## 1 Introduction  
With the rapid development of urban public transportation (UPT), passenger flow prediction is very significant in meeting passengers' travel needs, which is one of the important issues in improving the UPT services. Recently, the research on passenger flow prediction has been converted from a single station to multiple stations because multi-station passenger flow prediction is more applicable in UPT. Due to the complex spatial features and time-varying traffic patterns of networks [1], passenger flow at a single station has been simultaneously affected by the spatio-temporal features of historical passenger flow at the directly or indirectly connected stations in the whole network [2]. Thus, a single station passenger flow prediction model could not dynamically and effectively predict the spatio-temporal distribution and congestion in the entire network, which limits the real-time passenger flow organization, formulation, and adjustment of the operation management strategy [3]. To this end, more and more researchers have devoted themselves to passenger flow prediction for multiple stations. How to deeply capture the complex spatio-temporal features to make a more accurate passenger flow prediction model for the whole network stations is becoming a hotspot in recent studies. In addition, many well-performing deep learning models continue to emerge, focusing on capturing spatio-temporal correlation between stations by constructing spatio-temporalfeature learners (STFL) in traffic inflow and outflow prediction [4]. [PERSON] et al. [5] proposed a deep neural network that simultaneously extracted the spatial features using graph convolution neural network (GCN) and the temporal features using long short-term memory (LSTM) to make both short-term and long-term traffic flow predictions. [PERSON] et al. [6] proposed a deep learning model combining convolutional LSTM (ConvLSTM) and stack autoencoder (SAE) to predict the short-term passenger flow of URT for multiple stations. ConvLSTM was used to extract spatio-temporal features of passenger flow based on thirteen external factors related to passenger flow. [PERSON] et al. [7] proposed a deep learning-based model named GCN-Transformer, which comprised the GCN for spatial features extracting and the modified Transformer for temporal features extracting for short-term passenger flow prediction of multiple stations. The above models are all hybrid models which capture the spatio-temporal features simultaneously to predict passenger flow at stations in the whole network. However, the passenger flow at the same station in different periods presents different characteristics, and various stations in the same period also have different passenger flow changes. We illustrate the differences by using Figure 1 as an example. Figure 1a,b show one-week inbound and outbound passenger flow from 4 March 2019 to 8 March 2019 at Xianhou Station and Dongzhai Station in the Xiamen bus rapid transit (BRT), respectively. Both stations had clear characteristics of inbound and outbound tidal flows in their respective time intervals, but the difference was that the largest volume of inflow and outflow between the two stations was nearly 16 times in the same period. Their variability was also quite different. These indicate that globally extracting the spatio-temporal features for multi-station passenger flow prediction in the whole network may not be powerful enough to achieve the expected performance for every station [8]. Making a more accurate multiple-station passenger flow prediction model for every station is necessary and significant. Furthermore, most existing studies on urban metro station classification were mainly based on the features of land location [9], point of interest (POI) [10; 11; 12], population distribution [11], station location [11; 13], length of road network [11], passenger flow [9; 11; 12; 14; 15], and their combinations. These studies were mainly divided into two directions [16]. The first was \"place oriented,\" which focused on land use function.