Abstract
Multi-Agent and Fuzzy Inference-Based Framework for Traffic Light Optimization
Highlights
THE optimization of signal light control in urban areas is at the forefront of research in the field of Artificial Transportation Systems (ATS)
That’s why agent-based systems are well suited for the traffic and transportation domain, since these systems are geographically distributed in a nonstationary environment [1]
We propose a distributed and adaptative, as well as online, optimized traffic signal control scheme enabled by a decentralized multi-agent system, where each group of agents represents a signalized intersection control unit, each group coordinates and collaborates with adjacent surrounding groups, and each group achieves local optimization, taking into consideration global network optimization
Summary
THE optimization of signal light control in urban areas is at the forefront of research in the field of Artificial Transportation Systems (ATS). Multiagent technology treats a complicated system in a distributed manner; it splits the complex control system into simple subtasks, allowing parallel and fast decision-making [2] With this being considered, the Multi-agent Cooperative Traffic Signal Optimization (MCTSO) is proposed to maximize the signalized intersection throughput and reduce congestion in urban arteries with three contributions: (1) the real-time optimization is introduced to adapt the system in a timely way to the continuously changing conditions and disturbances, supported by online monitoring of the optimum indicators to detect congestion and maintain the system not far off from the suitable operating point as much as possible. We propose a distributed and adaptative, as well as online, optimized traffic signal control scheme enabled by a decentralized multi-agent system, where each group of agents represents a signalized intersection control unit, each group coordinates and collaborates with adjacent surrounding groups, and each group achieves local optimization, taking into consideration global network optimization.
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