<![CDATA[Traffic congestion in urban areas has become an increasingly complex problem due to the rapid growth in the number of vehicles and the limitations of fixed-time traffic signal control systems. Conventional approaches are unable to respond dynamically to fluctuations in traffic density, often resulting in high waiting times and reduced intersection capacity. This study aims to design and evaluate an adaptive traffic signal control system based on Mamdani fuzzy logic to improve intersection control performance. The developed system uses two input variables, namely the number of vehicles on the main approach and the number of vehicles on the competing approach, and one output variable representing the green signal duration. Membership functions are modeled using triangular and trapezoidal shapes, while the rule base is structured in the form of a Fuzzy Associative Memory (FAM). The inference process is performed using the Mamdani method, and the crisp output value is obtained through centroid defuzzification. Performance evaluation is conducted under five traffic density scenarios representing low to highly congested conditions by comparing the fuzzy-based system with a fixed-time control system. The performance indicators used include average vehicle waiting time, queue length, and intersection throughput. The experimental results show that the fuzzy-based system is able to reduce average waiting time by 18–25% and increase throughput by 15–20%, particularly under moderate to congested traffic conditions. These findings demonstrate that Mamdani fuzzy logic can produce more adaptive, responsive, and efficient signal control compared to conventional methods, indicating its strong potential as an effective solution for the development of intelligent transportation systems in urban environments.]]>
