# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 7 Conclusions  
This research introduces a robust framework for travel time prediction, integrating the DeepST-Net architecture, a Deep Spatiotemporal Neural Network with advanced spatiotemporal data refinement techniques. As part of an ablation study, we systematically evaluated models based on the same architecture but utilizing varying levels of data refine ment, providing a detailed analysis of how preprocessing strategies influence predictive performance. The findings highlight the critical role of data quality in capturing complex spatiotemporal patterns, enhancing model accuracy and robustness. Furthermore, the training dynamics observed across different configurations validate these conclusions, demonstrating the importance of high-quality data preprocessing in achieving stable and reliable model performance. Our model DeepST-Net exhibited strong predictive performance by effectively integrating temporal and pattern-based features, as demonstrated in the ablation study. Beyond these evaluations, the model was benchmarked against established approaches, including MURAT, ST-NN, JSTC, and XGB-IN, to further assess its effectiveness. DeepST-Net consistently outperformed these models across multiple metrics, achieving a MAE of 59.27 s, which is significantly lower than that of the benchmark methods. These results emphasize the advantages of combining advanced data refinement techniques with a robust deep learning architecture. By improving predictive accuracy and stability, DeepST-Net highlights its potential to address key challenges in transportation systems, from optimizing urban mobility to advancing intelligent transportation solutions. While the evaluation was conducted on a specific dataset, further validation across diverse datasets and geographic contexts are essential in establishing the generalizability and broader applicability of our approach. Additionally, external factors such as weather conditions, road infrastructure, and real-time traffic incidents were not included due to data limitations. Incorporating these variables in future studies, alongside the consideration of sensor errors and real-time traffic data, could significantly enhance the predictive accuracy and utility of travel time prediction systems in more complex urban environments. In conclusion, this study provides meaningful contributions to the field of travel time prediction, emphasizing the importance of spatiotemporal data refinement, feature engineering, and model design. These findings not only advance the field of travel time prediction but also contribute to the broader adoption of AI-driven solutions for improving urban mobility and reducing congestion. The insights gained lay a strong foundation for future advancements in intelligent transportation systems, with the potential to significantly enhance urban mobility and operational efficiency in real-world applications. Conceptualization, A.B. and A.-C.C. ; methodology, B.B. ; software, A.B. ; validation, A.B., A.-C.C. and B.B. ; formal analysis, B.B. ; investigation, A.B. ; resources, B.B. ; data curation, A.B. ; writing--original draft preparation, A.B. ; writing--review and editing, A.B and G.D.; visualization, A.B. ; supervision, A.-C.C. ; project administration, A.-C.C. ; funding acquisition, G.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion, MICIU/AEI/10.13039/501100011033, grant number PID20201-122215 NB-C32; the European Regional Development Fund, EU grant numbers PID2021-122215 NB-C32 and 2022-GRIN-34113; and the University of Castilla-La Mancha grant number 2022-GRIN-34113. Additionally, this research is supported by the Algerian Ministry of Higher Education and Scientific Research under the PRFU project C00L07 UN250220220005, titled \"Integration of Intelligent Systems in Public Environments\" (2022-2026). The data used in this study were sourced from publicly accessible repositories.