Multi‐Intersection Cooperative Control Platform Framework and Empirical Study in Connected and Automated Vehicle Environment: An Engineering Implementation Perspective

With the acceleration of urbanization and rapid growth of vehicle ownership, traffic congestion has become a critical bottleneck constraining sustainable urban development. Traditional traffic signal control methods struggle to adapt to the complex traffic environment where connected and automated vehicles (CAVs) coexist with conventional vehicles. This perspective review synthesizes current research advances with practical implementation insights to address these challenges, examining multi‐intersection cooperative control platform frameworks for CAV environments from both theoretical and engineering perspectives. Current platform developments typically adopt three‐layer architecture designs comprising a data access layer, a data middleware layer, and a data application layer, achieving decoupling between data management platforms and algorithm integration platforms, thereby enhancing system flexibility and scalability. In terms of data management, a graph‐structured data model is introduced to better represent road network topology, and a multilevel data fusion mechanism is established at the parameter, feature, and decision levels. Regarding functional implementation, three core functions are integrated: intelligent speed guidance, emergency vehicle signal priority, and blind spot safety warning. Through the collaborative operation of on‐board units, roadside units, and control centers, these platforms enable organic integration of “smart vehicles” and “intelligent roads.” The platform design follows the core philosophy of “perceiving congestion, identifying congestion, and alleviating congestion.” Case study results from implementations in urban areas, such as Guangzhou’s central district, demonstrate potential for traffic efficiency improvements, with reported delay reductions of approximately 10% at key intersections. In the intelligent connected transportation system with electronic toll collection test in Foshan, vehicle identification accuracy exceeded 99.9%, with all system functions and performance indicators meeting design requirements. These developments provide insights into engineering‐implementable solutions for traffic control in intelligent connected environments, offering theoretical perspectives and practical implications for advancing smart transportation development. Through this integrated perspective that combines structured narrative analysis with validated implementation frameworks, this work aims to bridge the persistent gap between theoretical innovation and engineering practice in CAV cooperative control systems.