Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing

被引：35

作者：

Zhang, Haibo ^{[1
]}

Liu, Xiaoming ^{[1
]}

Ji, Honghai ^{[1
]}

Hou, Zhongsheng ^{[2
]}

Fan, Lingling ^{[3
]}

机构：

[1] North China Univ Technol, Sch Elect & Control Engn, Beijing 100144, Peoples R China

[2] Qingdao Univ, Sch Automat, Qingdao 266071, Shandong, Peoples R China

[3] Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100192, Peoples R China

来源：

ENERGIES | 2019年 / 12卷 / 07期

基金：

中国国家自然科学基金;

关键词：

(DITS)-I-2; data-driven control; multi-agent systems; adaptive cooperative control; queuing strength balance; urban traffic signal timing; CONTROL DESIGN; REAL-TIME; NETWORKS; SYSTEMS;

D O I：

10.3390/en12071402

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Data-driven intelligent transportation systems (D(2)ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

引用

页数：19

共 30 条

[1] Reinforcement learning for True Adaptive traffic signal control [J].

Abdulhai, B ;

Pringle, R ;

Karakoulas, GJ .

JOURNAL OF TRANSPORTATION ENGINEERING, 2003, 129 (03) :278-285

[2] Multi-Agent System in Urban Traffic Signal Control [J].

Balaji, P. G. ;

Srinivasan, D. .

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2010, 5 (04) :43-51

[3]

Cheng Z., 2015, P AS CONTR C ASCC, P1

[4] Cooperative, hybrid agent architecture for real-time traffic signal control [J].

Choy, MC ;

Srinivasan, D ;

Cheu, RL .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2003, 33 (05) :597-607

[5] Neural networks for continuous online learning and control [J].

Choy, Min Chee ;

Srinivasan, Dipti ;

Cheu, Ruey Long .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (06) :1511-1531

[6] New geometric inference techniques for type-2 fuzzy sets [J].

Coupland, Simon ;

John, Robert I. .

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2008, 49 (01) :198-211

[7]

Gokulan B.P., 2009, P IEEE INT C FUZZ SY, P1624

[8]

He ZH, 2013, P AMER CONTR CONF, P842

[9] From experiment design to closed-loop control [J].

Hjalmarsson, H .

AUTOMATICA, 2005, 41 (03) :393-438

[10]

Hou Z., 2014, Model Free Adaptive Control: Theory and Applications

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