## 5 Simulation Experiments
### Macro Traffic Flow Case Study  
This section presents a simulation conducted in accordance with the real situation depicted in the video data, specifically with regard to the traffic flow. The scenario involves 500 vehicles per hour on the ramp, of which 50% are CAVs. The main-road traffic flow is 1700 vehicles per hour, all of which are MDVs. Among them, large, medium, and small vehicles account for 1%, 2%, and 97%, respectively. This study considered the size and maneuverability of different vehicle types. The parameters are adjusted accordingly during the simulation. The study found that 50% of participants were conservative, 30% were moderate, and 20% were aggressive, respectively. Figure 11, Figure 12, and Figure 13 separately show the displacement-time curves of vehicles with no control, pure game control, and reinforcement learning game control. This set of figures clearly shows the location of the merging vehicles completing their merges. In the figures, the red curve represents CAVs, the blue represents MDVs, the dark curve represents vehicles on ramps and acceleration lanes, and the light curve represents vehicles on the main road. The slope of the curve represents the speed of the vehicle, and the transition point from dark to light on the curve represents the location and timing of the vehicle merging into the main road. By comparing these three figures, it can be clearly seen that under the control of the proposed method, the position of the on-ramp CAV merging into the main road is farther away from the end of the acceleration lane and with higher speed. Figure 14, Figure 15, and Figure 16 show the speed-time curves of the vehicle, respectively. Comparing Figures 15 and 16, speed fluctuation under the control of DRL and the game is significantly less than that under the game only. Because of integrating DRL into the game, the success rate and security have been further improved, and the fluctuation of speed has also been alleviated. These results demonstrate that the uncontrolled case presents a challenging scenario for merging due to the high density of traffic flow on the main road and that it fails to consider the potential for MDVs to engage in cooperative actions. In the case of pure game control, the results are similar to the simulation of the micro case study. The game model is unable to identify a strategy that can reach equilibrium quickly, and it is difficult to coordinate the actions of conflicting vehicle pairs in most cases. The speed also shows more dramatic fluctuations in the car. The reinforcement learning game serves to regulate the actions of merging CAVs, which are able to respond to the varying styles of MDVs that effectively predict their degree of coordination. In the case of conservative and moderate games, the realization of coordination is largely achieved. In the case of aggressive games, due to the high proportion of MDVs refusing to yield, it is difficult for merging vehicles to be inserted in front. Such a scenario is not implausible. Figure 11: Vehicle displacement–time curve without control. Figure 14: Vehicle speed–time curve without control. Figure 12: Vehicle displacement–time curve under pure game control. Figure 13: Vehicle displacement–time curve under reinforcement learning game control. The results of this study indicate that the maximum merging traffic flow that the roadway can withstand without control is approximately 700 vehicles per hour. However, under the control of the decision algorithm proposed, the maximum merging traffic flow is more than 850 vehicles per hour, an increase of about 21.4%. Figure 17 depicts the average speeds attained through the roadway for varying traffic volumes. The results show that the average speed of the merging vehicle is increased by 18.3% when it coordinates with the main-road vehicle by inserting itself in front of it, in comparison to the no-coordination strategy.