# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 4 Discussion
### Performance of Prediction Model under the Influence of the Epidemic  
In order to further examine the SARIMA-NAR combination model's adaptability to the great fluctuation of traffic flow during special periods and verify its performance, the traffic flow in 2020 under the influence of the epidemic was predicted in this paper. The prediction process is the same as above, and the prediction result is shown in Figure 12, and the comparison of error of each model is shown in Table 5.  
The average absolute error of SARIMA-NAR combination model 1 prediction was 8.07%, and its fitted MAE was 8.28%, indicating a good prediction effect. For the sudden decrease in traffic flow during the epidemic, the SARIMA-NAR combination model can get a relatively accurate prediction result. In particular, the SARIMA-NAR model 1 performed better and could quickly adapt to intense changes in traffic flow. The fitting and prediction accuracy of the two combined models were both greater than 0.85, indicating that the combined model was reliable and effective.  
In addition, in the process of constructing the SARIMA model, attention should be paid to the parameter selection of the non-seasonal difference coefficient d and the seasonal difference coefficient D. When the ADF test results of the sequences after different differences are similar, it can be verified whether the model can effectively stabilize the traffic flow sequence from the aspects of autocorrelation diagram, sequence diagram, and BIC value statistics. Subsequent calculations cannot be performed if the series is non-stationary after differencing.  
In future research, automated methods for image recognition and data integration decision making for SARIMA parameter selection can be explored. Further, the spatiotemporal  
Figure 12: Forecast results of traffic volume under the influence of the epidemic.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{\(\epsilon_{\text{MAPE (\%)}}\)} & \multicolumn{2}{c}{\(\epsilon_{\text{MAE (10^{4})}}\)} & \multicolumn{2}{c}{\(\epsilon_{\text{RMSE (10^{4})}}\)} & \multicolumn{2}{c}{\(A\)} & \multicolumn{2}{c}{\(R\)} \\ \cline{2-11}  & **Prediction** & **Fitting** & **Prediction** & **Fitting** & **Prediction** & **Fitting** & **Prediction** & **Fitting** & **Prediction** & **Fitting** \\ \hline SARIMA & 12.25 & 9.63 & 6.18 & 4.29 & 7.67 & 6.62 & 0.87 & 0.84 & 0.76 & 0.82 \\ NAR & 13.24 & 12.17 & 7.47 & 4.79 & 9.44 & 6.64 & 0.83 & 0.83 & 0.54 & 0.81 \\ SARIMA–NAR combination model 1 & 8.07 & 8.28 & 4.29 & 3.48 & 5.66 & 5.57 & 0.90 & 0.87 & 0.86 & 0.87 \\ SARIMA–NAR combination model 2 & 10.30 & 9.98 & 5.46 & 4.31 & 7.13 & 6.38 & 0.87 & 0.85 & 0.73 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model prediction and fitting situation under the influence of the epidemic.  
correlation of traffic flow can also be considered in the future to realize the fusion prediction of spatial mode and time series. Coupling applications between different forecasting dimensions is a general trend towards more in-depth research on complex problems.