## 2 Related Work  
### Deep Learning Methods for Short-Term OD Prediction in Urban Rail Transit  
As an emerging technology, deep learning has been widely used in various fields with its powerful feature extraction and expression ability. In recent years, several deep learning methods have emerged for short-term OD prediction in URT [8; 17; 18; 19; 20; 21; 22]. Based on the multi-source spatio-temporal data, [PERSON]. [8] proposed a spatio-temporal long short-term memory network by improving the architecture of LSTM to improve prediction accuracy. The experimental results demonstrated that STLSTM can effectively exploit the potential information of multisource data and improve the prediction effect. Moreover, a channel-wise attentive split-convolutional neural network (termed as CASCNN) [17] was proposed to solve the data availability and data sparsity issues by using an inflow/outflow-gated mechanism and a masked loss function. Ref. [18] proposed a multi-resolution spatio-temporal neural network (termed as MRSTN) for real-time OD prediction, where the multi-resolution spatial feature extraction modules were first used to capture the local spatial dependencies, and ConvLSTMs were used to capture the temporal evolution of demand. The experimental results demonstrated that MRSTN outperformed the advanced deep learning methods (e.g., STResNet [6]). Ref. [19] proposed a completion augmentation-based self-attention temporal convolutional network (termed as CA-SATCN) by addressing the recent destination distribution availability, augmenting the flow presentation for each station, and digging out the global spatial dependency to improve the short-term OD prediction accuracy. Ref. [20] proposed temporal Pearson correlation coefficients, approximate entropy, and spatial correlations as indicators to reflect the inherent spatio-temporal correlations and complexity of the OD flow. Ref. [21] proposed a multi-fused residual network (termed as MF-ResNet) to capture multiple complex dependencies, in which the convolution-based residual network units model the temporal closeness, mid-term periodicity, as well as long-term periodicity features, and the fully connected layers capture external factors. Ref. [22] proposed a spatio-temporal convolutional neural network (termed as STCNN) by using OD pair importance calculation, lagged spatio-temporal relationship construction, lagged spatio-temporal learning, real-time information learning, and sequential-temporal learning blocks. The experimental results demonstrated that the STCNN outperforms the advanced methods with significant superiority on critical OD pairs.  
It can be seen that there are few deep learning methods that introduce channel-spatial attention into the field of short-term OD prediction in URT. The first contribution of this paper is introducing the proposed OAM, a variant of the channel-spatial attention module, into the field of short-term OD prediction in URT. In addition, for the existing deep learning methods (e.g., CASCNN [17], CA-SATCN [19]) with several inputs, there is a problem of low feature utilization between two adjacent network branches. Thus, the second contribution of this paper is proposing a feature interaction to further improve the feature utilization.