## 3 Experiments
### Data Description  
Two real-world ridership datasets are used to validate the effectiveness of the proposed (Transformer-K-Means)-(ResNet-GCN-AttLSTM): (1) the Beijing metro dataset, which is shared in [27]; (2) the Xiamen BRT dataset, which is collected from the BRT system in Xiamen, China. Because the Xiamen BRT adopts the closed viaduct mode, the mode of stations in the Xiamen BRT is similar with the stations in metro systems. Thus, we use the two datasets in our proposed model [28].  
The details of these two datasets are summarized in Table 1. The dataset in the Beijing metro is from 29 February to 1 April 2016, which contains five continuous weekly inbound and outbound passenger flows. As of April 2016, there are a total of 17 lines covering 276 stations. The dataset in the Xiamen BRT is from 4 March to 5 April 2019, which contains five continuous weekly inbound and outbound passenger flows. Tomb Sweeping Day is on 5 April 2019, and is one of China's traditional holidays. As of April 2019, a total of eight lines are covering 44 stations.  
Consequently, station-level passenger flow in the Beijing metro is much more complex than it in the Xiamen BRT. To avoid the influence of passenger flow at weekends, we only choose the passenger flow on workdays for study. The total five weekly inflow and outflow data are used in the classification block. The first four weekly data are for training, and the last week's data are for testing in the prediction block. The time intervals used in the two datasets are 10 min, 15 min, and 30 min, respectively. The two datasets' daily service hours are from 5:00 a.m. to 11:00 p.m., with both containing 18 h. Thus, DS in different time intervals is different, which is shown in Table 1.  
As shown in Figure 4, the real-time, daily, and weekly inflow and outflow at 1 st Wharf Station in the Xiamen BRT and No. 1 Station in the Beijing metro during the five workdays are quite different. Different weekly ridership at the same station is periodic and stable. It verifies that using the three types of data modes (real-time, daily, weekly) for temporal feature extraction is useful in our proposed model.  
Moreover, as shown in Figures 1 and 4, the inflow and outflow at the same station are also different. For example, No. 1 Station in the Beijing metro has a larger inflow, while there is a smaller outflow in Figure 4b. Xianhou Station in the Xiamen BRT has a smaller  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Description** & \multicolumn{2}{c}{**Beijing Metro**} & \multicolumn{2}{c}{**Xiamen BRT**} \\ \hline  & 29/02/2016–04/03/2016 & 04/03/2019–08/03/2019 & 07/03/2016-11/03/2016 & 11/032019-15/03/2019 \\ Train Timespan & 14/03/2016–18/03/2016 & 18/03/2019–22/03/2019 & 21/03/2016–25/03/2016 & 25/03/2019-29/03/2019 \\  & 28/03/2016–01/04/2016 & 01/04/2019–05/04/2019 & 05/04/2019 \\ Week Number & 5 & 5 & 5 \\ Day Number & 25 & 25 & 25 \\ Daily Service Hours & 5:00 a.m.–11:00 p.m. & 5:00 a.m.–11:00 p.m. \\ Time Interval & 10 min & 15 min & 30 min & 10 min & 15 min & 30 min \\ DS & 108 & 72 & 36 & 108 & 72 & 36 \\ Total DS & 2700 & 1800 & 900 & 2700 & 1800 & 900 \\ Line Number & 17 & & 8 & & \\ Station Number & 276 & & 44 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Description.  
Figure 4: Stations’ passenger flow in the Xiamen BRT and the Beijing metro. (**a**) Five weekly inflow and outflow at 1 st Wharf Station in the Xiamen BRT. (**b**) Five weekly inflow and outflow at No.1 Station in the Beijing metro.  
inbound flow, and a larger outbound flow in Figure 1a. The inflow and outflow at Dongzhai Station in Figure 1b are quite similar to the flows at 1 st Wharf Station in Figure 4a in the Xiamen BRT. Consequently, before passenger flow prediction, it is significant to classify the stations based on passenger flow similarity.