# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 1 Introduction
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**Reference** & **Description** \\ \hline [PERSON] et al. [7] & The HA model forecasted Lisbon Airport’s passenger numbers to determine the demand in a new airport using non-causal methods. \\ [PERSON] et al. [8] & This paper proposed a mutli-pattern deep fusion approach which used the AP algorithm for clustering analysis and fused the output of the multi-pattern DBNs to obtain the final prediction results. \\ [PERSON] et al. [9] & The EMD-BPN model decomposed the short-term passenger flow series data into a number of intrinsic mode function (IMF) components as inputs to the BPN and then applied BPN to passenger flow prediction. \\ [PERSON] et al. [10] & The paper incorporated weather variables, personal travel history, and network features into the GBDT algorithm. \\ [PERSON] et al. [11] & The multilayer perceptron (MLP)-based passenger flow prediction model was developed to predict the passenger flow at key stations. \\ [PERSON] et al. [12] & Dynamic-GRCNN model integrated the relationship of passengers and GCN and LSTM units to learn complex traffic spatial-temporal features. \\ [PERSON] et al. [13] & The TCN-LSTM model solved the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. \\ [PERSON] et al. [14] & Graph-based DCRNN integrated graph features into the RNN to capture the spatiotemporal dependencies in the bus network. The diffusion convolution recurrent neural network (DCRNN) architecture was adopted to forecast the future number of passengers on each bus line. \\ [PERSON] et al. [15] & The MFGCN model included the GCN with a spatial-attention mechanism and the LSTM with a temporal-attention mechanism to extract higher-order spatial-temporal interaction characteristics. \\ [PERSON] et al. [16] & The GRU model can selectively retain and update important information about past time steps due to its gating mechanism of information flow within the network. \\ [PERSON] et al. [17] & Authors applied convolutional operations to extract spatial and temporal features from the passenger flow time series. By stacking multiple convolutional layers, the CNN can capture complex and hierarchical representations of the input sequence. \\ [PERSON] et al. [18] & The GCN presented a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. \\ [PERSON] et al. [19] & The ARConv-GCN model combined GCN and the attention residual 3 DCNN for more accurate short-term passenger flow prediction in rail transit. \\ [PERSON] et al. [20] & This paper proposed a parallel-structured deep learning model that consists of a GCN and a stacked BiLSTM for predicting passenger inflow. \\ [PERSON] et al. [21] & Authors introduced GCN to capture the geographical connectivity relationships between sites on the basis of GRU. \\ [PERSON] et al. [22] & The PVCGN is a Seq2 Seq model including a graph convolution gated recurrent unit (GC-GRU) for spatial-temporal representation learning and a fully connected gated recurrent unit (FC-GRU) for capturing the global evolution tendency. \\ [PERSON] et al. [23] & This paper incorporated the convolution operation between LSTM cells and predicted passenger flow for three different types of stations: terminal station, transfer station, and regular station. \\ [PERSON] et al. [24] & This paper proposed a novel two-step strategy (Transformer-K-Means)-(ResNet-GCN-AttLSTM), which includes classification block based on the K-Means, and prediction block, which includes ResNet, GCN, and AttLSTM. \\ [PERSON] et al. [25] & The ST-ED-RMGC model was proposed, which is an encoder-decoder framework used to study the OD-based ride-sourcing demand prediction problem. \\ [PERSON] et al. [26] & The Conv-GCN model is a fusion of multi-graph GCN and 3D CNN, allowing it to effectively capture high-level spatiotemporal features among different patterns of passenger flow. \\ [PERSON] et al. [1] & The predicted passenger flow results can serve as a key solution to alleviate the operational pressure brought about by increasing demand. \\ [PERSON] et al. [2] & Passenger flow prediction can provide reference for optimizing traffic operations in disturbed environments and ensuring a secure and comfortable travel experience for passengers. \\ [PERSON] et al. [3] & Passenger flow prediction can also be used to respond to emergency situations or traffic interruptions. \\ [PERSON] et al. [4] & Passenger flow prediction results can help to rearrange subway schedules and reduce delays. \\ [PERSON] et al. [5] & Accurate passenger flow prediction can contribute to limiting train delays, especially in a bottleneck area of railway traffic management. \\ [PERSON] et al. [6] & The results of passenger flow prediction are helpful for train scheduling in bottleneck sections and reducing train energy consumption. \\ \hline \hline \end{tabular}
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