## 2 Literature Review
### Deep Learning Methods for Traffic Flow Parameter Prediction Applications
[34] developed an innovative deep learning architecture for predicting traffic flow. The architecture combined linear models with a series of tanh layers to address the challenges of sharp nonlinear spatiotemporal effects produced by transitions between free flow, congestion, recovery, and gridlock. [PERSON] et al. [35] used the predictive scheme of seasonal ARIMA (SARIMA) models to overcome data availability issues and used limited input data for short-term traffic flow prediction. [PERSON] et al. [36] proposed a method for optimizing the elements of fuzzy rule-based system (FRBS) hierarchies using a GA and cross-entropy (CE) hybrid, called GACE, for short-term traffic congestion prediction. [PERSON] et al. [37] proposed a chaotic prediction method based on the Lyapunov exponent for traffic flow characteristics with chaos. [PERSON] et al. [38] proposed an end-to-end trainable unified model that combines an autoencoder and a random forest to construct a random decision tree model for guiding parameter learning. [PERSON] et al. [39] used Dest-ResNet to correct the original traffic speed prediction errors and solved the time series relationship problem caused by crowdsourcing map queries. [PERSON] et al. [40] used a long short-term memory (LSTM) network and other models for traffic flow prediction. This model further improved the RNN and addressed gradient explosions caused by missing data in simple RNNs. [PERSON] et al. [41] applied LSTM networks for traffic speed prediction, demonstrating that the \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Data** & **Main Methods** & **Typical Research** \\ \hline
1 & traffic speed & CNN combined with LSTM and & [PERSON] et al. [28] \\
2 & traffic speed & & & \\
3 & traffic flow data from the M6 & & & \\  & freeway in the U.K. & & & \\
4 & PeMS database & Mixed methods on convolutional recurrent & [PERSON] et al. [31] \\  & flow, speed, journey time & neural network & \\
5 & from UK & CNN and GRU & [PERSON] et al. [32] \\
6 & PeMS database & Dynamic Tensor Completion & [PERSON] et al. [33] \\ \hline \end{tabular}
\end{table} 
Table 3: Recent methods on multivariate predictions. LSTM structure can capture long-term time dependencies in traffic data and overcome the vanishing gradient problem by using memory blocks, demonstrating excellent performance in time series prediction with long-term dependencies. [PERSON] et al. [42] proposed a tree-based ensemble method that improved the performance of highway section travel time predictions by considering variables derived from historical data. Additionally, gradient boosting tree methods have also been developed, revealing hidden patterns in travel time data and improving the accuracy and interpretability of models. Deep learning technology has tremendous potential for traffic flow prediction. Future research could further explore deep-learning-based traffic flow prediction methods to improve the efficiency of traffic management and reduce the negative impacts of urban traffic problems. In summary, numerous methods have been proposed to address traffic operating status estimation and classification. A comparative analysis of these methods has revealed the following characteristics: First, many studies focus on the parameters of either the time or spatial dimensions to conduct state classification or recognition research, and the selected methods rely primarily on pattern recognition or clustering methods. Second, prediction and estimation methods based on multivariate variable reconstruction have been continuously developed, with classic theories such as Bayesian theory, Kalman filters, support vector machine, and complex networks being applied widely. Additionally, with the rapid development of neural networks, deep learning, and other technologies, multivariate phase space reconstruction prediction and estimation methods have gradually become research hotspots.