<![CDATA[Urban areas face increasing challenges in managing transportation systems due to rising population densities and traffic congestion. Traditional traffic management methods often lack the flexibility and responsiveness needed to address dynamic conditions in real time. This study explores the utilization of multi-agent systems (MAS) as a solution for optimizing smart transportation systems within urban environments. The research aims to evaluate the effectiveness of MAS in improving traffic flow, reducing congestion, and enhancing system responsiveness through autonomous decision-making and coordination among multiple agents. A simulation-based methodology was employed to analyze MAS performance in managing various transportation variables, including traffic density, signal timing, and incident response. Each agent was programmed to perform specific tasks, such as monitoring traffic, optimizing traffic signals, and re-routing vehicles, with collaborative decision-making to address congestion in real time. Results indicate that MAS implementation led to a 30% improvement in traffic flow efficiency and a 25% reduction in congestion levels. The system also demonstrated adaptive capabilities, allowing for real-time adjustments to unexpected conditions, such as accidents or road closures. The findings suggest that multi-agent systems provide a viable, scalable solution for smart transportation management in complex urban settings. Implementing MAS can significantly enhance the efficiency and adaptability of urban transportation systems, contributing to more sustainable and efficient mobility solutions in rapidly growing cities.]]>
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