## 1 Introduction
### Research Background and Significance
However, vehicles must operate on roads according to traffic rules, and even the smartest intelligent connected vehicle cannot perceive all variables in the overall traffic environment relying solely on its own capabilities; cooperative coordination yields superior system-wide performance compared to isolated vehicle optimization. Traffic control is one of the most important technical means for regulating traffic flow, improving congestion, enhancing safety, and even reducing energy consumption and emissions. Recent empirical studies have provided compelling evidence demonstrating the substantial benefits of advanced traffic control systems in urban environments. [PERSON] et al. [1] conducted a comprehensive analysis of China's 100 most congested cities, revealing that big data empowered adaptive traffic signals achieved remarkable performance improvements: peak-hour trip times were reduced by 11% and off-peak times by 8%, resulting in an estimated annual CO\({}_{2}\) reduction of 31.73 million tons. Despite requiring an implementation investment of USD 1.48 billion annually, the societal benefits--encompassing CO\({}_{2}\) reduction, time savings, and fuel efficiency enhancements--were quantified at USD 31.82 billion, demonstrating a remarkable benefit-cost ratio of approximately 21:1. Furthermore, [PERSON] and [PERSON] [2] developed an optimization framework integrating macroscopic traffic models with emission estimation models for Xi'an city, which successfully reduced both vehicle travel times and emissions of four major pollutant categories through environment-friendly signal control strategies. These findings substantiate the transformative potential of intelligent traffic control systems in simultaneously addressing traffic efficiency, environmental sustainability, and economic viability challenges in urban transportation networks. Its progress and development have always kept pace with information and computer technology, as well as systems science. Consequently, traffic control theory and technology have evolved from offline to real-time online adaptive control systems. Due to the limitations of previous cross-sectional traffic detection technologies (loops, video, etc. ), traffic control still cannot intelligently prevent or address traffic congestion proactively. At the micro-level, it remains difficult to control traffic flow with high precision and optimality, such as providing dynamic feedback on the effectiveness of traffic coordination control, making it even more challenging to ensure the operational effectiveness of the entire road network. Road intersections frequently experience queue spillover, underutilization of green signal time, and frequent traffic safety hazards. Empirical evidence from arterial traffic data substantiates these operational challenges. [PERSON] et al. [3] demonstrated through high-resolution event-based data analysis that poor signal coordination and inappropriate signal timing significantly constrain intersection capacity, while the Queue-Over-Detector (QOD) phenomenon leads to misidentification of traffic states, masking actual congestion conditions. [PERSON] et al. [4] further validated that traditional queue estimation methods fail under congested conditions when detectors are persistently occupied, highlighting the inadequacy of current signal control approaches in managing oversaturated intersections. Given the importance of traffic control systems for improving and safeguarding urban traffic, academia and industry have almost continuously conducted research and development to enhance traffic control system functionality and performance. Through comprehensive review research on new generation urban traffic optimization control systems and key optimization controls, this paper aims to support the development of new generation traffic optimization control technologies and application products.