# Subway Station Capacity Maintained by Optimizing a Maintenance Schedule of Key Equipment
## 6 Simulation Study
### Passenger Flow Simulation Model  
The Xinjiekou station in Beijing subway line 4 is taken as a case study in this paper. Based on a field investigation of the station, the layout map and passenger flow data of the station are obtained. Figure 4: The flow chart for maintenance schedule optimization algorithm. The length of the station hall is 60 m, and the width is 17 m. The length of platform is 118 m, and the width is 9 m. The streamline layout of the station is shown in Figure 5.  
The AnyLogic model consists of a station environment model and a pedestrian behavior model. In the light of the layout of station, the station environment model is established in the first place. Firstly, the buildings of the station are built including the hall and the platform, such as walls, columns and obstacles. After that, various service facilities are built in the model, such as gates and escalators, and the corresponding parameters of 'line service' and 'escalator group' are set at the same time. Finally, the pedestrian sources and destinations are set with the 'target line'. The station environment model is completed. In the light of the layout of station, the station environment model is established, and the related environment model parameters are shown in Table 3. According to the streamline layout, the logical modules in the pedestrian library are used to construct the whole process from pedestrian generation, and the receiving services are established from various service facilities to the disappearance of pedestrians. The pedestrian behavior model is established and shown in Figures 6 and 7, respectively. It is necessary to calibrate the service parameters of the 'Ped Service' modules, and the related service parameters are shown in Table 4.  
In the simulation model, the pedestrian speed is 1 m/s. The subway train serving this station consists of six vehicles, and each vehicle is fitted out with four train doors. The headway time and dwell time running by the current timetable are 150 s and 30 s. Assume that there are no remaining passengers on the platform at the end of each alighting-boarding cycle. Total numbers of alighting passengers in each cycle during non-peak time and peak time are deterministic and equal to 160 and 510, respectively (In the Beijing subway, the peak time includes 7:00-9:00 a.m. and 6:00-8:00 p.m. and the rest of the operation time is non-peak time.). Passenger routing probabilities are estimated as the ratio of the passengers who arrived at an entrance and followed the specific route, by the total number of passengers who entered the system from this entrance. Other passenger flow data is described in Table 5.  
Due to the randomness of AnyLogic simulation results, multiple simulations experiments are carried out and the average value of the results is adopted. During non-peak time, the real time of the departure process and the arrival process is 134 s and 92 s, respectively. And the simulation time of the departure process and the arrival process is 130 s and 89 s, respectively. During peak time, the real time of the departure process and the arrival process is 187 s and 155 s, respectively. The simulation time of the departure process and the arrival process is 178 s and 148 s, respectively. The errors between simulation results and actual data are small. Therefore, the accuracy of the simulation model is verified. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Station Facilities** & **Parameter Name** & **Parameter Assignment** \\ \hline \multirow{4}{*}{Ped Service} & TVM & Delay time & normal (12.0, 40.0) * second \\  & Security check & Delay time & uniform (3.0, 6.0) * second \\ \cline{1-1}  & Gate & Delay time & exponential (3.0) * second \\ \cline{1-1}  & Escalator & Speed & 0.65 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Service parameters of the station facilities.