Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network

Abstract

Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model.

1. Introduction

With the rapid growth of the urban population, immense traffic pressure has inevitably been imposed on transportation systems. Compared to other transportation modes, rail transit, with its efficiency and high passenger capacity, is integral to sustainable development. As the rail transit network expands and urban land remains limited, metro stations have become larger and deeper to alleviate traffic congestion. This has resulted in significant passenger flows, especially during peak hours. Passenger flow is a fundamental measurement reflecting the crowding level in the increasingly extensive and complex metro network system. Accurately and effectively predicting passenger flow enhances overall metro operational efficiency, alleviates peak-hour congestion, and reduces operating costs. Therefore, as a crucial component of intelligent transportation systems, scientifically forecasting passenger flow holds essential practical significance [1]. Passenger flow data refers to time series data consecutively recorded within a specific time interval. The analysis of passenger flow data is the core of metro passenger flow prediction, helping to find the changes in passenger flow and providing the scientific basis for prediction.

In recent decades, there has been a sustained emphasis on time series prediction in academic research, with widespread applications across diverse fields such as financial market prediction [2], climate prediction [3], and traffic prediction [4,5]. Existing studies can be generally categorized into two classes: traditional methods and deep learning methods. Traditional methods include statistical analysis and machine learning approaches. Statistical analysis-based methods often treat passenger flow data as a time series prediction problem. For instance, the Autoregressive Integrated Moving Average (ARIMA) model, developed from autoregressive models, is one of the most typical time series forecasting models. Refs. [6,7,8] proposed a framework for analyzing short-term passenger flow during the special event based on the ARIMA model, demonstrating its superior accuracy and reliability. In addition to passenger flow, ARIMA is widely employed for stock price forecasting [9] and traffic prediction [10]. While statistical forecasting models such as ARIMA have proven effective in certain contexts, they often struggle with the nonlinear capabilities [11] inherent in metro passenger flow data. Machine learning methods provide a more flexible, data-driven alternative for learning temporal dynamics [12]. Traditional machine learning models, such as K-Nearest Neighbors [13], Support Vector Machine [14], and neural network models [15], are trained on historical data through supervised learning for predictive tasks. While these models can capture specific nonlinear structures within metro passenger flow data, they still face limitations in feature engineering and accuracy in complex scenarios. Deep learning, however, has emerged as a powerful tool, capable of leveraging potent nonlinear modeling capabilities, capturing more abstract latent features from extensive datasets, and reducing the need for manual feature engineering and model design [16].

Over the past few years, deep learning methods have made remarkable breakthroughs [17]. In studies of traffic flow or passenger flow using neural networks, previous studies are generally based on Multilayer Perceptron (MLP) and their variants [18,19]. It generates intermediate hidden layer feature representations through a series of non-linear layers to ensure ease of implementation. Yet, their accuracy tends to decrease when handling high-dimensional complex data [20]. Recurrent neural network (RNN) is another deep learning model suitable for processing sequential data. Unlike Multilayer Perceptron, RNN units integrate current data and memory capabilities. However, retaining long-term memory often leads to vanishing and exploding gradient problems, making it difficult to capture long-term dependencies [21]. To tackle this challenge, Long Short-Term Memory (LSTM) and gated recurrent unit (GRU) introduce memory cells, a distinctive form of hidden states designed to retain additional information [22], achieving improved efficiency in traffic flow prediction tasks. Meanwhile, road networks and metro networks encapsulate traffic and passenger flow data, offering datasets rich in both temporal and spatial location information. Previous studies mainly focused on temporal dependencies, overlooking valuable spatial information, which led to an inadequate capture of essential spatial dependencies. Convolutional neural networks (CNN) and graph convolutional networks (GCN) play a pivotal role in modeling spatial dependencies. Ref. [23] transformed traffic velocity across the entire road network into a sequence of static images, utilizing deep convolutional neural networks (DCNN) to proficiently capture spatial correlations. Similarly, Ref. [24] represented public transit data as spatiotemporal image matrices, showcasing the efficacy of the CNN-LSTM model in sequence predictive tasks.

However, it is noteworthy that CNNs are inherently designed for Euclidean domain data (regular grids, etc.) and may require enhancement when handling non-Euclidean domain data, such as topological structure. With the rise of graph neural networks, GCNs are increasingly adopted to replace CNNs, generalizing the applicable scope of traditional convolution from regular grid-based data to arbitrary graph-structured data. Ref. [25] embedded GCN into gated recurrent units (GRUs) and integrated graph convolutions into recurrent units to enhance dynamic correlation extraction. The message-passing paradigm of GCN models makes it challenging for them to have a global perspective on variations, and they are also insensitive to changes in the temporal dimension. Attention Mechanism is a general method that, by introducing learnable weights, allows the model to have a global perspective and dynamically adjust its focus based on the significance of different input parts. In recent years, the attention mechanism has recently been widely used in multivariate time series prediction. SAnD [26] and DSANet [27] have demonstrated the effectiveness of attention mechanisms in multivariate time series modeling. Furthermore, Ref. [28] integrated LSTM and attention mechanism, achieving excellent results in the tasks of predicting short-term passenger flow data in large-scale metro systems.

Despite the excellent performance of the aforementioned deep learning methods in prediction tasks, further exploration is needed in capturing both local and global dependencies of metro passenger flow. In the spatial dimension, traditional graph neural network models primarily focus on local spatial dependencies of nodes, making it difficult to capture global spatial dependencies. Therefore, combining GCN with a global attention mechanism could further explore the balance between local and global information in the spatial dimension. In the temporal dimension, traditional attention mechanisms are often insensitive to the contextual relationships of observations at time points. Thus, integrating convolutional networks with attention mechanisms can enhance the utilization of local contextual information, while series decomposition can better capture the global characteristics of time series. This paper proposes a novel STGNN framework for multistep passenger flow prediction tasks. The primary contributions of this research can be summarized as follows:

• A novel spatiotemporal graph neural network (STGNN) is proposed for multistep passenger flow prediction in metro stations.
• A spatial modeling module is proposed, which consists of a dynamic global attention network (DAGN) and graph convolution network (GCN). DAGN implicitly captures the dynamic influence of passenger flow variation between global node pairs, and GCN can integrate the structural information of the input graph.
• A temporal modeling module consisting mainly of series decomposition blocks and locality-aware sparse attention blocks (LSA). A series decomposition block is employed to better capture the global characteristics of time series. At the same time, the LSA block can extract multiple local contexts and reduce the computational complexity for long sequence modeling.
• Both simulation and real-world datasets are used in the experiments. For simulation data, the simulation scenario is the real-world 3D architecture model of Chengdu Metro Line 10 Shuangliu International Airport Terminal 1 station. The passenger flow data are acquired based on AnyLogic pedestrian simulation. For large-scale real-world data, the Automatic Fare Collection (AFC) data in Hangzhou, China, are acquired. The experimental results demonstrate that our proposed STGNN outperforms all those ten baselines.

