## 6 Conclusions
### Future Research Directions  
Based on the findings and limitations of this study, we have identified the following future research directions:  
(1) Methodological innovation and optimization: Conduct in-depth research on the randomness problem in deep learning models and explore more advanced hyperparameter optimization methods such as Bayesian optimization and genetic algorithms to optimize multiple hyperparameters simultaneously. Meanwhile, develop multi-objective hyperparameter optimization methods for multiple evaluation metrics (such as MAPE and RMSE) to enable selected models to achieve good performance on multiple metrics simultaneously. Furthermore, design more complex ensemble learning frameworks, including introducing more types of base learners (such as GRU and spatiotemporal convolutional networks), adaptive weight allocation mechanisms, or multi-level ensemble strategies to better integrate the advantages of different models. (2) Model applicability and generalization capability improvement: Validate the proposed methods on more urban road segments with different characteristics to examine their generalization capability and robustness. Meanwhile, introduce more external factors related to travel time, such as weather conditions, traffic control information, and traffic incident records, to explore the impact of these factors on prediction performance. Research how to extend the proposed methods to online learning scenarios, enabling models to dynamically update parameters based on newly arrived data to adapt to changes in traffic patterns, and explore prediction uncertainty quantification methods to provide prediction intervals or probability distributions. (3) Practical application and system integration: Explore the application of the proposed methods in actual intelligent transportation systems and research how to effectively integrate prediction results into traffic management and travel planning systems. Develop specialized models for different application scenarios, such as customized prediction systems for logistics delivery, ride-hailing dispatch, and shared mobility services. Meanwhile, establish standardized evaluation frameworks and benchmark datasets to provide unified standards for method comparison in the traffic prediction field, promoting standardized development and technological advancement in this field. Y.W. : conceptualization, methodology, formal analysis, writing--original draft, investigation, writing--review and editing. Y.L. : conceptualization, methodology, data curation, visualization, writing--review and editing. X.Y. : conceptualization, supervision, writing--review and editing, project administration. All authors have read and agreed to the published version of the manuscript. This study is supported by the National Natural Science Foundation of China (General Program), Research on Basic Problems of Vehicle-Infrastructure Cooperative Traffic Control for Special Vehicles (52472350); the China Postdoctoral Science Foundation, Cooperative Optimization on Right-of-Way at Signalized Intersections in Heterogeneous traffic Environment (2022M712410); the Guangxi Major Science and Technology Special Subproject, Reutilization of Pinglu Canal Cross-Line Bridges and Optimization of Traffic Organization (2023 AA14006). Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. All authors are grateful for the resources provided by the Intelligent Transportation System Research Center of Tongji University. The authors declare no conflicts of interest.