A Review of Max-Pressure Traffic Signal Control Algorithms: State of the Art and Research Opportunities

Abstract

Max-Pressure (MP) has emerged as a widely studied decentralized traffic signal control framework, thanks to its desirable theoretical properties and strong control performance. Early research on MP focused on improving control performance by exploring different variables of the traffic state for the definition of pressure. Subsequent studies expanded MP to incorporate the complexity of real-world transportation systems and emerging technologies. This paper provides a comprehensive review of these developments. The review first introduces the theoretical foundations of MP and examines early developments in movement weight definition. It then synthesizes major extensions for multimodal and multiscale network control, followed by recent developments in connected and automated vehicle (CAV) environments and the integration of MP with artificial intelligence (AI) and data-driven methods. Beyond summarizing existing approaches, this review examines their underlying assumptions, data requirements, implementation considerations, and practical limitations. Based on this comprehensive review, key research gaps and future directions are identified, including remaining limitations in the theoretical foundations of MP and the underexplored integration of autonomous vehicle control with the MP framework. Overall, this review provides a structured perspective on the evolution and current state of MP control and highlights opportunities for developing more proactive, robust, and practically deployable MP-based traffic control methods.