## 2 Related Work  
Traffic flow prediction plays a crucial role in traffic management and transportation planning. Accurate prediction of traffic flow can help to alleviate congestion, reduce travel time, and improve safety. Currently, machine learning is widely used in the field of traffic flow prediction and there are many models [16; 17; 18]. [PERSON] et al. [19] used a particle swarm optimization algorithm to find the key parameters in the SVM, and then improved the prediction accuracy of short-term traffic flow. [PERSON] et al. [20] proposed a KNN regression model for short-term traffic flow prediction by optimizing the traditional KNN regression algorithm. The model restructures the balanced training set to address the data imbalance issue in traffic flow data and considers the local and global patterns of traffic flow data. In addition to regression algorithms, with the development of deep learning, [PERSON] et al. [21] used a TCN model for traffic flow prediction, and this convolutional model greatly improves the parallelization of traffic data operation. Although the above models can also achieve traffic flow prediction, the prediction accuracy is slightly poor, and furthermore, more scholars use long- and short-term memory networks to build hybrid prediction models. In 2019, [PERSON] et al. [22] used the LSTM model as the basis and added the attention mechanism to better predict traffic flow data. [PERSON] et al. [23] combined CNN, LSTM, and attention mechanism, and proposed ATT-DHSTNet network architecture, which gave full play to the extraction ability of convolutional network for spatial information and recurrent neural network for temporal information, together with the control of global information by attention mechanism; this achieved good prediction results. [PERSON] et al. [24] used the KNN algorithm for spatial location site selection, considering the influence relationship between the traffic volumes of different location sites; then, the traffic volumes of different sites were predicted separately using the model built by the LSTM module; and finally the traffic predictions between different sites were given different weights as the predicted value of the site. Recurrent neural networks (RNNs) have been widely used in traffic flow prediction due to their high accuracy [25; 26; 27; 28]. However, recurrent neural networks suffer from gradient disappearance and gradient explosion, and the prediction accuracy decreases dramatically as the prediction length increases. To address this issue, researchers have explored methods such as self-attention mechanism to achieve long-term time series prediction. In recent years, many scholars have proposed various models based on self-attention mechanism, including [PERSON] et al. 's informer model [29], [PERSON] et al. 's autoformer model [30], and [PERSON]'s FEDformer model [31]. The informer model, developed on the basis of the transformer model for time series forecasting, applies self-attention mechanism to acquire similarity information for individual data points without considering the whole sequence. The autoformer model proposes an auto-correlation mechanism, in which the time-delayed aggregation operation further considers both individual and overall information and presents better results in traffic flow prediction. The FEDformer model predicts traffic flow from the perspective of the frequency domain. Compared with traditional RNNs, these models--based on self-attention mechanism--show better prediction performance in multi-step prediction. Due to the common characteristics of nonlinearity, stochasticity and instability of time series data such as stock prices, wind power generation, water flow, and traffic flow, the data decomposition methods used in other fields can be applied to traffic flow data to improve prediction accuracy. For instance, [PERSON] et al. combined EMD and LSTM models to mitigate the nonlinearity and non-smoothness of traffic flow data using EMD and formed a hybrid model [32]. [PERSON] et al.