## 5 Performance Evaluation
### Comparison Performance of Methods  
In order to evaluate our proposed methods' advantage further, the simulation compares the performance among three methods, i.e., SVM, PSO-LSTM and ARIMA models. These methods have been applied to various sequence data prediction tasks and satisfactory results have been obtained. In comparison to the baseline algorithms, we use the same dataset to make predictions and evaluate the performance of our algorithm. Multiple algorithms are employed to perform predictions on the dataset, and the evaluation is carried out using the same evaluation function. Additionally, we also perform optimization on the baseline algorithms to ensure a fair comparison. Table 3 shows the comparison results of prediction accuracy between MVHS-LSTM and other models. The MAPE score is particularly important for the performance of the indicator model. MAPE is used to detect the average absolute percentage error of each prediction model and the sample-observed traffic flow. Furthermore, for different sections of the data, the MAE data are not accurate, and MAPE can better reflect the average prediction accuracy. As shown in Table 3, the MAPE score of our model is 5.8102%, which is 10.4% lower than the ARIMA model, 13.1% lower than the SVM model, and 1.3% lower than the PSO-LSTM model. Our model does not exceed the SVM and ARIMA model in computing time. However, compared to the ARIMA model, our model is 37.2268 lower in MAE, 58.5902 lower in RMSE, 0.6715% lower in MAPE, and only 175 s longer in computing time. Compared to the PSO-LSTM model, although the evaluation metrics are slightly lower than our model, the computing time of the PSO-LSTM model is 513.4% longer than our model, which is unacceptable. Therefore, our model has outstanding comprehensive performance among several comparison models. Figures 7-10 are comparison charts of the traffic flow prediction effect. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ARIMA** & **SVM** & **PSO-LSTM** & **MVHS-LSTM** \\ \hline MAE & 110.8981 & 78.0017 & **71.7587** & **73.6713** \\ RMSE & 151.6354 & **82.8986** & **91.3037** & 93.0452 \\ MAPE & 6.4817\% & 6.6848\% & **5.8874**\% & **5.8102**\% \\ Time/s & **191** & **256** & 1879 & 366 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of evaluation function between methods. Figure 6: The degree of window length affecting MAPE and time. Figure 8: SVM model prediction effect chart. Figure 7: ARIMA model prediction effect chart. Figure 9: PSO-LSTM model prediction effect chart. It can be seen from the chart that the ARIMA model utilizes differencing methods, and its predicted values consistently fall between the actual data points. Overall, the predictions are relatively stable, but they lack short-term forecasting accuracy, and while the model captures the general trend reasonably well, most of the values are overly averaged, failing to capture the short-term characteristics of traffic flow and resulting in fewer fluctuations or spikes in the predictions. The SVM model focuses primarily on short-term characteristics and lacks the ability to learn from long time series data. As observed, the predictions in the first half of the sequence are relatively accurate. However, in the latter half of the predictions, the SVM method gradually deviates from the original data and tends to be positioned above the overall trend. With the passage of time, the predicted values may become increasingly inaccurate. The PSO-LSTM model utilizes certain hyperparameters from particle swarm optimization to find the optimal performance point. However, it is associated with long computation times, making it unsuitable for studying real-world traffic flow data. The MVHS-LSTM model not only effectively predicts peak and valley values, but it also demonstrates improved performance. It boasts a low computation time, making it well suited for practical applications. The update time of our online system depends on the frequency at which the traffic management system transmits data.