## 1 Introduction
Subsequently, many researchers have investigated prediction models that combine multiple methods. This is because only RNN models cannot capture the complex data characteristics well, resulting in poor prediction accuracy. [PERSON] et al. [24] introduced a novel Wave-LSTM approach that incorporates wavelets and LSTM for predicting inbound passenger flows in urban rail transit. [PERSON] et al. [25] integrated spatio-temporal analysis with GRU for passenger flow prediction, and proved its superior accuracy and stability compared to the standalone GRU model. [PERSON] et al. [26] used K-means clustering to catch the characteristics of passenger flow and subsequently fed them into an LSTM model to achieve short-term passenger flow prediction. However, these studies have some drawbacks in that they cannot predict multiple sites within the network simultaneously due to the inability to capture the spatial correlation. Many researchers use deep neural networks (DNN) in rail transit passenger flow prediction tasks, which are capable of acquiring spatio-temporal information and topological information. [PERSON] et al. [27] proposed a DNN-based traffic flow prediction model (DNN-BTF) and demonstrated its ability to predict traffic flow using big data. Combining CNN [28] can also automatically extract some important spatial features. [PERSON] et al. [29] introduced a CNN-based forecasting model named DeepST for urban pedestrian flow prediction. They separated the temporal characteristics of pedestrian flow into three modules: closeness, period, and trend, combined with spatial features input to the CNN, and considered the influence of holidays and meteorological information. This method comprehensively captures the elemental features affecting pedestrian flow and achieves good prediction performance. In 2015 [PERSON] et al. [30] proposed Deep Residual Learning, which addresses the gradient disappearance and explosion problem by introducing \"residual connections\". It has been effectively utilized in diverse datasets and applications, particularly in road traffic scenarios. Based on this, [PERSON] et al. [31] introduced an upgraded version of DeepST, named ST-ResNet, which leveraged residual network architecture to enhance its spatio-temporal modeling capabilities. After validation on bicycle and taxi datasets, the results proved to be better than DeepST. However, this excellent method has not been applied to rail passenger flow prediction scenarios yet, which brings inspiration for the generation of this article. As deep learning techniques evolve, attention mechanisms [32] have become a hot topic for researchers. The basic principle of the attention mechanism is to make the model pay more attention to important information by assigning different weights to different parts or features of the input. Through the attention mechanism, the model can automatically learn which parts or features are more important for the current task, thus improving the performance and effectiveness of the model. In recent years, it has made significant progress in various fields. [PERSON] et al. [33] considered the use of the attention mechanism for discriminative vision tasks, and experiments demonstrated that the attention mechanism achieved improvements on many models, in image classification on ImageNet and object detection on COCO. In the problem of time series data prediction, the addition of the attention mechanism can help the model to better handle time series data. [PERSON] et al. [34] used a two-stage attention-based recurrent neural network (DA-RNN) to capture long-term temporal correlations, outperforming state-of-the-art methods in time series prediction. [PERSON] et al. [35] introduced a localized spatio-temporal neural network (STNN), which employs novel spatio-temporal convolutional and attentional mechanisms to learn universal spatio-temporal correlations and improve traffic flow prediction accuracy. The above describes the evolution of passenger flow forecasting methods from traditional methods to artificial intelligence methods.