# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 2 Related Works
### Prediction Method  
When examining the methods used for prediction, the following three primary categories emerge: historical methods, statistical methods, and ML methods.  
Historical methods [22; 23] base their predictions on past travel data, assuming that historical patterns will remain consistent in the future. While straightforward and effective in stable traffic environments, these methods struggle as traffic patterns evolve, particularly in rapidly changing urban areas where conditions are highly variable [9].  
Statistical methods [24; 25; 26; 27] address some of the limitations of historical approaches by relying on mathematical models to analyze traffic data. However, these methods often require precise parameter tuning and may fail to capture complex interrelationships among multiple variables, especially in multifaceted urban systems. Consequently, statistical models can inadequately represent the intricate relationships driving travel time, leading to inaccuracies [11].  
In contrast, ML methods [7; 8; 10; 19; 20; 21; 28; 29] provide a more flexible and powerful alternative for travel time prediction. ML models, particularly deep learning models, excel at handling large and complex datasets, allowing them to learn non-linear relationships between inputs and outputs that traditional methods often miss [30]. These models continuously improve as they are exposed to new data, making them highly adaptable to changing traffic conditions. Recent advancements in this domain have led to the integration of various ML techniques to enhance prediction accuracy. For instance, CNNs have been employed for spatial feature extraction, and LSTM networks have been used for temporal modeling, as demonstrated by [PERSON] et al. [8]. Additionally, hybrid models, such as those combining XGBoost with neural networks [20], and models leveraging self-convolutional attention mechanisms with temporal convolutional networks (TCNs) [21], highlight the ongoing evolution and sophistication of ML-based travel time prediction models.  
While these advancements have significantly improved travel time prediction, they often place a primary focus on architectural innovations while treating preprocessing and data refinement as secondary considerations. Preprocessing steps such as noise reduction, temporal clustering, and spatial feature engineering play a crucial role in determining the quality of input data, yet their impact on model performance remains underexplored. For example, [PERSON] et al. [8] effectively model spatial and temporal dependencies with a hybrid CNN-LSTM architecture, but they do not systematically analyze how preprocessing affects predictive accuracy. Similarly, frameworks like JSTC [21] and XGBoost-based models [20] achieve high performance but do not evaluate the contributions of specific preprocessing techniques to their results. This gap is particularly critical in urban environments, where data noise, heterogeneity, and dynamic patterns require careful handling to ensure robust and scalable predictions.  
To address these limitations, our work emphasizes the integration of advanced spatiotemporal data refinement into the predictive pipeline. We propose a comprehensive framework that combines systematic preprocessing techniques with DeepST-Net, a novel deep spatiotemporal neural network designed for travel time prediction. This framework evaluates the direct impact of data refinement on predictive performance, bridging the gap between preprocessing strategies and model architecture. By enhancing input data quality and leveraging the capabilities of DeepST-Net, the framework aims to provide a robust and scalable solution for travel time prediction in complex urban environments.