# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 6 Results and Discussions
### Ablation Study Findings  
The study's results, detailed in Table 1, provide a comprehensive assessment of model performance across different configurations using evaluation metrics MAE, MAPE,and RMSE. These metrics are calculated based on 10 independent runs for each model, with different data seeds employed to ensure robustness and reliability. The table presents both the mean and standard deviation of each metric across the 10 runs, along with the best-performing single run for each model, offering a thorough comparison of performance stability and peak efficiency. The model trained on raw data exhibited the poorest performance across all metrics, with an average MAE of \(392.38\pm 5.34\) s, a MAPE of \(56.02\pm 1.23\%\), and a RMSE of \(538\pm 6.98\) s. Even in its best-performing run, the model achieved a MAE of \(378.58\) s, which remains substantially higher than models trained with refined data. These results highlight the inherent limitations of using unprocessed data for travel time prediction. Raw data often include noise, irrelevant variations, and inconsistencies that obscure underlying patterns and relationships critical for accurate predictions. The relatively consistent variability across runs, indicated by the low standard deviations, underscores the fact that while the model's inadequacies are stable, the absence of preprocessing renders it incapable of adapting to the complexities of urban travel environments. This finding reinforces the need for robust preprocessing techniques to mitigate noise and improve data quality as a foundational step in predictive modeling. In contrast, the model trained on clean data demonstrated significant improvements across all metrics, with an average MAE of \(145.43\pm 3.55\) s, a MAPE of \(21.91\pm 0.58\%\), and a RMSE of \(276.84\pm 3.73\) s. The reduced error rates and narrower standard deviations reflect the positive impact of data cleaning techniques, such as noise reduction, outlier removal, and data normalization, on predictive performance and model reliability. The best-performing run achieved a MAE of \(142.19\) s, underscoring the critical role of preprocessing in addressing data inconsistencies and enhancing the predictive capacity of the model. These improvements are particularly notable in the context of urban datasets, which are often characterized by high variability due to diverse traffic conditions and dynamic spatiotemporal factors. The clean data model's results confirm that even basic preprocessing steps can substantially improve model outcomes by providing a cleaner and more representative dataset. The temporal model, which incorporates temporal clustering to account for time-dependent patterns, outperformed the clean data model, achieving an average MAE of \(93.33\pm 3.73\) s, a MAPE of \(13.20\pm 0.61\%\), and a RMSE of \(148.06\pm 2.49\) s. The integration of temporal clustering allows the model to capture periodic variations in travel times, such as daily, weekly, or seasonal trends, which are crucial in urban settings with dynamic traffic patterns. The relatively low standard deviations across metrics indicate that temporal clustering not only enhances accuracy but also contributes to consistent performance. By segmenting the dataset into meaningful temporal intervals, the model effectively adapts to the evolving nature of urban travel behavior, demonstrating its capacity to improve predictive reliability in complex real-world scenarios. These findings highlight the impor