<![CDATA[Congestion at signalized intersections is often triggered by the use of fixed-time controllers that are unresponsive to the dynamics of traffic volume, queue length, and vehicle heterogeneity. This study aims to design an adaptive traffic light control system based on a Mamdani-type Fuzzy Logic Controller (FLC) by integrating three input variables: the number of vehicles, queue length, and vehicle type/size. The novelty of this research lies in the use of the vehicle type variable as an explicit input within the FLC model. The system was designed by determining the universe of discourse, membership functions, and a base of 27 IF-THEN rules, which were then implemented in Python using the scikit-fuzzy library through MIN-MAX inference mechanisms and Centroid of Area (COA) defuzzification. Testing was conducted on 27 input combinations representing traffic conditions ranging from low to heavy. The simulation results show that the generated green light duration ranges from 5.09 to 91.60 seconds, with a tendency to increase alongside the rise in the number of vehicles, queue length, and vehicle size. Thus, the proposed model is capable of generating green light duration decisions that are more responsive and representative of mixed traffic conditions.]]>
Copyrights © 2026
