## 3 Experiments
### Experiment Results
#### 3.4.1 Classification Results  
\(ASCR\) with different \(K\) in different time intervals in the Xiamen BRT and the Beijing Metro are shown in Table 4. The numbers in bold refer the best results in different values of \(K\). Since the Xiamen BRT network is much simpler than the Beijing metro network, the situations with the highest \(ASCR\) = 0.8 are more than those in the Beijing metro. For 10 min, 15 min, and 30 min time intervals in the Xiamen BRT, we choose \(K\) = 4 as the final number of categories for station classification. Because of the fact that when \(K\) is 4, the \(ASCR\) of all categories is 0.8, which is a more stable result. For 10 min, 15 min, and 30 min time intervals in the Beijing Metro, we choose \(K\) = 3, 4, 4 as the classification results, respectively. The classification results show that with more data, the number of station categories will not be too large. It is notable that only the station number is provided for the Beijing metro; we cannot list the stations' names. More information about the Beijing metro can be found in [27]. The names of the stations in the Xiamen BRT are listed in Table 5[29]. We only list the station classification results for the Xiamen BRT in 15 min time interval as an example. The station classification results are shown in Table 6, and the visualization of inflow from 3 April 2019 to 8 April 2019 in the four categories of stations is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ASCR**} \\ \cline{2-7} K & \multicolumn{3}{c}{**Xiamen BRT**} & \multicolumn{3}{c}{**Beijing Metro**} \\ \cline{2-7}  & **10 min** & **15 min** & **30 min** & **10 min** & **15 min** & **30 min** \\ \hline
2 & **0.8** & **0.8** & 0.795 & 0.794 & 0.797 & 0.797 \\
3 & **0.8** & 0.795 & 0.795 & 0.8 & 0.797 & 0.797 \\
4 & **0.8** & **0.8** & **0.8** & 0.787 & **0.798** & **0.798**Figure 5: _Cont_. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Station Name** & **No.** & **Station Name** & **No.** & **Station Name** & **No.** & **Station Name** \\ \hline
1 & 1 st Wharf & 12 & Caitang & 23 & Dongzhai & 34 & Pantu \\
2 & Kaihe Intersection & 13 & Jinshan & 24 & Tiancuo & 35 & Binhai Xincheng (Xike) \\  & & & Municipal & & & & Junction \\
3 & Sibei & 14 & Administrative Service & 25 & Xiamen North Railway & 36 & Guanxun \\  & & Center & & Station & & & \\
4 & Douxi Road & 15 & Shuangshi Middle & 26 & Institute of & 37 & Light Industry and \\  & & School & & Technovation & & Food Park \\
5 & Ershi & 16 & Xianhou & 27 & Gaoqi Airport & 38 & Sikouzhen \\
6 & Jinbang Park & 17 & Airport Terminal 4 & 28 & Fenglin & 39 & Industrial Zone \\
7 & Railway Station & 18 & Tan Kah Kee Stadium & 29 & Dong’an & 40 & 3 rd Hospital \\
8 & Lianban & 19 & Chengyi University & 30 & Houtian & 41 & Chengnan \\
9 & Longshanqiao & 20 & Huaqiao University & 31 & Dongting & 42 & Tong’an Junction \\
10 & Wolong Xiaocheng & 21 & University Town & 32 & Meifeng & 43 & Hongwen \\
11 & Dongfang & 22 & Chinese Academy of & 33 & Caidian & 44 & Qianpu Junction \\  & & Shanzhuang & & Sciences & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Names of stations in the Xiamen BRT. As shown in Figure 5, Railway Station (No. 7), Municipal Administrative Service Center Station (No. 14), Xiamen North Railway Station (No. 25), and Qianpu Junction Station (No. 44) have been divided into separate categories. Thus, the four stations have been deleted in our model based on the following Algorithm 1: K-Means for station classification. All four stations are importation stations because of their urban functions and geographical position. Therefore, there are 40 stations in the Xiamen BRT that have been used for classification and prediction, which are divided into four categories in 15 min time intervals. The max volume of inflow at the first category of stations is less than 300, which are defined as the small ridership stations.