## 4 Control Mechanism Transformation in New Heterogeneous Traffic Flow Environments on Urban Roads
### Traffic Control Mechanisms for Network-Level Intersection Scenarios  
Network-level traffic control represents the highest tier of traffic control systems, scaling beyond the arterial coordination methods described in Section 5.2 to achieve optimization throughout entire road networks. While arterial control extends isolated intersection techniques to corridor-level coordination, network control must integrate both single-intersection safety mechanisms (Section 5.1) and multi-intersection hierarchical frameworks (Section 5.2) into city-wide optimization systems. The intelligent connected environment provides rich data support and collaborative mechanisms for network-level control, achieving theoretical and technological breakthroughs primarily in the following aspects. Multi-objective network optimization frameworks are key methods for addressing road network complexity. [PERSON] et al. proposed a multi-objective traffic signal control method based on network-wide agent coordinated reinforcement learning (NACRL), aiming to achieve a multi-objective balance among traffic safety, efficiency, and network coordination [54]. This method designed a reward mechanism that simultaneously examines traffic safety and efficiency indicators, adopting a centralized training-decentralized execution framework to solve the data transmission limitation problems in field implementation. More importantly, the algorithm utilizes attention mechanisms to dynamically capture spatiotemporal dependencies across complex arterial networks, providing support for multi-agent coordinated control. Experiments on large-scale synthetic traffic grids and real-world arterial networks demonstrate that this algorithm simultaneously improves performance in terms of traffic safety, efficiency, and network coordination, while enhancing algorithm convergence and interpretability. [PERSON] et al. developed a coordinated learning approach for network-level signal control, modeling each intersection as an intelligent agent that learns optimal timing decisions through interaction with the traffic environment. By using the JTA to obtain exact infer ence of the best joint actions for all coordinated intersections, VISSIM tests on a network containing 18 signalized intersections demonstrate that this algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed-time plans in terms of network-level average delay, number of stops, and vehicle emissions. This method effectively addresses the complexity and randomness problems of traffic signal control in large-scale road networks [55]. Multi-agent collaborative frameworks provide strong support for network-level control. [PERSON] et al. proposed a traffic signal control method based on multi-agent deep Q-learning algorithms, introducing innovative reward concepts in multi-agent environments, achieving 11% reduction in fuel consumption and 13% reduction in average travel time. This research demonstrates the potential of reinforcement learning in improving traffic signal controller coordination, providing support for building more sustainable and efficient transportation systems [56]. [PERSON] et al. applied deep reinforcement learning methods (Double Deep Q-Network) to train local agents, with each local agent learning independently to accommodate regional traffic flows and dynamics. After completing the learning process, a global agent is created to integrate and unify the action policies selected by each local agent, achieving traffic signal coordination. This method is validated through urban mobility simulation, significantly improving average vehicle waiting time and queue length compared to PASSER-V and pre-timed signal setting strategies. This multi-agent learning strategy of \"local first, then global\" effectively balances local autonomy and global coordination [57]. Intersection heterogeneity is a key challenge faced by network-level control. [PERSON] et al.