The remainder of the paper is organized as follows. Section 2 summarizes the related works. Section 3 formulates the multi-step metro passenger flow predicting problem and describes the structure and mathematical formulation of the proposed STGNN model. Section 4 compares the multi-step prediction performances of the proposed model with other benchmark models based on both simulation and real-world datasets and conducts and analyzes the ablation experiments. Finally, Section 5 concludes the paper.

2. Related Works

2.1. Attention-Based Models

Since the Transformer [29] has emerged as a pivotal advancement in deep learning, the attention mechanism has been widely applied to time series tasks. This mechanism converts the input into queries, keys, and values and dynamically assigns weights to each value based on the relationship between the query and its corresponding key. Informer [30] and Reformer [31] achieve sparse attention for transformers by using strategies where Informer selects representative query prototypes and Reformer compares only similar queries and keys. These approaches reduce computation time and may improve forecasting. WHENS [32] combined wavelet attention and DTW attention with BiLSTM, enhancing the model’s predictive capabilities while addressing the distortion issues in multivariate time series. ConvTan [33] proposed a new framework for multivariate time series tasks, which builds on the Transformer by introducing absolute and relative positions and combining them with convolutional layers to improve the position and data embedding of time series data. ITransformer [34] revisits the architecture of the Transformer by adopting an inverted perspective, treating the entire time series of each variate as a separate token. Although the aforementioned methods have modified the attention mechanism in the Transformer architecture and achieved good results, they all overlook the drawback of dot-product self-attention being insensitive to the local context. Therefore, before implementing self-attention at time points, we attempt to use causal convolution to learn the local context of each point to mitigate this drawback.

2.2. Spatial-Temporal Graph Neural Networks

Spatiotemporal graph neural networks (GNNs) have become a common model for traffic forecasting due to their ability to handle spatiotemporal relationships effectively. MTMGNN [35] combined Graph Attention Networks and Diffusion Graph Neural Networks into a multi-graph neural network, extracting complex features from various graphs by inputting adjacency matrices of different types of metro networks. MST-GRT [36] proposed a multi-graph neural network that aggregated metro passenger flow data from smaller to larger time granularities, enhancing the model’s predictive performance. Ref. [37] designed a novel framework for metro passenger flow prediction, STP-TrellisNets+. This network can capture spatial correlations in passenger flow and both long-term and short-term correlations, while also addressing the flow discontinuity issue. Ref. [38] introduced the Spatiotemporal Multi-Graph Convolutional Network (ST-MGCN) by encoding the non-Euclidean pairwise relationships between regions into multiple graphs and utilizing multi-graph convolution to explain the impact of distant regions on the current regions. Ref. [39] extended this idea by adding the correlation of graph structure between preceding and succeeding time steps, proposing the Spatiotemporal Synchronous Graph Convolutional Network (STSGCN). Ref. [40] proposed a multi-level attention network to model the dynamic spatial-temporal dependencies among multiple geographic sensor time series. Ref. [41] integrated spatial-temporal attention mechanism and spatial-temporal convolution to capture dynamic spatial-temporal correlations and spatial patterns. Ref. [42] proposed an adaptive graph spatial-temporal transformer network to model the cross-spatial-temporal correlations on the spatial-temporal graph directly using local multi-head self-attention. Ref. [43] proposed a spatial-temporal memory-augmented multi-level attention network to explicitly model both short-term and long-term dependencies at different spatial scales. These methods primarily focus on the local spatial dependencies of graph network nodes, neglecting dynamic global spatial dependencies, which may result in the loss of information from distant nodes. Therefore, we attempt to capture local spatial dependencies using GCN while employing dynamic global attention to implicitly capture the dynamic influence of any global node pairs. Additionally, we use series decomposition to further extract the temporal features of both long-term and recent-term passenger flows, aiming to explore the potential of guiding passenger flow prediction from the perspective of series decomposition.

3. Methodology

3.1. Problem Formulation

Generally, passenger information primarily comprises three main variables: passenger flow, density, and speed. In this paper, the goal of multistep pedestrian flow forecasting is to predict multistep passenger flow in a certain future period based on historical passenger flow.

Definition 1 

(Pedestrian Flow Network). We define a pedestrian flow network as an undirected graph G = (V, E) and treat sensors as graph nodes, where V is a finite set of |V| = N nodes and E is a set of |E| = M edges. N and M represent the number of nodes and edges in the graph topology structure.$A\in {\mathbb{R}}^{N\times N}$is the adjacency matrix of the link set accommodating the structural information of graph G. The variable ${A(l}_i, l_j)$denotes whether link i and link j are connected, i.e., ${A(l}_i, l_j)=1$indicates $l_i$and $l_j$are connected or ${A(l}_i, l_j)=0$, otherwise.

Definition 2  

(Signal Matrix). The observation on the graph G at the time step t is regarded as matrix $X_t$= $(x_{t,1}, x_{t,2}, \cdots ,x_{t,N}{)}^T\in {\mathbb{R}}^{N\times C}$, where $x_{t,v}\in {\mathbb{R}}^C$represents the feature vector of node v at time t and C is the number of features (a collection of variables, e.g., passenger flow, density, speed).

