<![CDATA[Fundamental changes in urban mobility patterns have led to an unpredictable and non-stationary surge in vehicle volume, driven by Work From Anywhere policies and a significant increase in private vehicle usage and ride-hailing services. Consequently, a new paradigm integrating artificial intelligence with advanced computing infrastructure is required. This study constitutes a literature review aimed at providing a comprehensive critical analysis of Deep Reinforcement Learning models and Edge Computing schemes within the context of Adaptive Traffic Signal Control, with particular focus on implementation challenges in the new normal mobility era. The findings reveal four primary insights. First, Multi-Agent Deep Reinforcement Learning architectures incorporating communication mechanisms based on Graph Neural Networks demonstrate superior performance in multi-intersection scenarios, yet remain vulnerable to distributional shift phenomena caused by non-stationary travel pattern changes. Second, Edge Computing theoretically reduces latency and enhances system resilience to network failures, although its deployment is constrained by computational resource limitations and energy consumption issues on edge devices operating in extreme intersection environments. Third, an overreliance on simulation data from SUMO or VISSIM introduces significant validity gaps when models are applied to real-world mobility dynamics influenced by heterogeneous data sources such as probe vehicles and loop detector sensors. Fourth, implementation barriers are multidimensional, encompassing computational complexity, susceptibility to adversarial attacks on DRL policies, and regulatory and interoperability gaps with legacy infrastructure. The practical implications of this research emphasize the development of compact DRL models leveraging knowledge distillation for low-power edge devices, alongside technical interoperability guidelines to facilitate gradual transition from conventional systems.]]>
