## 4 Control Mechanism Transformation in New Heterogeneous Traffic Flow Environments on Urban Roads
### New Traffic Control Methods in Intelligent Connected Environments  
With the development of intelligent transportation systems, research on new generation traffic control system technologies based on multi-source heterogeneous data has gradually unfolded [32]. [PERSON] et al. reviewed urban road traffic signal adaptive control methods, systematically analyzing the technical characteristics and application status of three categories of methods: model-based traffic control, intelligent computing-based traffic control, and data-driven traffic control [30]. Vehicle-infrastructure coordinated traffic control technology provides new approaches for addressing the limitations of traditional control methods. [PERSON] et al. proposed an optimization method integrating traffic signals and vehicle trajectories, significantly improving intersection throughput efficiency through joint optimization of intersection signal timing and vehicle trajectories [36]. In terms of technical applications, [PERSON] et al. proposed an adaptive signal control system based on vehicle-infrastructure coordination, capable of real-time estimation of intersection turning flows and queue lengths, providing precise data support for traffic signal optimization [37]. [PERSON] et al. proposed an autonomous intersection management method based on ant colony systems, achieving coordinated vehicle passage through distributed decision-making [38]. Priority control technology for special traffic demands has been thoroughly researched. [PERSON] proposed an urban transit signal priority control method based on the Sydney Coordinated Adaptive Traffic System, designing control strategies accommodating bidirectional transit signal priority requests [39]. [PERSON] et al. proposed an actuator-based adaptive transit signal priority control system capable of dynamically adjusting priority strategies based on bus operational status and link traffic conditions [40]. With the development of intelligent connected technologies, traffic control methods are undergoing revolutionary changes. From a control mode perspective, traffic control will no longer be limited to traditional signal control but will combine various approaches such as vehicle trajectory guidance and overall traffic flow organization to form diversified control modes. From a control architecture perspective, traffic control system architectures are evolving from traditional centralized approaches toward distributed and edge-oriented directions, with edge computing technologies substantially enhancing system real-time performance and resilience, realizing multi-level control architectures with \"cloud-edge-terminal\" collaboration. From a control objective perspective, future traffic control will increasingly emphasize balancing multiple objectives such as efficiency, safety, environmental protection, and fairness. From a control method perspective, artificial intelligence-based traffic control methods will find wider application, with advanced algorithms such as deep reinforcement learning and multi-agent systems playing important roles in complex traffic environments. Overall, traffic control mechanisms under new heterogeneous traffic environments are undergoing fundamental transformations, with traffic control systems gradually progressing from \"inaccuracies in measurement, computation, evaluation, and control operations\" toward precision, intelligence, and collaboration, providing important support for constructing efficient, safe, and green urban transportation systems. The transformation of traffic control mechanisms in new heterogeneous traffic environments represents a paradigm shift from reactive to proactive control paradigms. As elucidated in this section, the evolution from fixed-time isolated control to network-wide adaptive systems has been accelerated by advances in sensing, communication, and computational technologies.