# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 6 Results and Discussions
### Evaluation Against Established Models  
In this section, we compare the performance of our proposed DeepST-Net model with several established models from the literature, all evaluated on the same dataset. Table 2 presents a comprehensive analysis of their performance, allowing us to assess DeepST-Net's effectiveness in travel time prediction. The selected models encompass diverse methodological approaches to urban travel time estimation, as follows:  
* **MURAT**[19] **(MUlti-task Representation learning model for Arrival Time):** This model employs a multi-task learning framework that simultaneously predicts several aspects of travel, leveraging shared representations to enhance the prediction of arrival times. * **ST-NN**[7] **(Spatio-Temporal Neural Network):** Leverages a unified neural network approach to estimate both travel time and distance for taxi trips. Figure 9: Models’ performance during training and validation. * **XGB-IN [20] (XGBoost prediction model of in-lier trips):** This model applies the XGBoost algorithm to predict travel times, focusing specifically on inlier trips which follow typical patterns without significant anomalies. * **JSTC [21] (Joint Spatial-Temporal Correlation):** Introduces a mechanism to capture joint spatial-temporal correlations using self-convolutional attention and temporal convolutional networks. Among the evaluated models, MURAT achieves a MAE of 139.44 s, representing one of the higher error margins among the models considered. Similarly, the ST-NN model follows closely with a MAE of 136.34 s. Both models exhibit relatively high MAPE values of 22.32% and 20.04% respectively, indicating a notable percentage of error in their travel time predictions. These results reflect the general performance of these models when applied to the dataset and provide a baseline for assessing more advanced techniques. The higher error rates observed in MURAT and ST-NN can be attributed to their inherent methodological limitations. MURAT, as a multi-task learning model, is susceptible to task interference, where shared representations across multiple objectives can introduce noise, reducing prediction precision. ST-NN, while utilizing a spatiotemporal framework, relies on predefined spatial embeddings rather than dynamically learned spatial representations, limiting its ability to adapt to evolving traffic conditions and capture the complex, non-stationary nature of urban mobility. Additionally, ST-NN's shallower network architecture, while computationally efficient, lacks the depth to capture the intricate relationships in the data, compared to deeper models. Furthermore, ST-NN's lack of an extensive data refinement process makes it more vulnerable to errors from noisy or inconsistent input data, further hindering its performance. The JSTC model demonstrates a notable improvement in predictive accuracy, achieving a MAE of 97.12 s and a MAPE of 13.14%. This reduction in error compared to MURAT and ST-NN suggests the model's capability to better capture underlying patterns in the data. However, the absence of RMSE values for MURAT, ST-NN, and JSTC limits a more comprehensive evaluation of their overall performance, particularly in assessing variability and outlier sensitivity. In contrast, DeepST-Net's architecture is meticulously engineered to balance depth with computational efficiency, a feature not present in JSTC, which incurs higher computational costs due to its reliance on self-convolutional attention. DeepST-Net's design facilitates the efficient learning of complex dependencies without excessive parameterization, resulting in faster convergence and reduced computational overhead compared to more elaborate architectures. Furthermore, unlike JSTC, which primarily focuses on structural spatiotemporal correlations within trip sequences, DeepST-Net employs a comprehensive data preprocessing pipeline, which significantly enhance the model's performance.