考虑关键路径序列的干道绿波协调控制方法

王厚沂; 张存保; 曹雨; 陈峰; 曾荣

doi:10.3963/j.jssn.1674-4861.2022.06.007

考虑关键路径序列的干道绿波协调控制方法

doi: 10.3963/j.jssn.1674-4861.2022.06.007

武汉理工大学智能交通系统研究中心武汉 430063

基金项目:

四川省科技计划项目 2022YFG0048

详细信息

作者简介:
王厚沂(1998—), 硕士研究生.研究方向: 交通管理与控制.E-mail: 1361881700@qq.com

通讯作者:
张存保(1976—), 博士, 研究员.研究方向: 交通信息工程及控制、交通安全等.E-mail: zhangcunbao@163.com

中图分类号: U491.5+4
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出版历程
- 收稿日期: 2022-03-29
- 网络出版日期: 2023-03-27

A Coordinated Green-wave Control Method on Arterial Roads Considering Critical Path Sequence

Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

摘要

摘要: 传统的干道协调控制通常以协调流向的通行效率最大为优化目标, 然而在实际交通流量波动环境中, 某些非协调流向的流量在局部时段可能与协调流向相当甚至高于协调流向, 从而影响干道运行的总体效率。为了解决该问题, 研究了1种考虑关键路径序列的干道绿波协调控制方法。利用路径流量分担率和行程时间指数计算各车辆行驶路径的重要度, 并采用系统聚类算法识别干道上车辆行驶的关键路径。在此基础上构建了考虑关键路径序列的干道绿波协调控制模型: 考虑了各关键路径信号相位之间的协调关系, 设置了含0-1变量的信号相位矩阵, 并构建模型的基础约束条件; 设置了无效带宽存在性判断变量和最小重要度判断变量, 构建了考虑路径重要度的绿波带宽分配策略, 确保绿波带宽优先分配给重要度大的关键路径; 以关键路径序列加权绿波带宽总和最大为优化目标, 构建了模型的目标函数。利用VISSIM仿真软件搭建仿真环境, 以武汉市中山路4处交叉口组成的干道路段为例进行仿真验证。实验结果表明: 相比于传统的干道绿波协调控制方法和干道多路径绿波协调控制方法, 考虑关键路径序列的干道绿波协调控制方法使得干道平均延误分别减少了12.1%和4.8%, 平均排队长度分别减少了13.6%和7.6%, 平均停车次数分别下降了16.5%和9.7%;各关键路径的车辆平均行程时间与自身重要度大小严格成反比, 避免了绿波带宽的浪费。
- 交通控制 /
- 干道信号协调 /
- 关键路径 /
- 带宽分配策略 /
- 混合整数线性规划
Abstract: Traditional coordinated control method on arterial roads usually takes the maximum efficiency of the coordinated flows as the optimization objective. However, uncoordinated flows may be comparable to or even higher than the coordinated flows during certain periods, which can significantly deteriorate the overall efficiency of road operation in the actual fluctuated traffic flow environment on arterial roads. To solve this problem, a coordinated green-wave control method for arterial roads considering critical path sequence is proposed. The identification of the critical path sequence on the arterial road is calculated by the systematic clustering algorithm, and two indexes of traffic sharing rate of its path and travel time index are used as clustering parameters. On this basis, a coordinated green-wave control model for arterial roads considering critical path sequence is established. Firstly, the coordinated relationship among the signal phases of each critical path is considered, the signal phase matrix based on 0-1 variables is developed, and the constraints underlying the model are proposed. Secondly, the indicators for invalid bandwidth existence and the minimum importance are set, respectively, and a bandwidth allocation strategy for green wave considering the path importance is developed to ensure that the bandwidth of green wave is allocated to the critical path with a high importance in priority. Finally, the objective function of the model is established with the maximum weighted sum of green-wave-bandwidth of critical path sequences as the optimization objective. The simulation environment is developed using VISSIM simulation software where an arterial road section consisting of four intersections on Zhongshan Road in Wuhan City is used as a case study. The experimental results show that compared with the traditional coordination control methods for arterial green wave and arterial multi-path green wave, the proposed method results in a 12.1% and 4.8% reduction in the average arterial delay, 13.6% and 7.6% reduction in the average queue length, and 16.5% and 9.7% reduction in the average number of stops, respectively. Besides, the proposed method makes the average travel time of each critical path be strictly inverse proportional to its own importance, which avoids the waste of bandwidth of green wave.
- traffic control /
- arterial coordinated signal control /
- critical path /
- bandwidth allocation strategy /
- mixed integer linear programming

