## 4 Examples and Discussion  
### Simulation Platform and Scenarios  
To validate the proposed control method for CAVs, this study utilizes SUMO simulation software to develop an intersection traffic simulation platform. The simulation platform integrates various components seamlessly, ensuring a comprehensive evaluation of the proposed control method for CAVs. This platform adopts a modular simulation approach, featuring a visual interface, result data collection, and other essential functions. The detailed architecture of this simulation platform is illustrated in Figure 4, providing a visual representation of the component interactions and their contributions to the system's overall functionality. The functional modules of the simulation platform illustrated in Figure 4 are described in detail in Table 1.  
Figure 4: Architecture of the simulation platform.  
Within the platform, SUMO constructs the fundamental simulation environment and supplies the RL framework with essential simulation outcome data. Through the Traci interface, the RL framework retrieves critical information for the evaluation, including intersection geometry information (lane and signal IDs), vehicle dynamics (speed, acceleration, and driving distance), and signal timing details (signal phase duration). Subsequently, an interactive environment integrating multiple data sources is developed to evaluate vehicle control algorithms via the RL framework.  
For the analysis and verification of the proposed control method's effectiveness, this study establishes a simulation scenario utilizing road map information and signal timing data from Lankershim Boulevard, featured in the Next Generation Simulation (NGSIM) dataset. Several experiments are carried out in this simulation scenario to assess the performance of the CAV, providing a comprehensive examination of the control method's real-road applicability.  
Figure 5 illustrates a simulation scene with four adjacent urban signalized intersections along Lankershim Boulevard. Upon entering each intersection, vehicles receive pertinent environmental information. The mixed traffic flow on the road is composed of the CAV and HVs, with HVs following SUMO's default Krauss car-following model and the CAV being algorithmically controlled. This research focuses on investigating the longitudinal car-following behavior of the CAV within mixed traffic flow, with known parameters such as signal timing and road length. In the experiment, the Krauss model and three RL algorithms, namely TD3, DDPG, and DQN, are adopted to govern the car-following movement of the CAV. Data from the simulation are gathered across various car-following modes for subsequent comparison and analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Functional** & **Description** \\
**Module** & This module is responsible for creating and editing the road network, with specific functions such as determining the starting and ending points, adding traffic demands, dividing the lanes, and configuring signal timing. \\ \hline Simulation operation & This module is responsible for running the simulation program, with specific functions including setting the simulation duration, calculating the state space and reward function, and outputting the action strategy, as well as resetting the environment when the algorithm training termination conditions are met. \\ \hline Data collection & This module is responsible for data acquisition and saving, with specific functions including acquiring vehicle trajectory data through the Traci interface, saving the simulation results in a numerical matrix, and outputting the data in *.csv file format for later organization and analysis. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Functional modules of simulation platform.  
Figure 5: The simulation scenario (Lankershim Boulevard).