## 2 Literature Review
### Prediction Model Selection Strategy  
Deep learning models can not only effectively fit complex nonlinear relationships, but also avoid overfitting problems in shallow structures [31]. In recent years, many researchers have applied deep learning models to time series prediction problems in the transportation field and achieved fruitful results. For example, for traffic flow prediction, [PERSON] introduced a spatiotemporal dynamic model (ST) to integrate the spatial correlation and temporal evolution of traffic flow to capture the dynamic interactions of different nodes in a road network [32]; [PERSON] combined basic graph (FD), Markov, and LSTM models to achieve the progressive prediction of traffic flow [33]; and [PERSON] designed CASAformer for low-speed congestion scenarios for traffic speed prediction, using a sparse attention mechanism to improve the prediction accuracy [34]. These achievements indicate that the success of deep learning in the field of transportation is due to its ability to mine the complex patterns in high-dimensional spatiotemporal data, especially its ability to dynamically model local features.  
However, in the field of container throughput prediction, the application of deep learning has not yet formed a scale. Unlike traffic flow forecasting, container throughput is dominated by long-term global factors, such as macroeconomic cycles and international trade policies, and its data often exhibit strong trends, low-frequency, and high-noise characteristics. Traditional statistical models are still widely used due to their ability to capture the explicit seasonality and linear trends [35]. However, these methods do not take into account the complexity of and variability in the data caused by external environmental changes. Fortunately, deep learning can explore the intrinsic patterns in data and analyze their features [36]. In recent years, scholars have attempted to break through this limitation. For example, [PERSON] [37] and [PERSON] [38] demonstrated, through comparative experiments, that LSTM outperforms SARIMA and Prophet in most scenarios. [PERSON] et al. [39] combined a discrete wavelet transform (DWT) with LSTM to validate the effectiveness of deep learning for throughput regression prediction. [PERSON] [40] further introduced the BiLSTM model to improve prediction accuracy using bidirectional time series modeling. However, the existing research has mostly focused on RNNs and their variants, which can capture temporal dependencies but lack the ability to extract local features.  
To compensate for this deficiency, some scholars have turned to models with stronger feature extraction capabilities, such as CNNs and GNNs. In practice, [PERSON] proposed a dynamic graph attention multi-attention network (DGAT-MAN), which significantly improved the prediction performance by modeling the spatiotemporal correlations through graph structures [41]. But such methods rely on precise node relationship definitions. Inmulti-index time series scenarios, the lack of prior knowledge about node relationships can easily lead to a decrease in model performance. Therefore, this article chooses to apply a CNN model to predict the throughput of sea-rail intermodal containers, as its convolutional kernel can adaptively learn the local correlation patterns between multiple indicators. By extracting the features through a multi-layer network structure, high prediction accuracy can be achieved.