## 5 Discussion  
### Influence of Design Parameters  
From Sections 4.1 and 4.2, we can summarize the influence of the design parameters on the fitting parameters and measurement results as follows:  
1. When the arrival rate of the inter-city transportation vehicle \(\lambda_{a}\) increases, \(\mu_{1}\) fluctuates within a certain range, but \(\mu_{2},U\) and \(D\) have a significant decrease; \(C\) and \(k_{C}\) fluctuate within a certain range, while there is a violent decrease in the value of \(\varphi_{d}\). Since changes in the arrival rate of the inter-city transportation vehicle \(\lambda_{a}\) hardly affect the physical structure of the queuing system, the service rate in non-congestion \(\mu_{1}\) and the capacity \(C\) do not change significantly. However, the larger the \(\lambda_{a}\), the more profound the congestion caused in the queuing system, and the more tremendous the sudden drop in capacity, resulting in a significant drop in \(\mu_{2}\) and \(\varphi_{d}\).
2. When the number of passenger lanes in queue \(n\) in the pick-up zone increases, \(\mu_{1}\) has a significant increase, but \(\mu_{2},U\) and \(D\) have a significant decrease; \(C,k_{C}\) and \(\varphi_{d}\) have a significant increase. After the increase in the number of passenger lanes \(n\), the waiting points of vehicles become more in non-congestion, which strengthens the efficiency of the connected roadway and triggers the increase in \(\mu_{1}\),\(k_{C}\) and \(C\). However, there is weaker resistance and evacuation ability for congestion, which triggers the decrease in \(\mu_{2},U,D\), and the increase in \(\varphi_{d}\).
3. When the number of lanes \(m\) in the pick-up zone increases, \(\mu_{1},\mu_{2},U\) and \(D\) all have a significant increase; \(C\) and \(k_{C}\) have a significant increase, but \(\varphi_{d}\) has a significant decrease. After the increase in the number of vehicle lanes \(m\), the larger number of vehicle lanes directly increases the service rate of the queue to the vehicles and eases the sudden drop in capacity, which triggers the increase in \(\mu_{1},\mu_{2},D,U,k_{C},C\), and the decrease in \(\varphi_{d}\).  
Figure 7: Sensitivity analysis diagram of different parameters. (**a**) is sensitivity analysis of parameter \(\mu_{1}\); (**b**) is sensitivity analysis of parameter \(\mu_{2}\); (**c**) is sensitivity analysis of parameter \(D\); (**d**) is sensitivity analysis of parameter \(U\).  
When the buffer of the single vehicle lane \(N\) increases, \(\mu_{1},\mu_{2}\) and \(U\) all have a significant increase, but \(D\) has a slow decrease; \(C\) and \(k_{C}\) have a significant increase, but \(\varphi_{d}\) has a significant decrease. After \(N\), which is similar to the effect of parameter \(m\), the greater buffer directly increases the service rate of the queue to the vehicles and eases the sudden drop in capacity, which triggers the increase in \(\mu_{1},\mu_{2},D,Uk_{C},C\), and the decrease in \(\varphi_{d}\).  
Based on the actual operational experience of the airport/station, the variation in the fitting results due to the change in controllable design parameters is in line with the actual situation. Moreover, based on the fitting results, we believe that the values of the design parameters did not have an effect on the fitness in terms of a clear trend. In addition, due to the different magnitudes between the different design parameters, we did not compare their effects on each fitting parameter and measurement result.