## 2 Literature Review
### Research Gaps and Prospects  
Through the above literature review, it can be found that existing research has achieved rich results in urban road travel time prediction, while also providing space for further research exploration:  
(1) The systematicity of method comparison needs to be strengthened. The existing literature mostly focuses on the improvement and application of specific methods, such as network structure optimization of deep learning methods or parameter adjustment of traditional methods, but there are relatively few studies that systematically compare different categories of methods under the same experimental conditions. This leaves researchers and practitioners lacking direct reference basis when facing method selection. (2) Insufficient attention to the stability of deep learning model evaluation. Although the application of deep learning in traffic prediction is becoming increasingly widespread, most studies use single training results for model evaluation, with relatively insufficient attention to the result variability that random factors may cause during the training process. This may affect the reliability of research conclusions to some extent. (3) Multi-dimensional performance evaluation analysis is relatively limited. Existing research often conducts model comparisons based on single or few evaluation metrics, with insufficient analysis of the differences that the same model may exhibit under different evaluation dimensions, which may limit comprehensive understanding of model applicability. (4) There is still room for exploration of ensemble learning strategies. Although existing research has explored the application of ensemble learning in traffic prediction, most focus on combinations of similar models, with relatively little exploration of heterogeneous model ensemble strategies, particularly the combination of deep learning and traditional methods. Based on the above analysis, this study conducted the following work:  
(1) Establish a comprehensive method comparison framework. This study constructed a unified experimental framework that compares the performance of deep learning methods and shallow learning methods in urban road travel time prediction. By designing multiple scenarios covering different sliding time window lengths and prediction scales, comprehensive empirical analysis is provided for the relative advantages and applicability of the two types of methods. (2) Implement robust model evaluation strategies. In response to the random variability of deep learning model training results, this study adopted multi-training statistical analysis strategies. By conducting multiple independent training runs for each hyperparameter configuration and calculating statistical indicators, the stability and reliability of model performance evaluation are ensured. The systematic application of this strategy provides valuable methodological reference for related research. (3) Design heterogeneous model ensemble learning schemes. This study explored ensemble learning frameworks that combine the advantages of shallow learning and deep learning methods. By selecting base learners with complementary characteristics and diversified meta-learners, the application potential of ensemble strategies in urban road travel time prediction is validated. (4) Conduct multi-metric dimensional performance analysis. By simultaneously using two representative metrics, MAPE and RMSE, for model evaluation, this study reveals the performance characteristics of different models under different evaluation dimensions, providing important reference for the comprehensiveness and accuracy of model evaluation. Analyze the impact patterns of prediction configurations. Through systematic design of different combinations of sliding time window lengths and prediction scales, this study conducted in-depth analysis of the impact patterns of these key parameters on the performance of different types of models, providing scientific basis for optimal configuration selection in practical applications.