# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 7 Conclusions and Future Perspectives
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Model** & **Advantages** & **Disadvantages** \\ \hline RNN & & The RNN can capture the dynamic change characteristics of time series data and effectively use historical traffic flow information to predict future trends and is suitable for processing time-dependent traffic data. It performs well in short-term traffic flow prediction. & It can easily encounter gradient vanishing or gradient exploding problems during training, making it difficult to effectively learn long-term dependencies. \\ \cline{2-3} RNN-based models & LSTM & LSTM’s unique gating mechanism can effectively alleviate the long-term dependency problem of traditional RNNs, enabling the model to better capture the long-term trends and periodic changes in time series and improve the prediction accuracy. It is suitable for short-term and long-term traffic flow prediction, especially when the data are highly periodic. & The LSTM model is relatively complex, takes a long time to train, and requires a large amount of historical data for training to achieve better results. \\ \cline{2-3} Deep learning models & GRU & The GRU is simpler than LSTM, but it can also solve problems such as long-term dependency and gradient disappearance. It is suitable for short-term and medium-term traffic flow prediction tasks, especially when computing resources are limited. & Is prediction performance is affected by the data quality and feature selection. In complex scenarios, its prediction ability may be slightly inferior to that of LSTM. \\ \cline{2-3} Deep learning models & GCN & It is suitable for processing non-Euclidean structured data, can capture the relationships between nodes, and has good generalization abilities. It is suitable for traffic flow prediction tasks with strong spatial correlation, especially traffic flow modeling based on urban road networks. & The computational resource requirements are high and overfitting may occur when processing large graphs. \\ \cline{2-3} Transformer & Transformer & The parallel computing capabilities of the Transformer greatly improve the computing efficiency, and its self-attention mechanism enables the model to consider all positions in the sequence at the same time, thereby better capturing long-distance dependencies. It is suitable for long-term traffic flow prediction tasks. & Requires a large amount of historical data for training, and the computational cost is high. \\ \hline Hybrid neural network & By integrating the strengths of multiple neural network models, hybrid neural networks enable the comprehensive learning and precise prediction of traffic flow data. This approach is particularly well suited for complex traffic forecasting tasks, including large-scale urban traffic flow modeling and intelligent transportation management systems, where capturing both spatial and temporal dependencies is essential for accurate and robust predictions. & The model is highly complex, and the training and optimization process is relatively difficult, requiring longer computing times and larger amounts of computing resources. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._accuracy of traffic management but also helping to optimize resource allocation, alleviate traffic congestion, and enhance the public's travel experiences. Although the short-term traffic flow prediction method based on deep learning has obvious advantages over the other two methods, it also faces certain limitations and challenges. One of the main problems is data acquisition and processing. This study observes that the datasets utilized in existing research predominantly originate from urban road traffic flow data. Given the substantial differences in traffic conditions between urban and rural areas, relying on a single type of dataset may result in the model's inability to adequately capture the traffic patterns in rural regions, thus compromising its predictive accuracy for rural data.