  Recent years have witnessed substantial growth in adaptive traffic signal
control (ATSC) methodologies that improve transportation network efficiency,
especially in branches leveraging artificial intelligence based optimization
and control algorithms such as reinforcement learning as well as conventional
model predictive control. However, lack of cross-domain analysis and comparison
of the effectiveness of applied methods in ATSC research limits our
understanding of existing challenges and research directions. This chapter
proposes a novel unified view of modern ATSCs to identify common ground as well
as differences and shortcomings of existing methodologies with the ultimate
goal to facilitate cross-fertilization and advance the state-of-the-art. The
unified view applies the mathematical language of the Markov decision process,
describes the process of controller design from both the world (problem) and
solution modeling perspectives. The unified view also analyses systematic
issues commonly ignored in existing studies and suggests future potential
directions to resolve these issues.
