Fuzzy-Based Traffic Light Control Strategy: The Good, the Bad, and the Ugly

Conventional traffic light control strategies often struggle to adapt to fluctuations in traffic volume because their periods are governed by static rules, which leads to inefficiencies and unnecessary delays. Incorporating fuzzy logic into traffic light control allows dynamic adjustment of traffic light timing durations by considering various traffic parameters collected by Intelligent Transport Systems (ITS). The research objective of this work is to investigate multiple traffic light control strategies by evaluating their performances in terms of average waiting time and queue lengths and, thus, to identify better strategies and their limitations on traffic light management at the transportation network level. In this work, the SUMO traffic simulator simulates traffic at an intersection to evaluate different traffic light control strategies. Simulation results show that with proper configurations, a fuzzy-based traffic light control strategy can significantly reduce the waiting time and queue length at an intersection compared to its conventional static ruled-based counterpart. The outcomes of this research provide insights into how to coordinate traffic at intersections and the pros and cons of different control strategies, paving the way for developing better traffic management systems with the help of real-time data collected from smart sensors and monitoring systems.