## 5 Conclusions  
This paper takes road traffic flow in Changsha city as the research object, effectively reduces the nonlinearity of complex traffic flow by the two-stage decomposition method, fuses the external characteristics of traffic flow for prediction, and finally optimizes the weights of the three prediction models established by Q-learning algorithm to obtain the optimal prediction results. Through the comparison and analysis of ablation experiments and other models, the conclusions are as follows:  
* Using STL and VMD, two-level decomposition can effectively reduce the unsteadiness of traffic flow data and decompose it into multiple smooth components, i.e., it can fully explore the intrinsic features of traffic flow and also allow the model to better extract the features in it. The impact of each module on the prediction accuracy was analyzed by ablation experiments, in which the decomposition module improved the prediction effect the most.
* It is important to improve the prediction accuracy by fully considering the spatial and temporal correlation of traffic flow and the influence of the external environment on traffic flow.
* Three phase-divergent models, GA-KELM, LightGBM, and LSTM-Attention, are constructed, and the Q-learning method is used to integrate the three single models, which can fully extract the potential features in the traffic flow and fuse the advantages of each model. Compared with other models, the evaluation index of the model D-T-Q (E_L_LA) in this paper is lower and more suitable for the prediction of short-time traffic flow.  
In conclusion, the short-time traffic flow prediction method proposed in this paper is feasible and has a good prediction effect for short-time traffic flow.  
The current study can be further delved into by the following aspects: First, traffic accidents are also an important factor affecting the traffic status, and when a traffic accident occurs, it will lead to abnormal traffic flow, so using it as an influencing factor can improve the accuracy of the prediction. Second, the performance of the model is only verified in the current single-step prediction, and it is noteworthy that multi-step prediction is also important for ITS. Third, the method in the paper can still be applied to highway scenarios and tasks, such as traffic speed prediction and travel time prediction, which will help improve road access efficiency and safety through the high accuracy prediction results obtained.  
Conceptualization, K.C.; formal analysis, D.Q.; Investigation, S.W. and Q.W.; Methodology, K.C.; Writing--original draft, K.C.; Project administration, D.Q. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 51678320).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \(E_{MAE}\) & \(E_{RMSE}\) \\ \hline LSTM & 9.561 & 12.459 \\ CNN-GRU & 7.923 & 10.242 \\ D-GA-BP & 5.232 & 6.705 \\ D-LightGBM & 2.883 & 3.913 \\ D-TQ (E-LLA) & 2.622 & 3.479 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction errors across models.