Given a spatiotemporal series of historical pedestrian flow signal matrices $X\in {\mathbb{R}}^{T_h\times N\times C}$ over the past $T_h$ time steps, the goal is to learn a mapping function f to predict the spatiotemporal series of future signal matrices $Y=\left(X_{\mathrm{t}+1},X_{\mathrm{t}+2},\cdots ,X_{\mathrm{t}+T_p}\right)\in {\mathbb{R}}^{T_p\times N\times C}$ over the next $T_p$ time steps. To accurately align with the long-term historical data and capture repeated patterns in passenger flow, such as peak hours, we aggregate recent-term and long-term historical passenger flow signal matrices. We divide the historical sequence $T_h$ into two time series segments of lengths $T_r$ and $T_l$ along the time axis as the input of the recent-term and long-term, respectively. As illustrated in Figure 1, the segment $X_{\mathit{r}}=\left(X_{\mathrm{t}-T_r+1},X_{\mathrm{t}-T_r+2},\cdots ,X_{\mathrm{t}}\right)\in {\mathbb{R}}^{T_r\times N\times C}$ represents the recent-term segment. Additionally, we select data from the past d days in extensive historical records that are closest to the recent-term segment and correspond to the same time slice as the long-term segment $X_{\mathit{l}}=\left(X_1,X_2,\cdots ,X_{T_l}\right)\in {\mathbb{R}}^{T_l\times N\times C}$, where $T_l$ is d times $T_p$. As a result, the passenger flow prediction problem can be formulated as:

$$\left[X_l,X_r;A\right]\stackrel{f_{\theta }\left(·\right)}{\to }Y$$

(1)

3.2. Overview

To address the multistep passenger flow prediction challenge, we propose a novel STGNN model to capture intricate spatiotemporal interactions in metro passenger flow prediction (MPFP) tasks. Figure 2 illustrates the overall pipeline of STGNN. The model mainly consists of four parts: long-term and recent-term spatial encoders, temporal encoders, and temporal decoders. The input of STGNN includes long-term passenger flow $X_l$, recent-term passenger flow $X_r$, and adjacent matrix $A$. $X_l$ and $X_r$ are separately input into the L-Spatial Encoder and R-Spatial Encoder to update the spatial features of nodes by dynamic global attention networks and graph convolution networks. The concatenated long-term and recent-term passenger flows are input into the temporal encoder of a stack of $L$ layers to update the temporal features. The recent-term passenger flow is input into the temporal decoder of a stack of $L^{\prime }$ layers, after passing through a series decomposition block. The fusion gate accumulates the overall trend parts extracted from input data and hidden variables for prediction. Finally, the output of the temporal decoder is sent to the regression layer to obtain the future passenger flow.

3.3. Spatial Modeling

In this section, we explain the architecture of the L-Spatial Encoder and the R-Spatial Encoder. GNNs are robust architectures well-suited for processing structured data, enabling the capture of complex and interrelated structural information. However, a reliance solely on GCN modules for capturing spatial features encounters limitations inherent to the characteristics of the input graph. Previous studies have highlighted the restricted expressive capacity of the message-passing paradigm. Although increasing the depth or breadth of GCNs can augment the receptive field, challenges such as optimization instability and information oversquashing persist [44,45].

To address these constraints, the global attention mechanism is embraced to extract spatial features not directly correlated with graph structure, thereby mitigating some of the limitations of GCNs. Additionally, it can be interpreted as a global message-passing mechanism facilitating the computation of pair-wise interactions between arbitrary node pairs. Consequently, the fusion of global attention and GCNs facilitates a comprehensive exploration of spatial features and captures implicit dependencies, such as sudden fluctuations in passenger flow at specific nodes or latent connections and long-range interactions between arbitrary node pairs. The resulting expression is depicted as follows:

$$X=\left(1-\alpha \right)\mathrm{D}\mathrm{G}\mathrm{A}\mathrm{N}\left(X^{\left(0\right)}\right)+\alpha \mathrm{G}\mathrm{C}\mathrm{N}\left(X^{\left(0\right)},A\right)$$

(2)

where $\alpha$ is a weight hyper-parameter, and $A$ represents the adjacency matrix.

3.3.1. Dynamic Global Attention Network

The correlation among nodes will likely change over time. The idea is to use a dynamic spatial attention mechanism to adaptively capture the dynamic spatial correlation strengths among nodes in the spatial dimension. The dynamic global attention is calculated as follows:

$$S=V_s·\sigma \left(\left(X^{\left(0\right)}{W}_1\right)·W_2·(W_3{X}^{\left(0\right)}{)}^T+b_s\right)$$

(3)

$$\mathrm{D}\mathrm{G}\mathrm{A}\mathrm{N}\left(X^{\left(0\right)}\right)=\mathrm{M}\mathrm{a}\mathrm{t}\mathrm{M}\mathrm{u}\mathrm{l}(\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left(S\right),X^{\left(0\right)})+X^{\left(0\right)}$$

(4)

where $X^{\left(0\right)}=\left(X_1,X_2,\cdots ,X_T\right)\in {\mathbb{R}}^{T\times N\times C}$,$S\in {\mathbb{R}}^{T\times N\times N}$, T is the length of the temporal dimension, $C$ is the number of features, $V_s,b_s\in {\mathbb{R}}^{N\times N}$, $W_1,W_2\in {\mathbb{R}}^{C\times C}$, and $W_3\in {\mathbb{R}}^{N\times N}$ are learnable parameters, and $\sigma$ is the activation function. A softmax function is used to dynamically compute the attention matrix. In Equation (3), the attention matrix adjusting the correlation between nodes is to capture the global influence from other nodes over time, while the residual connection preserves the information of the centered nodes.

