## 4 Case Study  
In order to evaluate the effectiveness of the DRLDA algorithm and the ARMA-RBFNN-GA method, we used actual sensor data from Beijing Metro Line 4 (such as Xinjiekou, Pinganli, Xisi and Lingjhinutong) for simulation experiments, as shown in Figure 4.  
In practice, these data include train parameters, passenger flow data and railway line data. The train parameters were a type B train with 6-train marshaling, SFM05 model, a total staffing capacity of 1440, etc. The train adopted DC 750 V third rail power supply. The designed maximum speed was 80 km/h. The passenger flow data were derived from historical operation data, including dynamic passenger flow demand data and train operation schedule data. For example, the metro passenger flow data were collected from the Automatic Fare Collection (AFC) data, survey data were provided by the Beijing MetroCompany and the railway passenger flow data were obtained from statistics provided by the Passenger Transport Center, Beijing Railway Administration. Since the railway timetable is affected by many factors, it cannot be easily changed. The planning time range for the metro should be determined based on the Beijing Metro Line 4 (such as Xinjiekou, Pinganli, Xisi and Lingjinghutong) and the actual travel time range of passengers. In addition, in order to ensure that the case study was representative, we investigated the original passenger flow data of the Xinjiekou, Pin'anli, Xisi and Lingjinghutong stations of Beijing Line 4 from 7:00 to 9:00 on weekdays for one month, with 30 s interval statistics. The passenger flow data from one week is visualized in Figure 5. Railway line data include stations, line length, planned train operating time, speed limit, etc. Actual energy consumption data were obtained by the Beijing Metro Company.  
As shown in Figure 5, passengers embarking on/disembarking from the Beijing metro conform to the actual passenger flow distribution.  
In AnyLogic simulation software, as shown in Figure 6, the pedestrian library was used to construct a simulation of passengers entering and leaving the station and queuing for trains. The PedSource module was used to generate passengers. The passengers were shown to enter the platform through the gate, disperse in front of each door and wait for the train to arrive and stop before boarding. Then, the PedSink module was applied to demonstrate the disappearance of the passengers. Some passengers disembarked from the train, made their way from the platform to the gates and left through those gates. These  
Figure 4: Illustration of Beijing Metro Line 4 (such as Xinjiekou, Pinganli, Xisi and Lingjinghutong) (Source: Baidu map).  
passengers disappeared, thus completing the simulation of passengers entering and leaving the station and embarking on/disembarking from the train.  
When building the train operation simulation, the Train Source module was used to generate the train. In the simulation, the train runs through track operation into the platform, which was completed by the Train Move To module. The TrainUpload module was used to represent the process of passengers embarking on/disembarking from the train. Then, the train goes to the next station. According to the actual operation of the train, several simulation parameters of the train (such as train and line length, running speed, etc.) were set with the property inspector of Train Source and Train Move To, as shown in Figure 7.