FUZZY LOGIC-BASED TRAFFIC FLOW OPTIMIZATION FOR CONGESTION REDUCTION: A REVIEW

Abstract

Traffic congestion is a critical issue in urban transportation systems, leading to increased travel delays, fuel consumption, and environmental pollution. Traditional traffic management strategies, such as fixed-time signal control and sensor-based actuation, often fail to adapt to dynamic and unpredictable traffic conditions. Fuzzy logic provides an intelligent and flexible approach to traffic flow optimization by incorporating human-like reasoning to process uncertain and imprecise data. Through fuzzy inference systems (FIS), traffic signals can be adjusted dynamically based on real-time parameters such as vehicle density, queue length, and average speed, resulting in improved traffic flow and reduced congestion.