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Recent years have witnessed substantial growth in adaptive traffic signalcontrol (ATSC) methodologies that improve transportation network efficiency,especially in branches leveraging artificial intelligence based optimizationand control algorithms such as reinforcement learning as well as conventionalmodel predictive control. However, lack of cross-domain analysis and comparisonof the effectiveness of applied methods in ATSC research limits ourunderstanding of existing challenges and research directions. This chapterproposes a novel unified view of modern ATSCs to identify common ground as wellas differences and shortcomings of existing methodologies with the ultimategoal to facilitate cross-fertilization and advance the state-of-the-art. Theunified view applies the mathematical language of the Markov decision process,describes the process of controller design from both the world (problem) andsolution modeling perspectives. The unified view also analyses systematicissues commonly ignored in existing studies and suggests future potentialdirections to resolve these issues.

A Critical Review of Traffic Signal Control and A Novel Unified View of  Reinforcement Learning and Model Predictive Control Approaches for Adaptive  Traffic Signal Control