The integration of multi-agent systems (MAS) into traffic control and optimization is a promising research direction that addresses the complexities of modern road networks. Thispaper investigates the application of MAS in enhancing traffic management through decentralized decision-making and adaptive strategies. We explore how MAS can be utilized to regulate traffic flow, minimize congestion, and optimize the use of transportation infrastructure. The study involves the development of a novel MAS framework that consists of multiple agents, each responsible for a segment of the road network. These agents communicate and collaborate with each other to share information about traffic conditions and to make real-time adjustments to traffic signals and road usage policies. The simulation results demonstrate the effectiveness of the proposed system in reducing travel time, decreasing emissions, and improving overall traffic efficiency. Moreover, the adaptability of MAS allows for dynamic adjustments in response to changing traffic patterns and unforeseen disruptions. The findings indicate that MAS can serve as a robust tool for traffic control and optimization, offering a feasible solution to the ever-increasing challenge of managing urban mobility.
Martin, E. Multi-Agent Systems for Traffic Control and Optimization. Transactions on Applied Soft Computing, 2023, 5, 37. https://doi.org/10.69610/j.tasc.20230316
AMA Style
Martin E. Multi-Agent Systems for Traffic Control and Optimization. Transactions on Applied Soft Computing; 2023, 5(1):37. https://doi.org/10.69610/j.tasc.20230316
Chicago/Turabian Style
Martin, Emma 2023. "Multi-Agent Systems for Traffic Control and Optimization" Transactions on Applied Soft Computing 5, no.1:37. https://doi.org/10.69610/j.tasc.20230316
APA style
Martin, E. (2023). Multi-Agent Systems for Traffic Control and Optimization. Transactions on Applied Soft Computing, 5(1), 37. https://doi.org/10.69610/j.tasc.20230316
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