## 4 Control Mechanism Transformation in New Heterogeneous Traffic Flow Environments on Urban Roads
### Evolution and Theoretical Foundations of Traffic Control Mechanisms  
Traffic control systems have evolved from isolated fixed-time control to networked coordinated control and then to adaptive control. [PERSON] et al. systematically reviewed the four-generation development process of road traffic control systems: first-generation systems primarily employed fixed-time control, second-generation systems began introducing actuated control, third-generation systems implemented adaptive control through computer networking, while fourth-generation systems are characterized by multi-source data-driven approaches and connected vehicle participation [24]. Figure 1 illustrates this evolutionary progression, highlighting how each generation of traffic control has been enabled by specific technological breakthroughs, from mechanical timing devices in early systems to current 5G and edge computing technologies that enable vehicle-infrastructure cooperation.  
The limited information volume of fixed-location traffic data collection methods constrains traditional adaptive control systems (such as SCOOT, SCATS, etc.), with unguaranteed precision and inability to obtain key information such as turning flows, link generation, and attraction volumes. Consequently, predicted traffic flow demands significantly differ from actual conditions, preventing traffic control from truly efficient real-time responses to traffic flow changes [25; 26]. [PERSON] et al. analyzed the application of Shanghai's \"Intelligent Traffic Lights\" and the Sydney Coordinated Adaptive Traffic System (SCATS), noting that Shanghai's \"Intelligent Traffic Lights\" offered higher real-time performance and stronger emergency response capabilities, yet still had significant room for improvement [27].  
With the rapid development of artificial intelligence, Internet of Things, big data, and other technologies, traffic control systems are undergoing revolutionary changes. [PERSON] et al. proposed the concept of traffic cybernetics, systematically explaining the common principles and methods of traffic system control, arguing that information equals control, and proposing traffic cybernetics thinking that takes enhancing the system organization level as the fundamental goal, traffic entropy as the basic criterion, and information as the control means [28].  
In terms of theoretical methods, [PERSON] et al. systematically analyzed the application of deep reinforcement learning in traffic signal control, discussing its advantages and challenges in complex traffic systems [23]. With the rapid development of vehicle networking technology and artificial intelligence algorithms, traditional model-based traffic control methods are gradually transitioning to data-driven intelligent control methods. The intelligent traffic signal control method based on deep reinforcement learning proposed by [PERSON] et al. achieved real-time optimization of signal timing by integrating multiple traffic data sources, significantly improving intersection efficiency [29].  
The application of edge computing and distributed control architectures provides new approaches for addressing the real-time performance and reliability challenges faced by traditional centralized control systems. [PERSON] et al. developed a Fuzzy Intelligent Traffic Signal (FITS) control system that receives signal controller hardware messages through intermediate hardware devices and overrides traffic light indications during real-time operations, offering a convenient and economical method for improving existing traffic light infrastructure [30]. This distributed control architecture not only enhances system response speed but also strengthens system resilience against abnormal situations such as network failures.  
Figure 1: Evolution timeline of traffic control mechanisms and key technological enablers.