3.3.2. Incorporation of Structural Information

In spectral graph analysis, studying the structural information of the graph usually refers to analyzing the corresponding Laplacian matrix and its eigenvalues. D denotes the diagonal degree matrix, and the Laplacian matrix of a graph and its normalization are defined as $L=D-A$ and $L^{sys}=D^{1/2}L{D}^{1/2}=I_N-D^{1/2}A{D}^{1/2}$, where $D_{ii}={\sum }_j^n{D}_{ij}$, and $I_N$ is the identity matrix. The eigen decomposition of normalized Laplacian is $L^{sys}=U\Lambda U^T$, where $U$ is the Fourier basis, and $\Lambda =diag[{\lambda }_1,{\lambda }_2,\cdots ,{\lambda }_n]$ is the diagonal matrix of eigenvalues. The GCN model constructs a filter to implement graph convolution operations in the domain of the Laplacian spectrum. The polynomial filtering operation for graph convolution can be formulated as:

$$g_{\theta }\ast x=g_{\theta }\left(L\right)x=U{g}_{\theta }\left(\Lambda \right)U^{T}x$$

(5)

where $g\ast x$ denotes a graph convolution and $g_{\theta }\left(\Lambda \right)$ denotes the spectral filter. The $\theta$ denotes the polynomial filter coefficients, and $g_{\theta }\left(\mathsf{\lambda }\right)={\sum }_{i=0}^K{\theta }_i{\lambda }^k$, $\lambda \in \left[\mathrm{0,2}\right]$. However, direct eigenvalue decomposition on a larger-scale Laplacian matrix is computationally expensive. The ChebNetII [46] model, a type of graph convolution neural network, is adopted here to incorporate the prior structural information of the input graph. The graph convolution therefore can be formulated as:

$$\mathrm{G}\mathrm{C}\mathrm{N}\left(X^{\left(0\right)},A\right)={{\displaystyle \frac{2}{K+1}}{\sum }_{k=0}^K{\sum }_{j=0}^K{w}_j{T}_k\left({\mathcal{z}}_j\right)T_k\left(\widehat{L}\right)f}_{\theta }\left(X^{\left(0\right)}\right)$$

(6)

where $\widehat{L}=2L^{sys}/{\lambda }_{max}-I_N$, ${\lambda }_{max}$ is the largest eigenvalue of $L^{sys}$, $K$ is a hyper-parameter, the Chebyshev nodes ${\mathcal{z}}_j$ for $T_k(·)$ is defined as $\mathrm{c}\mathrm{o}\mathrm{s}\left(\right(j+1/2)\pi /(K+1\left)\right)$, $w_j$, $j\in \left\{1,\cdots ,K\right\}$ is the learnable parameters, and $f_{\theta }$ is an MLP. The recursive definition of Chebyshev polynomial can be expressed as $T_k\left(\mathcal{z}\right)=2x{T}_{k-1}\left(\mathcal{z}\right)-T_{k-2}\left(\mathcal{z}\right)$, with $T_0\left(\mathcal{z}\right)=1$ and $T_1\left(\mathcal{z}\right)=x$.

3.4. Temporal Modeling

Metro passengers exhibit considerable stability in their travel behaviors, particularly concerning temporal characteristics, encompassing recent metro passenger flow trends, daily cyclic patterns, and weekly fluctuations. Building on time series decomposition methods and the strong performance of Transformer architectures in handling long-term dependencies, our temporal modeling framework integrates Transformer blocks with seasonal-trend decomposition blocks [47]. This integration aims to effectively address the intricate periodic patterns in metro passenger flow data by capturing both cyclic fluctuations and trend components concurrently. Given a series of length L and dimension D, denoted as $X\in {\mathbb{R}}^{L\times D}$, the process of series decomposition can be succinctly articulated as:

$$X_{trend}=\mathrm{A}\mathrm{v}\mathrm{g}\mathrm{P}\mathrm{o}\mathrm{o}\mathrm{l}\left(\mathrm{P}\mathrm{a}\mathrm{d}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}\left(X\right)\right)$$

(7)

$$X_s=X-X_{trend}$$

(8)

where ${X_{trend},X}_s\in {\mathbb{R}}^{L\times D}$, AvgPool($·$) represents the average pooling with padding. The input of the temporal encoder $X_{en}$ consists of the recent term $X_{en}^r$ and the long term $X_{en}^l$.

$$X_{en}=\left[X_{en}^l,X_{en}^r\right]$$

(9)

The input of the temporal decoder consists of the seasonal component $X_{des}$ and the trend component $X_{det}$. Each of them comprises two segments: the component decomposed from the recent term $X_{en}^r$ to convey recent information, and the other contains placeholders with length $T_p$ filled by a scalar value of zero and a mean of $X_{en}^r$. They can be expressed as:

$$X_{ens},X_{ent}=\mathrm{S}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{p}\left(X_{en}^r\right)$$

(10)

$$X_{des}=\left[X_{ens},X_0\right],X_{det}=\left[X_{ent},X_{Mean}\right]$$

(11)

3.4.1. Temporal Encoder

The encoder adopts a multilayer structure. In the l-th encoder, the overall equations can be summarized as: $X_{en}^l=$ Encoder ($X_{en}^{l-1}$), where $l\in \left\{1,\cdots ,L\right\}$. The details can be formalized as:

$$S_{en}^{l,1},\_=\mathrm{S}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{p}\left(\mathrm{L}\mathrm{S}\mathrm{A}\left(X_{en}^{l-1}\right)+X_{en}^{l-1}\right)$$

(12)

$$S_{en}^{l,2}=\mathrm{F}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{F}\mathrm{o}\mathrm{r}\mathrm{w}\mathrm{a}\mathrm{r}\mathrm{d}\left(S_{en}^{l,1}\right)+S_{en}^{l,1},X_{en}^l=S_{en}^{l,2}$$

(13)

where $S_{en}^{l,i}$, $i\in \left\{1,2\right\}$ denotes the seasonal component after the i-th decomposition block in the l-th layer, respectively. LSA is a locality-aware sparse attention operation. The feed-forward consists of two linear transformations with a ReLU activation in between, as in:

$$\mathrm{F}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{F}\mathrm{o}\mathrm{r}\mathrm{w}\mathrm{a}\mathrm{r}\mathrm{d}\left(S_{en}^{l,1}\right)={\mathrm{R}\mathrm{e}\mathrm{L}\mathrm{U}(W_{l,1}S}_{en}^{l,1}+b_{l,1})W_{l,2}+b_{l,2}$$

(14)

where $W_{l,i}$, $b_{l,i}$, $i\in \left\{1,2\right\}$, respectively represent the projector and bias in the i-th layer.

