Because traditional station passenger flow prediction often focuses on predicting the total amount of passenger flow entering and leaving a certain station, it is difficult to distinguish the origin and destination (OD) of different passengers, and its guiding significance for urban rail transit transportation planning is limited [1]. Therefore, accurate prediction of origin and destination (OD) passenger flow is becoming a hot research topic for relevant scholars. The main methods used by researchers to predict urban rail OD passenger flow can be divided into traditional methods, shallow machine learning methods, and deep learning methods. The more commonly used statistical origin-destination (OD) prediction methods in the early period are maximum entropy [2; 3; 4], maximum likelihood [5], least squares [6], and Bayesian inference [7]. Notably, the Kalman filter method stood out. [PERSON] et al. [8] first applied the Kalman filter method to the dynamic OD passenger flow prediction. Subsequently, time series models were also applied in OD prediction. [PERSON] et al. [9] used an autoregressive model to process smart transport card data in London. For the predictive machine learning of OD passenger flow in London's transit system, using a probability tree model, [PERSON] et al. [10] learned from the historical OD information and obtained the probability of occurrence of each OD pair to predict destinations based on real-time starting point data and predict passenger flow in the metro network by accumulating all contributions. With the introduction of deep learning models into passenger flow prediction tasks, their neural network structure can better capture the spatiotemporal characteristics and variation patterns of data, thus achieving significant advantages in OD passenger flow prediction tasks [11]. In terms of a single model, [PERSON] et al. [12] first used the GRU model to predict OD passenger flow in UTR passenger flow prediction. The results show that the GRU model is better than autoregressive synthetic moving average models. [PERSON] et al. [13] reviewed the research status and application effects of a long short-term memory network (LSTM) on traffic flow prediction and summarized the optimization methods of the model in OD flow prediction. [PERSON] et al. [14] proposed a deep learning-based Generative Adversarial Network (GAN) for a more accurate prediction of short-term passenger flow in rail transit with higher efficiency and lower memory usage. [PERSON] et al. [15] proposed an adaptive heterogeneous spatiotemporal graph convolution predictor to predict the amount of passenger data for short-term inter-station travel and dynamically adapt the spatiotemporal characteristics of OD traffic through a graph convolution neural network. For the combination of multiple models, [PERSON] et al. [16] modeled the spatial dependency of multiple stations in pairs and combined the Hypergraph Attention Recurrent Network (HGARN) and the Hawkes attention mechanism to simulate the impact of events and capture the key moments that affect passenger flow. [PERSON] et al. [17] proposed an AEST model to improve OD prediction by solving the data coefficient problem of population flow in different areas of the city. Combining CNN/GCN and LSTM to effectively capture spatiotemporal correlations. [PERSON] et al. [18] built a graph depth learning model (ST-GDL) for long-term OD prediction in view of the complex and dynamic spatiotemporal correlations of traffic information and also combined the extracted spatiotemporal features with meteorological information features to obtain more accurate prediction results. [PERSON] et al. [19] combined a convolutional neural network (CNN) and a convolutional long short-term memory network (Conv-LSTM) to build an asymmetric spatiotemporal network (NTSN), which can predict bus OD by extracting spatiotemporal features, thus improving the generalization ability of NSTN. In addition to the integration of models, the integration with attention mechanism is also a scheme to predict OD passenger flow.