## 4 Experiments and Discussion
### Traffic Flow Decomposition and Reconfiguration  
Firstly, the processed traffic flow data are decomposed by STL, and the decomposition results are shown in Figure 6; from the trend term \(Y_{t}\) in the figure, we can see that the overall traffic flow shows an upward trend, and the two depressions correspond to the traffic flow of the weekend, which has a significant decrease. The tail of \(Y_{t}\) is the National Day period, and the demand for going out increases, which leads to a higher traffic flow than the usual period. From the period term \(Y_{c}\), we can see the periodicity of the traffic flow, similar traffic flow changes on a day-to-day basis, and that the traffic flow in the morning and evening peak is higher than the flat period. The residual term \(Y_{r}\) obtained disorderly changes, but there are still potential features in it, so the optimized VMD is used to decompose \(Y_{r}\) in the second level, and the optimal choice is achieved by the GA of VMD, where =14 and =1880 are obtained, and the decomposition results are shown in Figure 7.  
Figure 5: Feature correlation analysis chart. Figure 6: STL decomposition results. After the secondary decomposition of the traffic flow sequence by STL and VMD, multiple sequence components are obtained. To reduce the complexity of calculation and improve the efficiency of prediction, fuzzy entropy [46] and the Lempel-Ziv [47] complexity index are used to reconstruct the components scientifically and reasonably. Where entropy is a covariate to describe the state of the object to analyze the complexity of the sequence; researchers proposed to use entropy to measure the complexity and fuzzy entropy is to use fuzzy affiliation function instead of Heaviside function in sample entropy so that it can better evaluate the complexity of the sequence more robustly. Unlike the statistical significance of entropy, the Lempel-Ziv complexity index reflects the probability of increasing nonlinear features in the traffic flow, and a larger value indicates that the traffic flow is more complex and contains more nonlinear features. The components obtained after the second-level decomposition show more obvious and smoother characteristics compared with the original residual terms. To improve the computational efficiency and reduce the interference of subjective factors such as human selection on the prediction effect, the fuzzy entropy (FE) and Lempel-Ziv complexity index (LZ) of each component are calculated for reconstruction, and the calculation results are shown in Table 3.  
Referring to FZ first, BIMF (1,7,8,9) is reconstructed to get RBIMF1, BIMF (5,6,10,11) is reconstructed to get RBIMF2, and BIMF (2,3,4,12,13,14) is reconstructed to get RBIMF3. LZ reflects the periodicity of the sequence, and a larger LZ indicates the fewer periodic components inside the sequence; it can be seen from the LZ of each sequence that the LZ of BIMF (1,7,8,9) is smaller, and the periodicity feature is more obvious; the LZ of BIMF (2,3,4,12,13,14) is larger, which indicates that the regularity of sequence change is weaker, which fully proves the rationality of component reconstruction. The results of the trend \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **BIMF1** & **BIMF2** & **BIMF3** & **BIMF4** & **BIMF5** & **BIMF6** & **BIMF7** \\ \hline FE & 0.31 & 0.54 & 0.57 & 0.52 & 0.44 & 0.35 & 0.28 \\ LZ & 0.10 & 0.37 & 0.35 & 0.37 & 0.33 & 0.34 & 0.25 \\ \hline  & **BIMF8** & **BIMF9** & **BIMF10** & **BIMF11** & **BIMF12** & **BIMF13** & **BIMF14** \\ \hline FE & 0.26 & 0.30 & 0.35 & 0.44 & 0.52 & 0.57 & 0.52 \\ LZ & 0.23 & 0.30 & 0.35 & 0.35 & 0.37 & 0.38 & 0.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fuzzy entropy and Lempel-Ziv complexity index calculation results for BIMFs. Figure 7: GA optimization of VMD residual term decomposition results.