## 4 Control Mechanism Transformation in New Heterogeneous Traffic Flow Environments on Urban Roads
### Traffic Control Mechanisms for Arterial Scenarios  
Arterial traffic control represents the intermediate level connecting isolated intersection and network-level control, extending the control mechanisms established in Section 5.1 to coordinate multiple adjacent intersections. While isolated intersection control provides the foundation for intelligent connected traffic management, practical implementation requires expansion to corridor-level coordination. Arterial traffic control represents the intermediate level connecting isolated intersection and network-level control, with its core challenge lying in coordinating control strategies across multiple adjacent intersections to achieve traffic flow optimization over larger areas. Under intelligent connected environments, arterial traffic control mechanisms have achieved breakthrough developments in the following aspects. Hierarchical control architectures provide effective solutions for complex arterial traffic systems. [PERSON] et al. proposed a two-layer longitudinal control method targeting heterogeneous traffic environments (intelligent connected vehicles and human-driven vehicles) on signalized arterials, effectively addressing traffic control complexity through hierarchical design with upper-layer travel time optimization and lower-layer intelligent connected vehicle movement optimization across urban arterial links [49]. This method, based on vehicle physical constraints and the relationship between estimated arrival times and traffic signal timing, considers four control scenarios and achieves travel time reductions of up to 29.33%. [PERSON] et al. further validated the effectiveness of hierarchical control architectures through large-scale experiments in actual road environments, using 100 longitudinally controlled vehicles as Lagrangian traffic actuators in the CIRCLES project with a hierarchical control architecture named \"MegaController\" [50]. This architecture, comprising an upper-layer centralized \"Speed Planner\" optimization control algorithm and a lower-layer distributed vehicle-mounted control system, successfully suppressed traffic flow instabilities and reduced energy waste. These studies demonstrate that hierarchical control architectures can simultaneously satisfy dual requirements for global optimization and local execution, particularly suitable for complex traffic control in arterial scenarios. Spatiotemporal collaborative optimization strategies represent key technologies for arterial control. The dynamic temporal and spatial speed control framework developed by [PERSON] et al., specifically designed for partially connected automated vehicles on signalized arterials, achieved traffic efficiency improvements through dynamic regulation of vehicle speeds in spatiotemporal dimensions [51]. This method first proposes a speed control optimization approach considering signal status and queuing conditions to minimize stop frequency for both the vehicle itself and following vehicles; second, introduces a secondary speed control method based on dynamic control areas to guide intelligent connected vehicles to target positions; and finally, designs corresponding dynamic variable parameter models to optimize control area operational parameters under different penetration rates, minimizing total fuel consumption for all vehicles. Simulation results indicate that at a 30% penetration rate, this strategy reduced total stop delays by 60.9% and saved 6.5% of total fuel consumption. The decentralized approach for connected automated vehicle trajectory optimization along signalized arterials proposed by [PERSON] et al. simultaneously considers both longitudinal and lateral dimensions [52]. This method employs a two-stage model: the first stage roughly estimates the minimal travel time required for a single connected automated vehicle traveling along the arterial, while the second stage optimizes connected automated vehicles' longitudinal and lateral behaviors by minimizing delay and lane-changing costs.