A Taxonomy of Signal Vehicle Coupled Control from a Mathematical Programming Perspective

Connected and Autonomous Vehicles (CAVs) have the potential to revolutionize traffic management and urban traffic control, offering benefits such as real-time data provision, active participation in traffic control operations, improvements in safety, energy efficiency, and emissions reduction. One key advancement enabling the integration of CAVs in traffic management is the simultaneous control of traffic signals and vehicles, known as Signal Vehicle Coupled Control (SVCC). SVCC comprises three sub-problems: traffic signal and phase optimization, CAV trajectory optimization, and CAV route scheduling and planning. While literature exists on SVCC and its components, no article has classified the literature from the perspective of mathematical programming. In this paper, we introduce a taxonomy that categorizes SVCC problems from the mentioned perspective, aiming to provide a structured framework for organizing research, identifying research gaps, and comparing methodologies. The taxonomy also aims to unify and consolidate current concepts and criteria in the literature as well as introduce new ones to characterize SVCC problems from an optimization point of view. SVCC literature from the last six years has been categorized using this taxonomy to illustrate its descriptive power. From this categorization, different remarks are made regarding the current gaps and trends in the addressed area.