3.4.2. Temporal Decoder

The decoder also adopts a multilayer structure. In the l-th decoder, the overall equations can be summarized as: $X_{de}^l,{\mathcal{T}}_{de}^l=$ Decoder ($X_{de}^{l-1},{\mathcal{T}}_{de}^{l-1},K_{en},V_{en}$), where $l\in \left\{1,\cdots ,L^{\prime }\right\}$. The details can be formalized as:

$$K_{en}=X_{en}^L,\hspace{1em}\hspace{1em}\hspace{1em}{V}_{en}=X_{en}^L+{\sum }_{i=1}^L{V}_{en}^i$$

(15)

$$S_{de}^{l,1},{\mathcal{T}}_{de}^{l,1}=\mathrm{S}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{p}\left(\mathrm{L}\mathrm{S}\mathrm{A}\left(X_{de}^{l-1}\right)+X_{de}^{l-1}\right)$$

(16)

$$S_{de}^{l,2},{\mathcal{T}}_{de}^{l,2}=\mathrm{S}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{D}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{p}\left(\mathrm{C}\mathrm{F}\mathrm{A}\left(S_{en}^{l,1},K_{en},V_{en}\right)+S_{en}^{l,1}\right)$$

(17)

$$S_{de}^{l,3}=\mathrm{F}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{F}\mathrm{o}\mathrm{r}\mathrm{w}\mathrm{a}\mathrm{r}\mathrm{d}\left(S_{en}^{l,2}\right)+S_{en}^{l,2},\hspace{1em}\hspace{1em}\hspace{1em}{X}_{de}^l=S_{de}^{l,3}$$

(18)

$${\mathcal{T}}_{de}^l={\mathcal{T}}_{de}^{l-1}+W_{l,1}{\mathcal{T}}_{de}^{l,1}+W_{l,2}{\mathcal{T}}_{de}^{l,2}$$

(19)

where $S_{de}^{l,i}$, ${\mathcal{T}}_{de}^{l,j}$, $i\in \left\{1,2,3\right\}$,$j\in \left\{1,2\right\}$ are the seasonal and trend part component after the i-th decomposition block in the l-th layer. LSA is locality-aware sparse attention operation, and CFA is cross-full attention operation. $W_{l,j}$, $i\in \left\{1,2\right\}$ is the projector for the j-th trend component ${\mathcal{T}}_{de}^{l,j}$. The feed-forward also consists of two linear transformations with a ReLU activation in between. The final prediction output is the sum of the two refined decompositions after a linear transformation and can be expressed as:

$$Y=\mathrm{L}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{a}\mathrm{r}\left({X_{de}^{L^{\prime }}+\mathcal{T}}_{de}^{L^{\prime }}\right)$$

(20)

where $\mathrm{L}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{a}\mathrm{r}$ denotes a linear transformation.

3.5. Locality-Aware Sparse Attention

The multi-head self-attention mechanism is a specific implementation of the attention mechanism and a commonly adopted form due to its effectiveness in capturing long-term dependencies [29]. The basic operation in multi-head attention is scaled dot-product attention, defined as:

$$\mathrm{A}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\left(Q,K,V\right)=\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left({\displaystyle \frac{Q{K}^T}{\sqrt{D}}}\ast M\right)V$$

(21)

where $Q=K=V$, $Q,K,V$, and $D$ are queries, keys, values, and their dimensions, respectively. The mask matrix $M$ is an optional term to avoid future information leakage.

Metro passenger flow dynamics are also susceptible to perturbations induced by unforeseen events, such as extreme weather occurrences or holidays. Consequently, the variations in flow patterns at the time of observation are closely linked with the local contextual cues. Nevertheless, conventional self-attention mechanisms, tailored for discrete tokens, compute similarities between queries and keys solely based on their point-wise values, disregarding the local contextual nuances. This limitation challenges the effective application of such mechanisms to sequence transformations in passenger flow analysis, potentially causing query-key mismatches and optimization difficulties. [48] endeavored to mitigate this discrepancy by leveraging causal convolution [49]. However, the adequacy of extracting local context information through fixed window convolutions remains questionable. Concurrently, the computational complexity of self-attention is O($L^2$), making it infeasible to directly model long-term series. To alleviate these two problems, we design a locality-aware sparse attention block (LSA), as illustrated in Figure 3. In the Mixture of Causal Conv, a set of causal convolution kernels of different sizes are employed to extract and fuse multiple local context information, transforming the input into queries and keys, and combining them using a set of data-dependent weights. The details can be expressed as:

$$Q=\mathrm{S}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{L}\left(X\right)\right)\ast F_Q\left(X\right)$$

(22)

$$K=\mathrm{S}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{L}\left(X\right)\right)\ast F_K\left(X\right)$$

(23)

where $F_Q$, $F_K$, are two different sets of causal convolution kernel, and L is a fully connected layer. $\mathrm{S}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{L}\left(X\right)\right)$ is the weight for combing. Then, the max-mean measurement [30] method is used as a select function to select top u dominant queries. The details can be expressed as:

$$\overline{Q}=\mathrm{S}\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\left(Q\right)$$

(24)

$$\mathrm{L}\mathrm{S}\mathrm{A}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\left(\overline{Q},K,XW\right)=\oplus \left({\mathrm{L}\mathrm{S}\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}}_1,\cdots ,{\mathrm{L}\mathrm{S}\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}}_{\mathrm{h}}\right)W^O$$

(25)

$$\mathrm{L}\mathrm{S}{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}}_{\mathrm{i}}=\mathrm{A}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\left({\overline{Q}}_i,K_i,X{W}_i\right)$$

(26)

where Select () is the operation of max-mean measurement, and Attention is the operation of scaled dot-product attention, and $W$ is the projector.

