Intersections are critical points in a road network, facilitating the convergence of two or more road alignments and traffic flow at those points. One of the most effective methods to manage traffic flow, especially at intersections in high-volume areas, is with traffic signal control systems designed to regulate the timing and sequencing of the signals at an intersection. The main objective of this research chapter was to develop a better way to compare the performance of different traffic signal control systems. First, a real-world intersection was used as the test case. The intersection was then modelled using the Simulation of Urban Mobility (SUMO) modelling environment. Furthermore, the tool was used to simulate the operations of different traffic signal systems at the intersection. Vehicle counts were generated in the simulated environment based on the observed counts. Four traffic signals were used for analysis: actuated, delay-based, static, and a Reinforcement Learning (RL) system. A Multi-Criteria Decision Analysis (MCDA) was used to assess the performance of the various systems. After various tests, the ranking of the TSC systems was delay-based, actuated, RL, and Static.