ADAPTIVE FUZZY LOGIC TRAFFIC CONTROL SYSTEM FOR OPTIMIZED FLOW AND EMERGENCY VEHICLE PRIORITY

Abstract

This paper presents an intelligent fuzzy logic–based traffic signal control system designed for isolated intersections to enhance urban traffic flow efficiency. The proposed controller dynamically adjusts green light durations based on real-time parameters such as vehicle queue length and average waiting time, thereby minimizing congestion and delay at intersections. In addition to adaptive timing, the system incorporates an emergency vehicle priority mechanism using a siren detection sensor, which ensures immediate green-light allocation and uninterrupted passage for emergency vehicles such as ambulances, police cars, and fire trucks. The entire system is modeled and simulated in MATLAB/Simulink using Mamdani-type fuzzy inference, where multiple rule-based decisions govern signal transitions. Comparative performance analysis between the proposed fuzzy controller and a conventional fixed-time traffic controller demonstrates significant improvements in average waiting time, queue length, and overall delay. The results indicate that the fuzzy logic–based approach provides a more efficient, responsive, and intelligent solution for modern traffic management systems.