3.6. Embedding

The enduring constancy of static node features underscores the need to incorporate spatial heterogeneity while preserving the structural integrity of graphs. To address this, we introduce an augmented node embedding matrix, denoted as $\mathrm{N}\mathrm{E}\in {\mathbb{R}}^{N\times C}$, where each node is associated with an additional embedding vector. We also leverage fixed position embeddings, denoted as $\mathrm{P}\mathrm{E}(t,d)$, to provide the model with order-awareness, where $t$ signifies the position of the input element and d denotes the vector dimension. Furthermore, to capture hierarchical temporal granularities (e.g., minutes, hours, and weeks), we integrate learnable temporal stamp embeddings (e.g., MLP), denoted as $\mathrm{S}\mathrm{E}\left(t\right)$, corresponding to each hierarchical level. The amalgamation of these components yields the final temporal position embedding $\mathrm{T}\mathrm{E}\left(t\right)$ for an input element positioned at t, encapsulated by the expression:

$$\mathrm{T}\mathrm{E}\left(t\right)=\mathrm{P}\mathrm{E}\left(t,\right)+\mathrm{S}\mathrm{E}\left(t\right)$$

(27)

$$\mathrm{P}\mathrm{E}\left(t,2d\right)=\sin \left(t/{1000}^{2d/D}\right)$$

(28)

$$\mathrm{P}\mathrm{E}\left(t,2d+1\right)=\cos \left(t/{1000}^{2d/D}\right)$$

(29)

where t is the relative index of each symbol in the sequence and $1\le d\le D$.

3.7. Loss Function

In the training stage, to minimize the error between the ground truth and the predicted value, the loss function is defined as:

$$\mathcal{L}\left(Y,\widehat{Y}\right)={\displaystyle \frac{1}{\left|T\right|\times \left|\mathcal{V}\right|}}{\sum }_{\tau \in T}{\sum }_{v\in \mathcal{V}}‖{\mathcal{Y}}^{\left(\tau ,v\right)}-{\widehat{\mathcal{Y}}}^{\left(\tau ,v\right)}‖+{\lambda L}_{reg}$$

(30)

where the first term is used to calculate loss and the $L_{reg}$ is a regularization term; $\lambda$ is a hyperparameter; $Y$ and $\widehat{Y}$ are denoted as the ground truth and the predicted value. All model parameters are updated by minimizing the loss function through the mini-batch gradient descent algorithm in the training process.

4. Experiments

4.1. Data Preparation

4.1.1. AnyLogic Simulation in Metro Station

In accordance with the architectural 3D CAD model, we identified 13 Origin-Destination (OD) regions within the metro station. As delineated by circular markers in Figure 4a, we first identified 13 OD regions within the two-level metro station, each of which corresponds to pertinent functional facilities (such as escalators, exits, and security checkpoints) or commercial facilities (including ticket and vending machines), and these regions are areas where passenger flow converges and disperses. This deliberate selection serves to abstract the intricate movements of passengers within the metro station, conceptualizing their transit between these 13 OD pairs, thereby constituting a graph with 13 nodes. As shown in Figure 4, if there is an edge between any two nodes, it indicates a transition of passenger flow between these two nodes. In AnyLogic-professional-8.7.11 simulation, sensors positioned within the aforementioned 13 OD regions meticulously captured variations in passenger flows. These observations encompassed movements within functional facilities as well as within commercial facilities. Data collection was conducted comprehensively from the operational hours of 6:00 a.m. to 11:00 p.m., facilitating an extensive examination of passenger dynamics within the station.

4.1.2. Hangzhou Metro System

Each piece of the AFC record in Hangzhou data that will be used in the in-flow and out-flow data generation process is summarized in Figure 4b. The data is divided into specific periods according to the AFC field to aggregate inflow and outflow. Then, we set a time interval window to count the number of inbounds and outbounds of each metro station to obtain the MSP inflow and the MSP outflow.

In this study, two distinct passenger flow datasets are used, whose detailed characteristics are outlined in

Table 1. Dataset description.

Datasets	Nodes	Time Span	Daily Range
Ours	13	1 January–21 January	6:00–23:00
Hangzhou	81	1 January 2019–26 January 2019	6:00–23:30

. The first dataset comprises simulated data obtained from 13 sensors deployed within an AnyLogic simulated metro station. This dataset encompasses real-time passenger flow recordings aggregated approximately every 5 min over 21 days, from 06:00 to 23:00, about eighty-seven thousand data points. The second dataset is from the Hangzhou metro system (https://tianchi.aliyun.com/competition/entrance/231708/information (accessed on 30 October 2023)), comprising 81 metro stations and an aggregate of 70 million data points collected from three metro lines in Hangzhou, China, spanning from 1 January to 26 January 2019. To facilitate passenger flow prediction, the raw data has been processed, aggregating inflow and outflow information into discrete 5-min intervals, thereby enhancing temporal granularity, from 06:00 to 23:30.

4.2. Setting

All datasets are split at a ratio of 7:1:2 into training sets, validation sets, and test sets by the time and normalized all data into the range [−1, 1] with the Min-Max method. The STGNN model uses the PyTorch framework and NVIDIA GPU (GeForce RTX 3090). We train our model using Adam optimizer [50] with a learning rate decay strategy. We set an early stop of 10 to save the best model as the model checkpoint. The initial learning rate and batch size are set as 0.001 and 32, respectively. The model dimensions C and D are 512, and the number of attention heads h is 8. For our dataset, $L$ and $L^{\prime }$ are 2 and 1, respectively. For the Hangzhou dataset, $L$ and $L^{\prime }$ are 3 and 1, respectively. For evaluation, we re-transform the predicted values back to the ground truth and employ two widely adopted metrics, namely Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), to evaluate the prediction performance of our model and baseline models.

4.3. Baseline Methods

The proposed STGNN model is compared with two types of methods: non-graph methods and graph-based methods. For the non-graph methods, the chosen baseline models are (1) LSTM [51]: Long Short-Term Memory network, a special RNN model. (2) Reformer [31]: An effective transformer replaces dot-product attention with one that uses locality-sensitive hashing for long sequence modeling. (3) Autoformer [47]: A novel decomposition architecture uses an auto-correlation mechanism based on the series periodicity to conduct the dependencies discovery and representation aggregation at the sub-series level. (4) TimesNet [52]: A temporal 2D variation model extends the analysis of temporal variations into 2D space by transforming the 1D time series into a set of 2D tensors based on multiple periods.

As for the graph-based methods, the chosen baseline models are (5) GCN [53]: A graph convolution network is used to capture spatial dependence. (6) STG-NODE [54]: A spatial-temporal graph network based on a neural-controlled differential equation extends the concept of a neural-controlled differential equation and designs two neural-controlled differential equations for temporal processing and spatial processing. (7) T-GCN [25]: A temporal graph convolution network combines graph convolution and gated recurrent units to capture spatial dependence and temporal dependence. (8) ASTGCN [41]: a spatial-temporal convolutional network based on an attention mechanism uses temporal and spatial attention to capture spatial-temporal dependencies. (9) ASTGNN [55]: a spatial-temporal graph network is based on attention mechanisms, graph convolution networks, learning dynamics, and the heterogeneity of spatial-temporal graph data.

