## 4 Control Mechanism Transformation in New Heterogeneous Traffic Flow Environments on Urban Roads
### Section Summary  
While research demonstrates significant theoretical benefits across all three control scenarios (isolated, arterial, network), substantial gaps remain between laboratory results and real-world deployment. Technology maturity variations across different control mechanisms create uneven deployment landscapes, with some technologies requiring years of additional development before practical implementation. Economic viability remains questionable for many advanced control systems, particularly when considering total cost of ownership including maintenance, upgrades, and personnel training. Regulatory and institutional barriers often impede deployment of innovative control mechanisms, as transportation authorities require extensive validation and may resist departures from established practices. User acceptance challenges may limit effectiveness even when technical performance is satisfactory, particularly for control strategies that alter familiar traffic patterns or require behavioral adaptations from drivers. In summary, the new heterogeneous traffic control mechanisms for urban roads in intelligent connected environments exhibit comprehensive characteristics of multiple levels, multiple methods, and multiple objectives. From the perspective of single intersections, methods such as safety control, adaptive signal optimization, schedule-driven collaborative control, and flexible facility reservation control each have their own characteristics and can effectively improve traffic efficiency and safety under different CAV penetration rates. From the arterial level, hierarchical control architectures, spatiotemporal collaborative optimization, and trajectory optimization considering queuing effects solve the complex problems of multi-intersection coordination. From the network level, innovative methods such as multi-objective network optimization, multi-agent collaborative frameworks, heterogeneous network control, and knowledge sharing and communication mechanisms effectively address the challenges of large-scale traffic control. These studies collectively indicate that with the continuous development of intelligent connected technologies, traffic control is evolving from traditional passive response to active prediction, from single-pointoptimization to network coordination, and from single-objective to multi-objective balance. Table 2 systematically summarizes typical research methods, main innovations, key technologies, and performance improvements of traffic control in different scenarios under intelligent connected environments, comprehensively showcasing the research progress and technical systems in the field of new heterogeneous traffic control. The future development of traffic control in intelligent connected environments should focus on the following aspects: first, exploring progressive control strategies under different intelligent connected vehicle penetration rates; second, developing more efficient and real-time algorithms to meet practical application requirements; third, research  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Control Scenario** & **Method Category** & **Main Features/Innovations** & **Key Technologies** & **Performance Improvements** & **References** \\ \hline \multirow{9}{*}{Isolated Interaction Scenario} & \multirow{4}{*}{Safety-Critical Control} & \multirow{4}{*}{The Enables connected automated vehicles to maintain safe distances with both preceding and following vehicles, provides formal safety guarantees} & \multirow{4}{*}{Ensures collision-free behavior under various traffic conditions; minimizes deviation from nominal stabilizing controllers} & \multirow{4}{*}{[PERSON] et al.