Abstract

Fuzzy logic deals with uncertainty, scalability, data integration, and inaccuracy that offers an appealing solution to Intelligent Transportation Systems (ITS), especially in traffic management in urban cities. This paper conducts a comparative study of five different fuzzy logic techniques, like Mamdani, Sugeno, Type-2, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Genetic Fuzzy Systems (GFS), and evaluates their performance in a SUMO-MATLAB simulation framework. The results demonstrate that GFS has the shortest average wait time (29.90 seconds) and computational delay (0.08 milliseconds). Type 2 Fuzzy Systems, on the other hand, are better at dealing with sensor noise. Research has determined that a concentration on hybrid fuzzy approaches improves urban transportation.