## 4 Results and Discussion  
As Figures 2 and 3 shows, freeway travel demand decreases as toll rates increase, and tolls show \"synchronous phases\" and \"asynchronous phases\" as toll rates increase, which is basically consistent with the findings of existing theoretical studies. In the lower toll rate \"synchronous phase,\" the increase in toll rates reduces travel demand to a certain extent, but the impact of the increase in toll rates outweighs the impact of the decrease in travel demand, so the overall tolls show an upward trend. In the higher toll rate \"asynchronous phase,\" further toll rate increases also reduce travel demand, but the impact of increased toll rates is weaker than the impact of decreased travel demand, resulting in a decrease in overall tolls. In the results from the simulation, the experimental scenario when the toll rate of CYN 0.45 per kilometer is used, the average freeway toll taken to reach the maximum value is CYN 58.9 thousand. Obviously, in theory, there exists a toll rate interval that makesthe freeway tolls the highest. In addition, different toll rates can lead to different travel demand; however, there could be situations with the same freeway toll, such as when the toll rate is CYN 0.3 per kilometer and CYN 0.6 per kilometer, respectively. Here, the freeway user share is 55.94% and 28.47%, respectivley, but the corresponding freeway tolls are very similar to each other, at CYN 53.7 thousand and 53.6 thousand, respectively. Notably, the \"synchronous phase\" and \"asynchronous phase\" of changes in freeway tolls in response to toll rate changes are not symmetrical, with the \"synchronous phase\" being relatively more dramatic and the \"asynchronous phase\" being relatively smoother. The rationality level varies in the interval of 0-1 while the tolls remain basically unchanged, as shown in Figure 4, which shows that the rationality level does not have a significant direct effect on the tolls. The main reason for this lies in the fact that the tolls in this study are defined as the average of the freeway charging in the last 600 s of the simulation to ensure that the value is stabilized. However, as shown in Figure 5, the degree of rationality affects the velocity at which tolls gradually reach a steady state from an initial state, i.e., the higher the degree of rationality, the less time it takes to reach a steady state. At the toll rate of CYN 0.45/km, the speed of the freeway is 120 km/h, and the speed of the parallel national and provincial arterial roads is 80 km/h; moreover, when the degree of  
Figure 3: Freeway toll rates and freeway user share. Figure 2: Freeway toll rates and total freeway tolls. rationality is 0.25, it takes 100 iterations of simulation for the toll to reach the steady state from the initial state, while, when the degree of rationality is 0.5, it takes only 10 iterations or less to reach the steady state. As a result, the degree of rationality does not affect the value of tolls in the steady state, but instead affects the time required for tolls to reach the steady state. This also brings to light the practice that the evaluation of the effects of freeway rate adjustment programs needs to be carried out after a specific time period, which depends on the rationality of local users' travel to a certain extent. In the \"synchronous phase,\" raising toll rates will lead to an increase in tolls, and, if an evaluation of the effectiveness of the rate scheme is carried out before a steady state is reached, it will result in an effectiveness evaluation that is lower than the actual benefits. In the \"asynchronous phase,\" raising toll rates will lead to a decrease in tolls, and, if an evaluation is carried out before a steady state is reached, an effectiveness evaluation that is higher than the actual benefits will result in a greater loss for the operation of the enterprise. Therefore, before determining the rate adjustment program or evaluating the effectiveness of the rate adjustment, it is  
Figure 4: Rationality and total freeway tolls.