# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 6 Method Comparison and Analysis
### Method Comparison
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Model** & **Advantages** & **Disadvantages** \\ \hline \hline \multirow{6}{*}{HA} & \multirow{6}{*}{ARIMA statistical models} & The model algorithm is simple, runs quickly and has a small execution time overhead. & The model predicts future data based on the mean of historical data. Simple linear operations cannot represent the deep nonlinear relationships of spatiotemporal series. It is not ideal for the prediction of data with strong disturbances. \\ \cline{2-3}  & & The model algorithm is simple and based on a large amount of uninterrupted data; this model has high prediction accuracy and is particularly suitable for stable traffic flows. & Its modeling capabilities for nonlinear trends or very complex time series data are relatively weak. When encountering non-stationary data, it must first be stabilized; otherwise, its applicability will be limited. \\ \cline{2-3}  & & The algorithm is efficient, can quickly process large amounts of data, is adaptive, and can automatically adjust the prediction results based on historical data. It is suitable for situations where the sensor data have large noise. It can smooth data during real-time prediction and is suitable for traffic flow prediction in short time intervals. \\ \hline \multirow{6}{*}{Machine learning models} & \multirow{6}{*}{KNN} & High portability, simple principles, easy to implement. High model accuracy and good adaptability to nonlinear and non-homogeneous data. Since KNN does not require a complex training process, it is suitable for application scenarios with smaller data scales. & The processing efficiency of large-scale datasets is low, the model converges slowly, and the running time is long; thus, it may not meet the requirements for the real-time prediction of road traffic flows. \\ \cline{2-3}  & & The training process of the MLP is relatively simple, and there are many optimization methods. It is suitable for regression tasks, such as traffic flow prediction based on static features. & The training process of the MLP may take a long time and require considerable computing resources. When the number of data or features is small, the MLP may face overfitting problems. Performance is usually limited when processing non-stationary data. \\ \cline{2-3}  & & CNNs can effectively extract the spatial characteristics and temporal dependencies of traffic flow data and have strong generalization abilities and high prediction accuracy. Therefore, they perform well in traffic prediction tasks based on road network topologies. & Due to the complex structure of the model, the training process requires a large amount of computing resources and time. At the same time, the structure of the CNN cannot capture time dependencies well and it has a limited ability to process sequence data. \\ \cline{2-3}  & & The AE can learn the laws and patterns of historical traffic data to predict future traffic flows and improve the efficiency and accuracy of traffic management. The AE is not used directly for prediction but is usually used as an auxiliary tool for feature extraction in combination with other deep learning models. \\ \cline{2-3}  & & The AE can learn the laws and patterns of historical traffic data to predict future traffic flows and improve the efficiency and accuracy of traffic management. The AE is not used directly for prediction but is usually used as an auxiliary tool for feature extraction in combination with other deep learning models. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the advantages and disadvantages of different methods.