The ongoing increase in urbanization and traffic congestion creates an urgent need to operate our transportation systems with maximum efficiency. Traffic signal control optimization is considered one of the main ways to solve traffic problems in urban networks. In publications in the field of intelligent transportation systems, a vast amount of different optimal traffic
light control methods is described. With new optimization methods being developed, it is important to know their performance compared to similar methods. Such a comparison is only possible if the same performance evaluation methodology is applied to all these methods. Most of the studies in the field of intelligent transportation system consider a self-defined evaluation methodology. A general evaluation methodology is developed to objectively evaluate the performance of these optimization methods. The developed general evaluation methodology is used to evaluate the performance of a dynamic programming and Q-learning method.