## 6 Discussion  
Experiments have shown that the proposed speed guidance algorithm can improve intersection efficiency to a certain extent, exhibiting the following characteristics under different traffic volumes and penetration rates: when the CAV penetration rate is constant, as traffic volume increases and the average number of stops and the average stop delay decrease significantly, but as the volume reaches a certain level, the guidance effect gradually diminishes. When the mixed traffic flow volume is constant, the higher the CAV penetration rate, the better the guidance effect. The speed guidance scheme proposed in this study can be applied to each approach lane of the intersection to enhance the overall efficiency of the intersection, and its optimization effects can also be extended to a broader area to improve regional traffic efficiency.  
The speed guidance algorithm proposed in this study guides the speed of CAV and indirectly affects HDV. When the CAV penetration rate is constant, as traffic volume increases, the number of guided CAV increases, indirectly affecting more HDV, which leads to a greater reduction in the average number of stops and average stop delay. However, when the traffic volume reaches a certain level, the degree of congestion increases. Although the number of guided CAV is high, the dense traffic flow on the road may prevent CAV from executing the guidance strategy (for example, if the guidance strategy is to accelerate, but due to vehicle queuing and small inter-vehicle distances, the safety distance constraint in the car-following model prevents the CAV from executing the acceleration command). When traffic volume suddenly decreases, the guidance strategy remains effective, but its impact may be diminished due to the sudden reduction in the number of CAVs. Conversely, when traffic volume suddenly increases to the point of congestion, vehicles queue and move slowly or even stop, rendering the speed guidance algorithm ineffective. When the mixed traffic flow volume is constant, a higher penetration rate of CAVs means that more vehicles in the mixed traffic flow are capable of providing guidance, resulting in a better guidance effect.  
Although this study considers the uncertainty of leading vehicles and conducts extensive simulation experiments on the proposed guidance algorithm, there are currently no practical experiments, and the real-world applicability of the proposed speed guidance method has not been assessed. Additionally, this research is based on certain assumptions, whereas actual urban road traffic scenarios (including pedestrians, bicycles, and buses) are more complex. Furthermore, this study only addresses speed guidance, without involving issues related to vehicle route optimization and traffic signal timing adjustments.  
Considering that the current research focuses on improving the traffic efficiency of urban road intersections and has not addressed the rapid transit needs of priority vehicles (e.g., ambulances, fire trucks, police cars), future research will analyze the impact of speed guidance measures on the environment, such as vehicle energy consumption and emissions. Additionally, further studies will be conducted on more traffic scenarios, such as rural roads, highways, and unsignalized intersections.