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图 1 多关键路径分布示例图

Figure 1. Sample graph of multi-critical path distribution

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图 2 多关键路径绿波协调优化控制方法流程

Figure 2. Process of multi-critical path green wave coordinated optimization control method

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图 3 绿波时距分析图

Figure 3. Green wave time-distance analysis figure

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图 4 考虑重要度的带宽分配流程

Figure 4. Process of bandwidth allocation strategy considering the importance

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图 5 目标干线渠化示意图

Figure 5. Canalization of target arterial road

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图 6 多关键路径绿波协调模型结果图

Figure 6. The results of the multi-critical path green wave coordination model

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图 7 不同模型评价指标对比图

Figure 7. Comparison of evaluation indices of different models

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图 8 车辆平均旅行时间对比分析图

Figure 8. Comparative analysis of the average vehicle travel time

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表 1 路径特征数据表

Table 1. Path characteristic data table

路径	流量/(veh/h)	I_Q/%	I_T	路径	流量/(veh/h)	I_Q/%	I_T
R₁：①-④	234	2.2	1.53	R₁₂：⑤-①	83	0.7	1.01
R₂：①-⑤	239	2.1	1.41	R₁₃：⑤-②	70	0.6	1.24
R₃：①-⑥	972	8.9	2.17	R₁₄：⑥-①	91	0.8	1.51
R₄：①-⑧	213	1.9	1.34	R₁₅：⑥-②	32	0.2	1.65
R₅：②-④	51	0.5	1.24	R₁₆：⑥-③	28	0.2	1.42
R₆：②-⑤	365	3.3	1.36	R₁₇：⑦-①	186	1.7	1.86
R₇：②-⑥	68	0.6	1.43	R₁₈：⑦-②	90	0.8	1.78
R₈：②-⑧	48	0.4	2.07	R₁₉：⑦-③	83	0.7	1.81
R₉：③-⑥	77	0.7	1.51	R₂₀：⑧-①	1 235	11.3	2.36
R₁₀：③-⑧	102	0.9	1.91	R₂₁：⑧-②	326	2.9	2.14
R₁₁：④-①	78	0.7	1.22	R₂₂：⑧-③	202	1.9	2.11

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参考文献(21)

