# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 6 Results and Discussions
### Evaluation Against Established Models
The findings from the ablation study explicitly demonstrate that these preprocessing steps are critical in removing noise and inconsistencies from the training data, thereby ensuring more accurate and reliable predictions. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **MAE (s)** & **MAPE (\%)** & **RMSE (s)** \\ \hline MURAT & 139.44 & 22.32 & / \\ ST-NN & 136.34 & 20.04 & / \\ JSTC & 97.12 & 13.14 & / \\ XGB-IN & 94.76 & 17.02 & 130.63 \\
**DeepST-Net** & **59.27** & **9.73** & **89.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of model performances. Study findings versus established models in the literature. Bold text highlights the optimal values in each evaluated metric. The XGB-IN model, which achieves a MAE of 94.76 s, MAPE of 17.02%, and RMSE of 130.63 s, shows promising predictive accuracy, coming close to JSTC. The inclusion of RMSE in the evaluation provides valuable insight into its performance, demonstrating that XGB-IN generates precise travel time predictions. This improvement can be attributed to its use of XGBoost regression, which leverages an ensemble of decision trees to capture non-linear relationships within the data. Despite its competitive performance, XGB-IN inherits several key limitations from ST-NN, as it follows its same preprocessing methods. Hence, XGB-IN lacks comprehensive data refinement mechanisms, while the ensemble nature of XGBoost helps it resist outliers, the absence of a systematic preprocessing pipeline limits its ability to fully capitalize on clean, high-quality data. As a result, XGB-IN may struggle to handle inconsistencies in the data, potentially leading to biased learning when exposed to noisy or unprocessed inputs. Additionally, XGB-IN focuses on static travel time prediction by applying isolated XGBoost regression models to predict travel times for inlier and extreme-conditioned trips separately, while this approach may work well for partitioning distinct travel patterns, it lacks a unified learning framework that can capture the interdependencies between various trip conditions. This stands in contrast to DeepST-Net, which employs a fully connected deep learning architecture designed to model both local and global spatiotemporal dependencies within a single unified framework, enabling the model to effectively learn and generalize across a broader range of urban traffic conditions. DeepST-Net outperforms all other models in predictive accuracy, achieving a MAE of 59.27 s, a MAPE of 9.73%, and a RMSE of 89.63 s. This significant reduction in errors highlights the model's ability to effectively capture complex spatiotemporal patterns and dependencies in the data. By leveraging temporal features and utilizing a deep neural network architecture, DeepST-Net delivers highly accurate travel time predictions. Furthermore, the consistently low variability across multiple runs underscores its robustness and reliability in diverse scenarios. In conclusion, the key takeaway from this work is that these performance improvements are driven by the synergy between the model design and the quality of the data. Through our ablation study and training dynamics analysis, we demonstrate how both factors contribute to the overall success of DeepST-Net in urban travel time prediction. The results underscore the practical implications of these advancements, as the lower metrics achieved by DeepST-Net translate to significant reductions in travel time estimation errors. This improvement enables more precise scheduling for logistics and delivery platforms, optimizing routes, reducing operational costs, and enhancing customer satisfaction through accurate estimated arrival times. For autonomous vehicles, the model's enhanced predictive accuracy facilitates dynamic route planning, allowing real-time adaptation to traffic conditions, which improves traffic flow and overall safety.