4.4. Performance Evaluation

4.4.1. Comparison with Baseline Methods

The proposed STGNN model is compared with various baseline methods for predicting 10, 20, 30, and 60 steps on a simulated metro flow dataset. The results in

Table 2. Average performance comparison for our dataset.

Models	Prediction Step = 10		Prediction Step = 20		Prediction Step = 30		Prediction Step = 60
	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
LSTM	6.40	3.68	6.65	3.81	7.43	4.10	9.62	5.39
Reformer	6.80	3.25	7.38	3.43	8.68	3.98	9.27	4.56
Autoformer	6.45	2.85	6.64	2.98	7.55	3.58	9.60	4.77
TimesNet	5.41	2.38	6.02	2.61	6.46	2.98	7.41	3.39
GCN	9.33	5.04	10.11	6.02	10.59	6.18	11.17	7.55
T-GCN	5.85	2.76	6.54	3.14	7.30	3.35	8.72	4.22
STG-NCDE	7.39	3.94	8.30	3.89	9.93	4.66	10.81	5.89
ASTGCN	5.14	2.19	5.81	2.61	6.92	2.69	7.71	3.42
ASTGNN	4.89	2.01	5.55	2.46	6.71	2.47	7.58	3.29
STGNN (ours)	4.58	1.91	5.07	2.23	5.67	2.39	7.17	3.08

substantiate the superior efficacy of the proposed STGNN model across all evaluated metrics.

Among the non-graph methods, LSTM exhibits the least favorable performance, primarily attributed to its inability to effectively capture the recurrent and daily patterns inherent in urban commuter behavior, as well as the broader fluctuations within metro flow dynamics. Autoformer, while slightly outperforming LSTM, struggles to capture the subtle long-term patterns in metro flow variations, indicating limitations in its autocorrelation mechanism. Conversely, the Reformer leveraging attention mechanism, demonstrates superior performance over other non-graph methods in shorter prediction intervals (i.e., 10 and 20 steps). Notably, TimesNet, which scrutinizes complex temporal metro flow patterns from a two-dimensional space, excels over non-graph methods in longer prediction horizons (i.e., 30 and 60 steps).

Turning to the graph-based methods, GCN fares relatively poorly owing to its exclusive focus on static spatial features, overlooking the temporal evolution of metro flow distribution. In contrast, T-GCN incorporates temporal dynamics and employs gated recurrent units (GRUs) to capture temporal dependencies, thereby exhibiting notable enhancements in short-term prediction accuracy. STG-NCDE, integrating spatiotemporal controlled differential equations, demonstrates commendable performance in analyzing traffic flow data, albeit its effectiveness wanes in capturing prolonged temporal dependencies within the metro flow. ASTGCN and ASTGNN excel in capturing spatial-temporal dependencies, with ASTGNN surpassing all methods in short-term predictions (10 and 20 steps). Notably, STGNN outperforms ASTGNN in 30 and 60 steps, underscoring its capability to enhance long-term prediction accuracy. Specifically, STGNN exhibits improvements over ASTGNN by 15.4% and 5.4% in terms of RMSE and by 0.6% and 0.3% in MAE for intervals of 30 and 60 steps, respectively.

Table 3. Average performance comparison over 10 and 20 steps for the Hangzhou dataset.

Models	Prediction Step = 10				Prediction Step = 20
	Inflow		Outflow		Inflow		Outflow
	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
LSTM	29.13	19.15	38.93	23.74	32.92	21.29	41.42	25.17
Reformer	22.47	13.27	30.83	18.01	24.43	14.01	31.29	18.47
Autoformer	24.59	15.41	32.65	20.68	32.44	19.95	39.44	24.30
TimesNet	23.94	14.03	32.10	17.95	25.36	14.91	33.81	18.91
GCN	67.82	38.51	74.44	41.10	68.60	39.80	75.22	42.91
T-GCN	27.96	17.08	35.94	22.79	30.53	18.45	38.62	24.04
STG-NCDE	24.20	13.57	31.61	18.35	30.93	16.86	42.21	23.37
ASTGCN	23.01	14.36	32.99	19.86	25.03	14.92	34.33	21.88
ASTGNN	20.61	12.30	28.63	17.12	22.36	13.44	29.54	17.86
STGNN (ours)	18.90	11.58	27.02	16.90	20.10	12.01	27.51	17.05

and

Table 4. Average performance comparison over 30 and 60 steps for the Hangzhou dataset.

Models	Prediction Step = 30				Prediction Step = 60
	Inflow		Outflow		Inflow		Outflow
	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
LSTM	34.41	22.38	43.47	26.94	37.20	24.25	46.42	29.39
Reformer	25.41	14.40	31.83	18.83	27.26	15.86	34.42	20.55
Autoformer	38.75	22.79	41.56	25.59	54.38	29.14	57.25	32.14
TimesNet	26.47	15.18	34.95	19.34	28.62	16.73	36.33	20.66
GCN	69.20	40.50	76.46	43.34	73.83	44.84	80.11	46.76
T-GCN	32.96	20.08	40.79	25.99	35.33	23.45	44.32	28.34
STG-NCDE	38.95	20.45	51.39	27.40	41.81	22.03	54.53	30.10
ASTGCN	30.34	17.99	36.80	22.10	32.87	19.96	39.33	23.28
ASTGNN	23.97	13.92	30.43	18.32	25.59	14.95	32.92	19.96
STGNN (ours)	21.44	12.65	27.81	17.12	23.40	13.78	28.93	18.35

illustrate the average prediction performance over 10, 20, 30, and 60 steps ahead on the Hangzhou metro flow dataset. The findings consistently affirm the superior performance of the STGNN model across all evaluated metrics. Notably, among the non-graph methods, Reformer outperforms TimesNet, suggesting that the efficacy of analyzing complex spatial-temporal patterns within metro flow data in a two-dimensional space diminishes with an expanded node count.

