Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.

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This article addresses the optimal contraflow scheduling problem that has arisen in the contraflow operation that has been implemented successfully in practice. The problem is formulated as a bilevel programming model in which the upper level problem is a binary integer programming formulation that aims to minimize the total travel time of a study area, while the lower level problem is a microscopic traffic simulation model that can simulate the dynamic reaction of drivers resulting from a contraflow scheduling scheme. As a consequence, such an adoption results in inexistence of analytical expression of the objective function in the upper level problem. Accordingly, conventional analytical solution methods for solving integer programming problems are no longer available for the proposed bilevel programing model. Therefore, this article develops a variation of the genetic algorithm that embeds with the microscopic traffic simulation module as well as a string repairing procedure to find the optimal contraflow scheduling solution. A case study in Singapore is carried out to evaluate the proposed methodology, in which PARAMICS as the microscopic traffic simulation model is applied.