[1]	查伟雄, 蔡其燕, 李剑, 等. 边路车辆出入条件下城市干线信号协调6574. ZHA W X, CAI Q Y, LI J, et al. Optimization of offset of urban arterial signal coordination undercondition of vehicle e-ntry and exit on side road[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 565-574. (in Chinese)
[2]	张靖思, 李振龙, 邢冠仰. 考虑动态车速的双周期干线信号协调控制多目标优化[J]. 交通信息与安全, 2021, 39(3): 60-67. doi: 10.3963/j.jssn.1674-4861.2021.03.008 ZHANG J S, LI Z L, XING G Y. Multi-Objective optimization for coordinated control of blecycling arterial signals considering dynamic vehicle speeds[J]. Journal of Transport Information and Safety, 2021, 39(3): 60-67. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.03.008
[3]	荆彬彬, 徐建闽, 鄢小文. 适于双周期的干道绿波信号协调控制模型[J]. 交通运输系统工程与信息, 2018, 18(1): 73-80. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201801012.htm JING B B, XU J M, YAN X W. Arterial signal coordination control model for double-cycle signal control[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(1): 73-80. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201801012.htm
[4]	MORGAN J T, LITTLE J D. Synchronizing traffic signals for maximal bandwidth[J]. Operations Research, 1964, 12(6), 896-912. doi: 10.1287/opre.12.6.896
[5]	LITTLE J D, MARK C, GARTNER N H, et al. MAXBAND: A versatile program for setting signals on arteries and triangular networks[R]. Cambridge, MA, USA: Massachusetts Institute of Technology, 1981.
[6]	GARTBER N H, ASSMANN S F, LASAGA F, et al. MULTIBAND-A variable-bandwidth arteries progression scheme[J]. Transportation Research Record, 1990(1287): 212-222.
[7]	STAMATIADIS C, GARTNER N H. MULTIBAND-96: A program for variable-bandwidth progression optimization of mult-iarterial traffic networks[J]. Transportation Research Record: Journal of the Transportation Research Board, 1996(1554): 9-17.
[8]	ZHANG C, XIE Y C, GARTNER N H, et al. AM-Band: An asymmetric multi-band model for arterial traffic signal coordination[J]. Transportation Research Part C: Emerging Technologies, 2015(58): 515-531.
[9]	卢凯, 郑淑鉴, 徐建闽, 等. 面向双向不同带宽需求的绿波协调控制优化模型[J]. 交通运输工程学报, 2011, 11(5): 101-108. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201105018.htm LU K, ZHENG S J, XU J M, et al. Green wave coordinated control optimizationmodels oriented to different bidirectional bandwidth demands[J]. Journal of Traffic and Transportation Engineering, 2011, 11(5): 101-108. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201105018.htm
[10]	ZHANG L, SONG Z, TANG X, et al. Signal coordination models for long arte-rialsand grid networks[J]. Transportation Research Part C: Emerging Technologies, 2016, 71(10): 215-230.
[11]	ARSAVA T, XIE Y, GARTNER N H, et al. Arterial traffic signal coordination utilizing vehicular traffic origin-destination information[C]. IEEE International Conference on Intelligent Transportation Systems, QingDao: IEEE, 2014.
[12]	YANG X F, CHENG Y, CHANG G L. A multi-path progression model for synchronization of arterial traffic signals[J]. Transportation Research Part C: Emerging Technologies, 2015(53): 93-111.
[13]	YANG X F, CHANG G L. Estimation of time-varying origin-destination patternsfor design of multipath progression on asignalized arterial[J]. Transportation Research Record Journal of the Transportation Research Board, 2017(2667): 28-38.
[14]	CHEN Y H, CHENG Y, CHANG G L. Concurrent progression of through andturning movements for arterials experiencing heavy turning flows and bay-length Constraints[J]. Transportation Research Record, 2019, 2673(9): 525-537.
[15]	CHEN C, CHE X, HUANG W, et al. A two-way progression model for arterial signal coordination considering side-street turning traffic[J]. Transportmetrica B, 2019, 7(1): 1627-1650.
[16]	CHEN Y H, CHENG Y, CHANG G L. Design of an arterial signal progression plan for multi-path flows with only intersection turning counts[J]. Transportation Research Part C: Emerging Technologies, 2021(130): 103322.
[17]	WANG Q, YUAN Y, YANG X T, et al. Adaptive and multi-path progression signal control under connected vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2021, 124(3): 102965.
[18]	贾洪飞, 郭明雪, 罗清玉, 等. GPS数据下的城市路网关键路段识别[J]. 吉林大学学报(工学版), 2020, 50(4): 1338-1343. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202004022.htm JIA H F, GUO M X, LUO Q Y, et al. Identifying critical links of urban road networks based on GPS data[J]. Journal of J-ilin University(Engineering and Technology Edition), 2020, 50(4): 1338-1343. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202004022.htm
[19]	阮树斌, 王福建, 马东方, 等. 基于车牌识别数据的机动车出行轨迹提取算法[J]. 浙江大学学报(工学版), 2018, 52(5): 836-844. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201805003.htm RUAN S B, WANG F J, MA D F, et al. Vehicle trajectory extraction algorithm based on license plate recognition data[J]. Journal of Zhejiang University(Engineering Science), 2018, 52(5): 836-844. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201805003.htm
[20]	李岩, 杨洁, 过秀成, 等. 基于小波变换的关联交叉口群关键路径识别方法[J]. 中国公路学报, 2012, 25(1): 135-140. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201201019.htm LI Y, YANG J, GUO X C, et al. Critical route identification method at related intersection group based on wavelet transform[J]. China Journal of Highway and Transport, 2012, 25(1): 135-140. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201201019.htm
[21]	马艳利. 混合整数非线性规划问题的分支定界算法研究[D]. 宁夏: 宁夏大学, 2014. MA Y L. Branch and bound algorithm of mixed integer nonlinear programming problem[D]. Ningxia: Ningxia University, 2014.