[PERSON] et al. [20] proposed the PSAM-CNN model for the oversaturation of passengers in OD passenger flow prediction. The traditional CNN model combined with the attention mechanism of PSAM can accurately predict OD passenger flow when the urban subway is oversaturated. [PERSON] et al. [21] designed an adaptive graph convolution model based on self-attention to predict the future traffic demand between each pair of OD regions, taking the Encoder-Decoder structure as the historical input encoding as the hidden state and decoding it as the future prediction. At the same time, the combination with the self-attention mechanism can also capture the time dependence and improve the accuracy of the prediction. [PERSON] et al. [22] designed an ST-MEN framework for dynamic spatial feature extraction to effectively capture the spatial dependency between OD passenger flow nodes, and they incorporated an external attention mechanism to effectively process external factors into the prediction process, thus achieving excellent prediction accuracy. [PERSON] et al. [23] constructed OD passenger flow stations as a matrix metro extension map and developed an SARGCN model integrating GCN, LSTM, and attention mechanisms to explore the spatiotemporal correlation between passenger inflow and outflow, and the model has excellent performance. Table 1 summarizes different passenger flow forecasting methods and the models used. In summary, current research on OD (origin-destination) passenger flow prediction is limited to data flow between stations, with a lack of focus on interval OD passenger flow prediction. Additionally, single models struggle to comprehensively capture the spatiotemporal features of interval OD passenger flows, while model integration requires considering the compatibility between models. To address these issues, our specific contributions are as follows:  
1. A prediction model for interval OD passenger flow is proposed based on a core combination of TCN and LSTM, incorporating an attention mechanism to adaptively handle the data. The core components of this model can capture both the spatiotemporal features and long-term dependencies of the OD passenger flow data. The attention mechanism assigns weights to different time steps, helping the model focus on criti \begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Method** & **Reference** & **Specific Form** \\ \hline \multirow{8}{*}{classical statistics} & [PERSON]. 1984 [2] & Maximum entropy \\  & [PERSON]; [PERSON] 1991 [3] & Maximum likelihood \\  & [PERSON] 1997 [4] & Maximum likelihood \\  & [PERSON]; [PERSON] 1988 [5] & Maximum likelihood \\  & [PERSON] 1984 [6] & Least squares \\  & [PERSON]; [PERSON] 1998 [7] & Bayesian inference \\  & [PERSON]; [PERSON]; [PERSON] 2001 [8] & Kalman filter \\  & [PERSON]; [PERSON]; [PERSON] 2015 [9] & Autoregressive model \\  & [PERSON]; [PERSON]; [PERSON] 2013 [10] & Probability tree model \\ \hline \multirow{4}{*}{deep learning (single model)} & [PERSON]; [PERSON]; [PERSON] 2016 [12] & GRU \\  & [PERSON]; [PERSON]. 2025 [13] & LSTM \\  & [PERSON]; [PERSON]; et al. 2023 [14] & GAN \\  & [PERSON]; [PERSON]; [PERSON]; [PERSON]. 2024 [15] & GCN \\ \hline \multirow{4}{*}{deep learning (combined model)} & [PERSON]; [PERSON]; [PERSON] et al. 2024 [16] & H\&ARN \\  & [PERSON]; [PERSON]; [PERSON]; et al. 2022 [17] & CNN\(\backslash\)GCN+LSTM \\  & [PERSON]; [PERSON]; et al. 2022 [18] & ST\(-\)GDL \\  & [PERSON]; [PERSON]; [PERSON]; [PERSON]. 2022 [19] & CNN+Conv\(-\)LSTM \\ \hline \multirow{4}{*}{deep learning (with attention)} & [PERSON]; [PERSON]; [PERSON]; [PERSON]. 2024 [20] & PSAM\(-\)CNN+Attention \\  & [PERSON]; [PERSON]; [PERSON]; [PERSON]. 2023 [21] & GNN+Encoder–Decoder+Attention \\ \cline{1-1}  & [PERSON]; [PERSON]; [PERSON]. 2024 [22] & ST\(-\)MEN+Attention \\ \cline{1-1}  & [PERSON]; [PERSON]. 2023 [23] & GCN+LSTM+Attention \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of OD passenger flow related work.