# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Case Studies
#### 4.2.4 Comparative Analysis of Results
The traffic flow gradually reached the maximum value on one day at the time axis of [150, 200], and the traffic flow fluctuated significantly, which brought great difficulties to the flow prediction in this time period. In order to more intuitively see the prediction effects of different models, this local prediction result was separately amplified and analyzed. The local prediction results are shown in Figure 13.  
Figure 11: Prediction results of the low-frequency component sequences. Figure 12: Comparison of multi-model prediction results. In Figure 13, it can be seen that the combined prediction model of traffic flow based on CEEMDAN and machine learning proposed in this paper (hereafter referred to as the combined model) has the best prediction results, followed by the BGWO-LSTM and IGWO-SVM models, and KNN has the worst prediction accuracy. Especially in the time period [185; 195] of the timeline, the traffic reached the maximum, and the prediction results of the three single prediction models were quite different from the actual traffic flow. The arrival of the peak of the traffic flow was not well predicted, while the prediction results of the combined model were relatively close to reality and successfully predicted the peak of traffic. In order to more intuitively compare the advantages and disadvantages of the above four models, the four index models of the mean squared error (MSE), the mean absolute error (MAE), the root mean square error (RMSE) and the decision coefficient (R\({}^{2}\)) were used for comprehensive analysis and evaluation, and the evaluation indicators of each model are shown in Table 3.  
As can be seen in Table 3, the mean squared error of the combined model is smaller than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model 41.26, 44.98 and 57.69, respectively. The mean absolute error of the combined model is smaller than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model, by 2.33, 2.44, and 2.70, respectively. The root mean square error of the combinatorial model is smaller than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indicators of the combined model  
Figure 13: Comparison of local prediction results of different models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Indicators** & **Model** & **BGWO-LSTM** & **IGWO-SVM** & **KNN** \\
**Based on CEEMDAN** & **Model** & **Model** & **Model** & **Model** \\ \hline \(\text{E}_{\text{MSE}}\) & 32.53 & 73.79 & 77.55 & 90.22 \\ \(\text{E}_{\text{MAE}}\) & 4.14 & 6.47 & 6.58 & 6.84 \\ \(\text{E}_{\text{RMSE}}\) & 5.70 & 8.59 & 8.81 & 9.50 \\ R\({}^{2}\) & 0.98 & 0.96 & 0.96 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comprehensive comparison of four short-term traffic flow prediction models. are much smaller than the remaining single models. At the same time, the coefficient of determination of the combined model is closer to 1 than that of the other models, indicating that the prediction result of the combined model was closest to the actual traffic flow. Of the remaining three single models, the BGWO-LSTM model performed the best, the IGWO-SVM model followed, and the KNN model performed the worst. In summary, the combined model proposed in this paper combines the advantages of a single prediction model, and the prediction accuracy is greatly improved compared to that of a single model. It can accurately predict road traffic flow and has good application prospects.