Regarding the graph-based methods, ASTGNN maintains its dominance over all other baseline methods. Specifically, concerning inflow and outflow predictions in the Hangzhou dataset, our STGNN model demonstrates notable performance over ASTGNN. Precisely, in the context of 30 step predictions, STGNN outperforms ASTGNN by 10.5% and 8.6% in terms of RMSE and by 9.1% and 6.5% in terms of MAE for inflow and outflow, respectively. Moreover, for 60-step predictions, STGNN exhibits enhancements over ASTGNN by 8.5% and 12.1% in terms of RMSE and by 7.8% and 8.0% in terms of MAE for inflow and outflow, respectively.

4.4.2. Comparison with Ground Truth

In Figure 5, we visualize the comparison in 10 steps predicted by STGNN and the ground truth values of two representative metro stations for the Hangzhou dataset and one node for the simulation dataset. From the figures, we can observe that the passenger flow predicted by STGNN is close to the ground truth values.

4.5. Effect of Different Network Configurations

A series of experiments are conducted to explore the impact of key hyper-parameter settings on model performance, employing different network configurations. All models adhere to uniform settings except for the studied variable. Figure 6 illustrates the results obtained with varying model dimensions and attention head numbers across all datasets, with the prediction step set to 30. In comparison to attention heads, the STGNN model is more sensitive to changes in dimensionality. Across all datasets, an augmentation in model dimensions correlates with a discernible reduction in RMSE and MAE. Notably, this trend is particularly pronounced in the Hangzhou dataset, where more substantial improvements are discernible. In contrast, it is noteworthy that an increase in the number of attention heads does not consistently yield improved prediction performance. Optimal results are attained when employing eight attention heads.

4.6. Ablation Experiments

To further analyze the effectiveness of the key components in STGNN, we conduct ablation experiments to evaluate three variants of our model on two datasets:

STGNN-noSM: It removes the total spatial modeling module to investigate the benefits of modeling the spatial dependencies of the passenger flow network.

STGNN-noDAG: It removes the dynamic global attention network to investigate the utility of dynamic spatial correlation strength impact between global node pairs rather than relying solely on static topological relationships.

STGNN-noLSA: It replaces the locality-aware sparse attention block with a traditional attention block to investigate the usefulness of considering various contextual information.

Except for the variances, all the variant models share identical settings. Figure 7 shows the performance of the three variants on our simulation dataset and the Hangzhou dataset. We can easily observe that the changes in RMSE and MAE are almost consistent across different datasets. By comparing STGNN-noSM and STGNN, no doubt that modeling the spatial dependences plays a positive role in multi-step metro station passenger flow prediction and verifies the effectiveness of the spatial modeling module. STGNN-noDAG, which only uses GCN for spatial modeling, yields inferior performance compared to STGNN but is slightly better than STGNN-noSM. This result indicates the significance of the dynamic global spatial relationship between global node pairs. Moreover, the superior performance of STGNN compared to STGNN-noLSA demonstrates the effectiveness of modeling local trends by employing multi-head self-attention for long-term passenger flow forecasting.

4.7. Computation Cost Study

For the ASTGCN, ASTGNN, and STGNN models, the computational complexity of attention operation is O($N^2·C$) in the spatial dimension. In the temporal dimension, the attention operation complexity for ASTGCN and ASTGNN is O($L^2·D$), while for STGNN, it is O(LlogL$·D$).

Table 5. The computation time for the training epoch.

Models	Computation Time for Training Epoch (s)
	Prediction Step = 30	Prediction Step = 60
STG-NODE	119	209
ASTGCN	11.49	27.81
ASTGNN	18.33	33.31
STGNN	8.59	13.28

shows the recorded inference time for one epoch during the training stage for the four spatial-temporal models in two different prediction steps. STG-NODE has a relatively long computation time because it uses ordinary differential equations for spatial-temporal modeling. STGNN utilizes sparse attention and a generative decoder, resulting in significantly faster inference time than ASTGCN and ASTGNN.

5. Discussion and Conclusions

This study presents a finely resolved subway station model using Anylogic passenger simulation. Additionally, we propose a novel spatial-temporal graph neural network named STGNN tailored for passenger flow prediction tasks in metro stations. Based on this, new modules for spatial and temporal dimensions are redesigned to capture respective spatial and temporal dependencies. Specifically, we devise a dynamic global attention network to discern and adaptively capture the dynamic global influence along the spatial dimension, thereby facilitating the exploration of passenger flow fluctuation between global node pairs. The GCN integrated into the spatial modeling module incorporates pertinent prior information derived from the input graph structure. Furthermore, we introduce specialized modules such as the series decomposition block and locality-aware sparse attention block designed for time series prediction. The series decomposition block decomposes the passenger flow series into seasonal and trend-cyclical components, helping the model more accurately capture the seasonality and progression of the passenger flow series. The locality-aware sparse attention block aims to extract multiple local contexts of information and alleviate the computational complexity associated with long sequence modeling, contributing to more accurate passenger flow predictions and reduced inference time. Through comparative experiments and ablation experiments on the simulation dataset and Hangzhou dataset, we demonstrate the effectiveness and robustness of our proposed approach.

Nevertheless, the STGNN model has limitations: (1) The intricate patterns of fluctuation and the spatial-temporal relationships inherent in metro passenger flow remain subject to the influence of numerous unknown external events, such as weather conditions. Our model can only implicitly learn the impact of these unknown external events on passenger flow evolutions and cannot accurately assess the influence of any specific external factor. (2) Our model analyzes passenger flow variations by aggregating long-term and recent-term passenger flow segments, where the long-term segments only include daily-period passenger flow while ignoring weekly-period passenger flow variations. In future work, we will enhance the applicability and generalization capability of STGNN from the following aspects: (1) We plan to adjust the components of the STGNN to generalize it to other prediction tasks. (2) We plan to construct the learning of spatial correlations of passenger flow between metro stations from a multi-graph perspective. (3) Future research could incorporate multi-source data, such as weather forecasts, holidays, and metro schedules, to enhance the model’s robustness. These will help optimize metro schedules, enhance passenger experience, and support data-driven decision-making in infrastructure development.