An Intelligent Urban Traffic Management framework based on Fuzzy Theory

Urban traffic congestion has become a major issue as a result of growing urbanization, diverse traffic composition, and unpredictable driving behavior.Conventional traffic signal control systems, such as fixed-time and actuated controllers, rely on exact sensor inputs and are inflexible in the face of changing traffic conditions.This research offers a fuzzy logic-based Intelligent Traffic Management System (ITMS) that can handle imprecision by using linguistic factors and human-like reasoning.A Mamdani-type Fuzzy Inference System (FIS) may be designed using traffic density, queue length, and waiting time as input variables and adaptive green-time extension and phase priority as outputs.The proposed system may be constructed and assessed using SUMO (Simulation of Urban Mobility).Performance is compared against fixed-time and actuated signal controllers based on criteria including average delay, queue length, throughput, and anticipated environmental impact.Simulation results reveal a reduction in average waiting time of up to 35% and a 20-40% decrease in queue length, especially under peak traffic circumstances.The findings demonstrate that fuzzy logic-based control is a reliable, scalable, and practical option for real-time urban traffic management, particularly in the mixed-traffic settings found in developing cities.