## 2 Literature Review
### Deep Learning Methods for Traffic Flow Parameter Prediction Applications  
Traffic flow parameter prediction is an important component of urban traffic management and planning, and it is crucial for alleviating traffic congestion, improving road usage efficiency, and optimizing transportation system operations. Some researchers have combined machine learning with traditional linear methods, including the K-nearest neighbor algorithm (KNN), support vector regression (SVR), K-means, and artificial neural network (ANN) models, to predict traffic flow parameters. In recent years, the rapid development of deep learning technology has attracted widespread attention to its applications in traffic flow parameter prediction. Deep learning can automatically learn feature representations from large-scale traffic data by constructing complex neural network structures, thereby improving the accuracy and robustness of predictions. Researchers have applied deep \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Data** & **Main Methods** & **Typical Research** \\ \hline
1 & period time series & Complex network & [PERSON] [16] \\
2 & speed, occupancy, and flow & Bayes theory and phase space & [PERSON] [17] \\  & & reconstruction theory & \\
3 & traffic flow data & Phase space reconstruction and [PERSON] et al. [18]; [PERSON] et al. [19] \\
4 & speed, occupancy, and flow &
\begin{tabular}{c} Support vector machine and chaos theory \\ Phase space reconstruction and genetic \\ algorithm (GA) and quantum particle swarm \\ optimization with the wavelet neural network \\ \end{tabular} & [PERSON] [20] \\
5 & traffic flow time series &
\begin{tabular}{c} Algorithm (GA) and quantum particle swarm \\ optimization with the wavelet neural network \\ \end{tabular} & [PERSON] [21] \\ \hline \end{tabular}
\end{table} 
Table 2: Traditional methods of multivariate phase space reconstruction and prediction. learning to traffic flow parameter prediction, achieving significant results. These deep learning models can obtain more accurate prediction results than early machine learning models and statistical models. For time series data such as traffic flow, recurrent neural networks (RNNs) have been used to fully extract the information of each time step during spatial-temporal feature extraction. Typical multivariate variable phase space reconstruction and prediction methods are shown in Table 3. [PERSON] et al. [28] presented a method for short-term traffic flow prediction based on an attention model that can extract spatiotemporal features and understand the influence of each unit of data. [PERSON] et al. [29] constructed a traffic flow prediction model based on image Motif-GCRNN, capturing spatiotemporal dependencies in road networks through graph convolutional learning. [PERSON] et al. [30] proposed a stacked autoencoder Levenberg-Marquardt model to improve the prediction accuracy by learning traffic flow features through layer-by-layer unsupervised learning. [PERSON] et al. [31] proposed a DNN-based traffic flow prediction model (DNN-BTF) that fully utilized the periodicity and spatiotemporal characteristics of traffic flows using convolutional neural networks and recurrent neural networks to extract spatial and temporal features. [PERSON] et al. [32] proposed an adaptive multimodal deep learning model, HMDLF, combining a convolutional neural network (CNN) and gated recurrent unit (GRU) models to capture local trends and long-term dependencies in traffic data, reducing the computational complexity. [PERSON] et al. [33] proposed a new short-term traffic flow prediction method based on dynamic tensor completion (DTC), where traffic data were represented as dynamic tensor patterns. This method was able to capture more traffic flow information, including the time variability, spatial features, and multimodal periodicity, than traditional methods. A DTC algorithm was designed to use multimodal information for low-rank constraint prediction of traffic flow. [PERSON] et al.