Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing
Data-driven intelligent transportation systems (D<sup>2</sup>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 changeab...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/7/1402 |
| Summary: | Data-driven intelligent transportation systems (D<sup>2</sup>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. |
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| ISSN: | 1996-1073 |