## 4 Experiments and Results Analysis
### Analysis of Ensemble Learning Model Prediction Performance  
To verify whether ensemble learning models can effectively improve urban road travel time prediction performance, we implemented ensemble learning models with different meta-learners and compared them with single models. Table 4 shows results for 5 prediction scenarios (30 min sliding time window with prediction scales of 2, 10, 20, 30, and 60 min). Table 4 is shown as follows:  
(a) **Bold values** indicate that the corresponding model achieved the optimal value among all models for the current prediction scenario and metric. (b) _Italic values_ represent the optimal values achieved by single models for the current prediction scenario and metric. (c) Underlined values indicate that although the corresponding model did not achieve the optimal value among all models for the current prediction scenario and metric, the values still exceeded the optimal values of single models. From the results, we can observe the following:  
(1) Selective advantages of ensemble learning: Not all ensemble learning models outperform single models. Among the five prediction scenarios, ensemble models achieved optimal results in MAPE values for three scenarios (predicting 2, 10, and 60 min ahead) and RMSE values for two scenarios (predicting 2 and 20 min ahead). (2) Performance of shallow learning meta-learners:  
i. Linear regression ensemble learning achieved an RMSE of 5.82 in the 2 min ahead prediction scenario, outperforming all single models. ii. Random forest ensemble learning showed no significant advantages on either MAPE or RMSE. (3) Performance of deep learning meta-learners:  
i. LSTM-DNN ensemble learning models performed excellently in specific scenarios, for example, LSTM-DNN(2) ensemble learning achieved the best MAPE for 2 min ahead prediction (8.63%), and LSTM-DNN(1) ensemble learning achieved the best MAPE for 60 min ahead prediction (17.21%). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MAPE (\%), Sliding Time Window = 30 min**} & \multicolumn{3}{c}{**RMSE, Sliding Time Window = 30 min**} \\ \hline Prediction Horizon (mins) & 2 & 10 & 20 & 30 & 60 & 2 & 10 & 20 & 30 & 60 \\ Linear Regression & 8.915 & 15.434 & 17.375 & 18.181 & 20.645 & **5.823** & 10.915 & 12.384 & 12.900 & 13.434 \\ Ensemble & 32.259 & 29.522 & 28.705 & 28.136 & 26.149 & 5.964 & 11.021 & 12.335 & 12.850 & 13.179 \\ LSTM-DNN(1) Ensemble & 8.675 & 15.344 & 15.846 & 20.410 & **17.211** & 5.883 & 10.849 & **12.201** & 13.191 & 13.196 \\ LSTM-DNN(2) Ensemble & **8.634** & 15.513 & 16.500 & 16.969 & 18.842 & 5.870 & 10.923 & 12.347 & 12.909 & 13.205 \\ DNN64\_2 Ensemble & 8.635 & 14.718 & 19.729 & 18.002 & 21.536 & 5.886 & 10.932 & 12.530 & 12.940 & 13.478 \\ DNN16\_2 Ensemble & 8.691 & **14.120** & 19.883 & 19.497 & 24.417 & 5.830 & 11.034 & 12.429 & 12.979 & 13.512 \\ Linear Regression & 8.937 & 15.615 & 17.582 & 18.722 & 20.782 & 5.915 & 10.950 & 12.426 & 12.981 & 13.426 \\ Ridge Regression & 8.937 & 15.615 & 17.582 & 18.722 & 20.782 & 5.915 & 10.950 & 12.426 & 12.981 & 13.426 \\ LASSO Regression & 32.312 & 28.731 & 26.867 & 25.701 & 23.542 & 5.919 & 10.946 & 12.421 & 12.964 & 13.421 \\ Random Forest & 32.289 & 29.875 & 28.986 & 28.228 & 26.264 & 5.945 & **10.724** & 12.332 & **12.628** & **12.978** \\ LSTM-DNN & _8.647_ & _14.292_ & **15.529** & **15.634** & _17.411_ & _5.854_ & 11.012 & _12.216_ & 12.958 & 13.154 \\ DNN84\_2 & 9.947 & 21.350 & 17.511 & 18.742 & 19.766 & 6.035 & 11.530 & 12.489 & 13.054 & 13.486 \\ DNN84\_4 & 13.495 & 14.386 & 17.672 & 17.705 & 20.717 & 6.615 & 11.117 & 12.484 & 13.071 & 13.463 \\ DNN128\_2 & 8.854 & 15.149 & 17.763 & 20.178 & 18.967 & 6.031 & 11.024 & 12.492 & 13.025 & 13.550 \\ DNN128\_4 & 9.081 & 14.575 & 22.282 & 19.043 & 23.114 & 6.160 & 11.014 & 12.783 & 12.955 & 13.493 \\ DNN256\_2 & 8.812 &2. DNN16_2 ensemble learning achieved the best MAPE result for 10 min ahead prediction (14.12%).