MULTI-AGENT TRAFFIC MANAGEMENT SYSTEM

Abstract

Urban traffic congestion has become a major challenge due to the rapid increase in the number of vehicles and inefficient traffic signal management. Traditional traffic control systems often operate on fixed-time signals, which are unable to adapt to realtime traffic conditions, resulting in delays, fuel wastage, and increased pollution. To address this issue, the proposed Multi-Agent Traffic Management System (MATMS) utilizes intelligent agents and real-time data analysis to improve traffic flow and optimize signal coordination at road intersections. The system employs multiple autonomous agents that represent different traffic components such as intersections, vehicles, and monitoring units. These agents communicate with each other and make decentralized decisions based on real-time traffic density, vehicle movement, and road conditions. Using traffic simulation and visualization techniques, the system monitors vehicle congestion at different junctions and dynamically adjusts traffic signals to minimize waiting time and prevent bottlenecks. A web-based interface is developed to visualize the live traffic scenario on a map, where traffic data from different regions is displayed along with agent decisions. The system also integrates surveillance-based vehicle detection and emergency handling mechanisms to prioritize emergency vehicles and manage road incidents effectively. The proposed system demonstrates how intelligent multi-agent coordination can significantly improve urban traffic management by reducing congestion, improving travel time, and enhancing road safety. This approach provides a scalable and adaptive solution for smart city transportation systems.