## 4 Numerical Experiments  
In order to obtain traffic data and analyze the influence of the design parameters of the pick-up zone of the hub on the connected roadway traffic characteristics, we built a simulation model based on the Anylogic simulation platform to simulate the queuing of passengers waiting for vehicles in the pick-up zone of the hub and the queuing of vehicles carrying passengers in the connected roadway.  
Anylogic is a modeling tool based on the latest advances in modeling science and information technology over the past years and is suitable for modeling and simulation in a wide range of industries, such as supply chain, manufacturing, transportation, warehouse operations, rail logistics, etc. The simulation software supports intelligent agent-based, system dynamics and discrete-event modeling approaches as well as multimethod hybrid modeling.  
The hub simulation model constructed in this paper is based on agent-based and discrete-event modeling, and the three main types of agents are inter-city traffic vehicles (we use aircraft and train agents), pedestrians and cars agents. The simulation process includes behaviors or scenarios such as aircraft or train arrivals and departures; passengers arriving at the hub choosing to board or take the train; passengers landing or arriving at the hub choosing to leave directly or wait in the passenger waiting area, leaving by public transportation; and vehicles arriving at the vehicle storage park in the connected roadway of the hub and driving from the storage park to the vehicle serving area, carrying passengers and then leaving. We analyzed the traffic characteristics of the traffic flow in the connected roadway, so we measured the traffic flow, average density and average speed in the roadway every 10 min during the simulation. The simulation duration was set to one week, and the obtained traffic data were analyzed. Using simulation, we carried out the following work:  
1. We changed the design parameters of the hub (including the arrival rate of inter-city transportation vehicle \(\lambda_{a}\), number of passenger queues \(n\), number of lanes \(m\) and buffer of single vehicle lane \(N\)) to set several simulation schemes. Then we obtained the different flow-density-speed data, fitting parameters (including the service rate of non-congested status \(\mu_{1}\), service rate of congested status \(\mu_{2}\), threshold at congested \(U\) and threshold at non-congested \(D\)) by fitting the model shown in Equation (13), and obtained measurement results (including the capacity of the connected roadway \(C\), critical density of capacity \(k_{C}\) and the degree of capacity drop \(\varphi_{d}\)).
2. We analyzed the influence and tendency of different design parameters on the fitting parameters, measurement results and fitting curve shape.
3. We selected other classical models for fitting the flow-density-speed data, and compared their errors and fitness in different areas. It verified the advantages and effectiveness of our model in describing the unique traffic characteristics of the inter-city hub connection roadway.
4. In order to analyze the sensitivity of fitting parameters, we changed the values of each fitting parameter of the benchmark and obtained the measurement results. Then we analyzed the influence and tendency of different fitting parameters on the measurement results and fitting curve shape.