# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 1 Introduction  
Transportation plays a vital role in modern society, facilitating the movement of people and goods across geographic regions. The reliability of transportation systems is crucial for commuters, as it directly impacts their ability to meet daily obligations such as work, school attendance, and other engagements. Reliability, in this context, refers to the consistency of travel time, which can be influenced by factors such as traffic congestion, weather conditions, and maintenance issues [1]. Accurate travel time prediction is crucial for modern transportation systems, benefiting both individuals and industries in numerous ways. By improving scheduling and planning efficiency, it enables significant time and cost savings. Moreover, it aids commuters and travelers to make informed decisions, reducing the likelihood of missed appointments or delays, as demonstrated by [PERSON] et al. [2]. Furthermore, precise travel time predictions play a pivotal role in enhancing traffic flow, effectively mitigating congestion on roads and highways, and reducing emissions from idling vehicles. This not only saves time for drivers but also promotes environmental sustainability, aligning with the urgent need to address climate change and air pollution challenges. The impact of travel time prediction models extends well beyond individual commuters, encompassing various sectors and the broader economy. Emergency services,for instance, can greatly benefit from reduced response times and optimized resource allocation. By accurately estimating travel times, emergency responders can swiftly reach their destinations, potentially saving lives and minimizing property damage. Additionally, logistics and delivery companies can optimize their operations by planning efficient routes and schedules for their vehicles. This optimization leads to significant cost reductions and improved delivery times [3], enhancing overall efficiency and customer satisfaction. In recent years, Machine Learning (ML) has emerged as a powerful tool for advancing transportation planning and operations. A comprehensive review by [PERSON] et al. [4] found that 64% of the 75 surveyed studies on travel time prediction utilized ML methods, particularly Artificial Neural Networks (ANNs). ANNs, a subset of ML algorithms inspired by the human brain's architecture [5], have demonstrated exceptional capabilities in modeling complex transportation systems, especially in urban areas with highly dynamic and heterogeneous traffic conditions [6; 7; 8; 9; 10; 11]. Their ability to process vast datasets and learn intricate patterns makes them particularly suited for travel time prediction. The availability of big data has significantly enhanced the potential of ML and ANNs in this domain. The exponential increase in data from diverse sources such as smart vehicles, mobile devices, traffic cameras, and sensors provides an unprecedented opportunity to improve transportation services. However, the presence of noise and outliers in such large datasets can reduce the predictive accuracy of the models [12]. Effective data preprocessing, including cleaning, feature selection, and scaling, is therefore critical for achieving high model performance. Despite significant advancements, existing studies often lack clarity on which aspects of their methodologies, such as data preprocessing or model optimization, most significantly influence performance. This gap highlights the need for a systematic approach that explicitly connects data refinement and categorization with model optimization. Effective preprocessing ensures high-quality input data, while categorization captures the spatiotemporal patterns essential for accurate predictions. In this study, we propose a comprehensive framework that incorporates advanced spatiotemporal data refinement techniques and categorization strategies to enhance the predictive capabilities of a deep learning models.