INTEGRATING FUZZY LOGIC AND ARDUINO MEGA FOR TRAFFIC SIGNAL OPTIMIZATION
Abstract
Modern traffic management systems are becoming increasingly critical as urbanization and vehicle numbers surge. Conventional traffic signal control relies on fixed time intervals, resulting in inefficiencies during varying road conditions, such as rush hours. To address this limitation, fuzzy logic traffic light control presents a promising alternative, offering a more flexible and adaptive approach. Fuzzy logic, an extension of Boolean logic, allows for a range of truth values, introducing shades of ambiguity that better capture real-world complexities.
In this study, we propose a traffic light control system based on fuzzy logic and vehicle density detection using the HC-SR04 sensor. The system aims to dynamically adjust traffic light durations in real-time, providing a smoother and more efficient traffic flow at intersections. By replacing proximity sensors with car-counting sensors, the controller gains a deeper understanding of traffic density on lanes, enabling a more accurate assessment of changing traffic patterns.
The Mamdani method, a well-established fuzzy logic technique, is employed to process linguistic rules and make informed decisions on traffic light adjustments. This intelligent approach is particularly crucial in densely populated areas like Indonesia, where the number of vehicles is rapidly increasing, exacerbating traffic congestion on limited road infrastructure.
The proposed traffic light control system has the potential to mitigate traffic jams and congestion by intelligently adapting to fluctuating traffic conditions. As a result, it can lead to improved traffic management, reduced travel times, and enhanced overall safety on the roads.
