## 2 Related Works
### Deep Learning Methods  
Within the second category of methods, this paper selects several representative examples for illustration. In order to solve the problem of urban traffic congestion, [PERSON] et al. estimated and predicted the traffic state based on an adaptive multi-kernel Support Vector Machine (AMSVM). The Gaussian kernel and polynomial kernel were combined to form AMSVM and analyze the nonlinearity and randomness of traffic flow with spatio-temporal information. The parameters of AMSVM were optimized by the adaptive particle swarm optimization algorithm, and a new method for adaptively adjusting the mixed kernel weight according to the trend of real-time traffic flow was proposed [8]. [PERSON] et al. equipped the deep learning technique with non-parametric regression. The first layer in the deep learning algorithm identifies spatio-temporal relationships between predictors and other non-linear relationships. The results were better than Online-Support Vector Regression (OL-SVM), Stacked Auto-Encoder (SAE) and Back-Propagation-Neural Network (BP-NNet). However, the model did not consider the multivariate time series for traffic prediction [9]. [PERSON] et al. analyzed the autocorrelation and distribution characteristics of traffic flow to correct the structure of the time series model. Based on the Gaussian distribution of the traffic flow, a hybrid model based on the Bayesian learning algorithm was proposed, which improved the Seasonal Autoregressive Integrated Moving Average (SARIMA)'s scenario and performed better in prediction accuracy and effectiveness [10]. [PERSON] et al. proposed that traffic flow data are mainly treated as time series data and chose a Bidirectional Recurrent Neural Network (BRNN) model in RNN. Compared with LSTM model and Gated Recurrent Unit (GRU) model; this model is good at processing time series data [11]. [PERSON] used the Convolutional Neural Network (CNN) to predict the spatio-temporal dynamics of urban road traffic flow and compared it with LSTM, Recurrent Neural Network (RNN), AlexNet and Spatio-Temporal Graph Convolutional Network (STGCN) models. The prediction accuracy and robustness of the proposed method had better performance [12]. [PERSON] et al. proposed an urban traffic flow prediction method based on the graph convolution neural network, which can excavate the spatio-temporal dynamic mode of urban road network traffic flow and realized accurate traffic flow prediction [13]. [PERSON] et al. used the Vector Auto-regression (VAR) model to evaluate the internal correlation between traffic variables and determined the predictable relationship between these variables. Then, the CNN-LSTM hybrid neural network model was used to predict the multi-feature speed of a spatial position. The prediction results showed that the multi-feature prediction was better than the single feature prediction [14]. [PERSON] et al. combined CNN and LSTM to predict road traffic risk using spatio-temporal multi feature traffic data. Compared with previous studies, our approach has lower loss values and a better convergence speed [15]. After meticulously analyzing the existing literature, it becomes evident that, while numerous studies have successfully enhanced data accuracy, there persist several unresolved issues that are seldom discussed. Firstly, there is a pressing need to take into account the intricate interdependence among traffic flow data. It is imperative to recognize that, in the prediction process, it is not always necessary to feed the entire dataset into the model for learning. This approach can often lead to an unwarranted increase in the learning burden, thereby compromising the efficiency of the prediction system. Furthermore, it is crucial to recognize that solely prioritizing accuracy without giving due consideration to learning time is inadequate for real-time traffic flow prediction scenarios. This approach fails to align with the inherent real-time nature and the rapid, dynamic changes that are characteristic of traffic flow.