delay time, the average number of stops, the capacity of arterials, and the average delay of the double-cycling intersection as the comprehensive optimization objectives.Then, the genetic algorithm is used to solve the model to obtain the optimized coordinated signal-timing scheme.Based on the COM interface, the cooperative vehicle infrastructure environment is built using Python and Vissim software, and the model is simulated by taking three intersections of Guanganmen Inner Street in Beijing as a case study.The results of this model are compared with those of the original scheme and the multi-objective optimization model of the double-cycling artery without speed guidance.Compared with the original scheme and the multi-objective optimization model without speed guidance, the average delay of arterial is reduced by 19.6% and 8.3%; the capacity increased by 5.6% and 8.4%; the average number of stops is reduced by 11.2% and 24.2%; the average delay of the cross street of the double-cycling intersection re-duced by 33.9% and 5.8%, respectively.The results show that this model combines speed guidance with multi-objective optimization to achieve dynamic speed guidance, with the increased traffic efficiency of the double-cycling artery, the reduced delay of a cross street at a double-cycling intersection, and the mutual optimization of the artery and double-cycling intersection.
- traffic control /
- double-cycling arterial coordination /
- speed guidance /
- multi-objective optimization /
- genetic algorithm

HTML全文

图 1 干线上下行协调车流时距图

Figure 1. Time interval for both directions

下载: 全尺寸图片幻灯片

图 2 广安门内大街某路段示意图

Figure 2. Section of the Guanganmen Inner Street

下载: 全尺寸图片幻灯片

图 3 模型优化与仿真流程图

Figure 3. Flow of speed guidance

下载: 全尺寸图片幻灯片

表 1 交叉口现状信号配时方案及平峰流量数据

Table 1. Original signal timing schemes and off-peak time volume of intersections

交叉口	相位	绿灯时间/s	黄灯时间/s	全红时间/s	周期/s	流量/pcu
交叉口	相位	绿灯时间/s	黄灯时间/s	全红时间/s	周期/s	东（南）进口	西（北）进口
1牛街	东西直行	89	4	2	218	814	686
	东西左转	31	4	2		87	90
	南北直行	43	4	2		445	432
	南北左转	31	4	2		156	103
2教子胡同	东西直行	100	4	2	218	816	766
	东西左转	37	4	2		203	112
	南北直左	63	4	2		340	308
3菜市口	东西直行	85	4	2	218	712	807
	东西左转	33	4	2		235	124
	南北直行	45	4	2		479	545
	南北左转	31	4	2		231	114

下载: 导出CSV

表 2 信号配时参数取值范围

Table 2. Range of signal-timing parameters

参数	取值范围
周期/s	120≤C≤190	60≤C_m≤95
绿灯时间/s	40≤g₁₁(g₃₁)≤70	20≤g₂₁≤36 11≤g₂₂≤20 20≤g₂₃≤30
	20≤g₁₂(g₃₂)≤32
	28≤g₁₃(g₃₃)≤45
	20≤g₁₄(g₃₄)≤31
相位差/s	0≤Offset₁₂≤C_m	0≤Offset₂₃≤C

下载: 导出CSV

表 3 各交叉口优化后的配时方案

Table 3. Optimized signal timing schemes for each intersection

交叉口	相位	无车速引导的双周期干线多目标优化模型结果		车速引导下的双周期干线多目标优化模型结果
交叉口	相位	绿灯时间/s	相位差/s	绿灯时间/s	相位差/s
1牛街	东西直行	74	Offset₁₂=31 Offset₂₃=63	49	Offset₁₂=29 Offset₂₃=64
	东西左转	25		28
	南北直行	38		38
	南北左转	29		27
2教子胡同	东西直行	33		24
	东西左转	18		18
	南北直左	26		23
	东西直行	73		48
3菜市口	东西左转	31		29
	南北直行	35		38
	南北左转	27		27

下载: 导出CSV

表 4 各方案仿真结果

Table 4. Simulation results of each scheme

方案	干线车均延误/（s/pcu）	干线通行能力/（pcu/h）	干线平均停车次数/（次/pcu）	双周期交叉口次路方向车均延误/（s/pcu）
原配时方案	277.13	1 582.00	6.49	61.49
无车速引导下双周期多目标优化	242.86	1 542.00	7.60	43.13
本文优化方案	222.71	1 671.00	5.76	37.06

下载: 导出CSV

表 5 优化结果对比

Table 5. Comparison of the optimization results

结果对比	变化率/%
结果对比	干线车均延误	干线通行能力	干线平均停车次数	双周期交叉口次路方向车均延误
本文相对于原方案	-19.6	5.6	-11.2	-33.9
本文相对于无车速引导方案	-8.3	8.4	-24.2	-5.8
无车速引导方案相对于原方案	-12.4	-2.5	17.1	-29.9

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