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

Haibo Zhang, Xiaoming Liu, Honghai Ji, Zhongsheng Hou and Lingling Fan
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Haibo Zhang: School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China
Xiaoming Liu: School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China
Honghai Ji: School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China
Zhongsheng Hou: School of Automation, Qingdao University, Qingdao 266071, China
Lingling Fan: School of Automation, Beijing Information Science & Technology University, Beijing 100192, China

Energies, 2019, vol. 12, issue 7, 1-19

Abstract: 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.

Keywords: D 2 ITS; data-driven control; multi-agent systems; adaptive cooperative control; queuing strength balance; urban traffic signal timing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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