基于MFD的城市区域过饱和交通信号优化控制

刘小明; 唐少虎; 朱凤华; 陈兆盟

doi:10.16383/j.aas.2017.c160250

基于MFD的城市区域过饱和交通信号优化控制

doi: 10.16383/j.aas.2017.c160250

刘小明^1,2,,
唐少虎^1,2,3, ,,
朱凤华^4,,
陈兆盟^1,2,

1.
城市道路交通智能控制技术北京市重点实验室北京 100144
2.
北方工业大学电气与控制工程学院北京 100144
3.
北京城市系统工程研究中心北京 100035
4.
中国科学院自动化研究所北京 100190

基金项目:

国家自然科学基金 61374191

国家科技支撑计划项目 2014BAG03B01

长城学者计划 CIT & TCD20150301

详细信息

作者简介:
刘小明北方工业大学电气与控制工程学院教授.2004年获得中国科学院自动化研究所控制理论与控制工程博士学位.主要研究方向为交通流理论和智能交通控制.E-mail:tslxm@sina.com

朱凤华中国科学院自动化研究所副研究员.2008年获得中国科学院自动化研究所博士学位.主要研究方向为平行交通系统, 交通信号控制, 社会交通.E-mail:fenghua.zhu@ia.ac.cn

陈兆盟北方工业大学电气与控制工程学院助理研究员.主要研究方向为交通信号控制, 智能交通信号控制器.E-mail:chenzhaomeng@126.com

通讯作者:
唐少虎北京城市系统工程研究中心助理研究员.2017年获得北方工业大学博士学位.主要研究方向为城市韧性, 交通控制, 智能算法.本文通信作者.E-mail:tshaohu@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2016-03-04
- 录用日期: 2016-08-31
- 刊出日期: 2017-07-20

Urban Area Oversaturated Traffic Signal Optimization Control Based on MFD

LIU Xiao-Ming^{1,2
,},
TANG Shao-Hu^{1,2,3
, ,},
ZHU Feng-Hua^4
,,
CHEN Zhao-Meng^{1,2
,}

1.
Beijing Key Laboratory of Urban Road Traffic Intelligent Technology, Beijing 100144
2.
College of Electrical and Control Engineering, North China University of Technology, Beijing 100144
3.
Beijing Research Center of Urban System Engineering, Beijing 100035
4.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds:

Supported by National Natural Science Foundation of China 61374191

National Science and Technology Support Program 2014BAG03B01

Great Wall Scholars Program CIT & TCD20150301

More Information

Author Bio:
Professor at the College of Electrical and Control Engineering, North China University of Technology. He received his Ph. D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences in 2004. His research interest covers traffic flow theory and intelligent traffic control

Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2008. His research interest covers parallel transportation system, traffic signal control, and social transportation

Assistant professor at the College of Electrical and Control Engineering, North China University of Technology. His research interest covers traffic signal control technology and intelligent traffic signal controllers

Corresponding author: TANG Shao-Hu Research associate at Beijing Research Center of Urban System Engineering. He received his Ph. D. degree from North China University of Technology in 2017. His research interest covers urban resilience, traffic control, and intelligent algorithm. Corresponding author of this paper.E-mail:tshaohu@163.com

摘要

摘要: 为了解决交通高峰时段城市区域路网过大的交通需求引起的路网通行效率下降以及区域内部交通流分布的异质性产生的道路资源浪费等问题.本文提出了基于区域路网固有属性宏观基本图（Macroscopic fundamental diagram，MFD）的过饱和区域控制优化模型，建立了边界控制信号和内部控制信号目标函数的双层规划优化，进一步设计了基于BP（Back propagation）神经网络的自适应动态规划（Adaptive dynamic programming，ADP）模型，对建立的双层规划区域交通信号进行求解，实例仿真结果验证了本文方法的有效性.通过本文的研究分析，对城市区域交通的需求管控、拥堵政策制定等城市区域交通管理具有一定的指导意义.
- 区域交通信号优化 /
- 宏观基本图 /
- 双层规划 /
- 自适应动态规划 /
- BP网络
Abstract: In order to solve traffic efficiency reduction of road network, which is caused by overlarge traffic demand of urban regions at peak hours, and resource waste of roads due to the heterogeneity of traffic distribution, this paper proposes an optimization model of control for oversaturated area based on inherent attributes macroscopic fundamental diagram (MFD) of regional road network, and builds up the bi-level programming optimization of objective function for boundary and internal signal control. Furthermore, an adaptive dynamic programming (ADP) model based on back propagation (BP) neural network is employed to solve the regional signal control of bi-level programming. Simulation results verify the validity of this method. The investigation of this paper has certain guidance for urban traffic management such as control and management of traffic demand, formulation of congestion policy, etc.
- Regional traffic signal optimization /
- macroscopic fundamental diagram (MFD) /
- bi-level programming /
- adaptive dynamic programming (ADP) /
- back propagation (BP) neural network
注释:

1) 本文责任编委董海荣

HTML全文

图 1 过饱和区域边界及内部交叉口示意图

Fig. 1 Boundary of oversaturated area and internal intersection diagram

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图 2 过饱和区域信号控制优化模型框架

Fig. 2 Frame of oversaturated area traffic signal optimization control model

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图 3 路网两种MFD关系模型

Fig. 3 Two MFD relational models of network

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图 4 基于ADHDP结构的自适应动态规划框架

Fig. 4 Adaptive dynamics programming frame based on ADHDP

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图 5 评价网络结构图

Fig. 5 Valuation network diagram

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图 6 执行网络结构图

Fig. 6 Executive network diagram

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图 7 山西临汾部分城区仿真路网

Fig. 7 Urban area simulation network in Linfen, Shanxi

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图 8 ADP模型训练值与标准值对比及误差

Fig. 8 Comparison and deviation between training value and standard value of ADP model

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图 9 路网车辆平均延误对比

Fig. 9 Comparison of network vehicle average delay

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图 10 路网车辆数对比

Fig. 10 Comparison of network vehicle number

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图 11 路网车辆占有率对比

Fig. 11 Comparison of network vehicle occupancy

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图 12 路网车辆平均停车次数对比

Fig. 12 Comparison of network vehicle average stops

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图 13 路径1和路径2平均延误对比

Fig. 13 Comparison of average delay between Route 1 and Route 2

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图 14 路径3和路径4平均延误对比

Fig. 14 Comparison of average delay between Route 3 and Route 4

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图 15 路径1和路径2平均停车次数对比

Fig. 15 Comparison of average stops between Route 1 and Route 2

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图 16 中路径3和路径4平均停车次数对比文标题

Fig. 16 Comparison of average stops between Route 3 and Route 4

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