Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques

In the evolving field of Intelligent Transportation Systems (ITSs), accurate and reliable traffic prediction is essential in enhancing management and planning capabilities. Accurately predicting traffic conditions over both short-term and long-term intervals is vital for the practical application of ITS. The integration of deep learning into traffic prediction has proven crucial in advancing traffic prediction beyond traditional approaches, particularly in analyzing and forecasting complex traffic scenarios. Despite these advancements, the existing methods are unable to effectively handle both short-term and long-term traffic patterns given their complex nature, revealing a need for more comprehensive forecasting solutions. To address this need, we propose a new approach named the Short-Term and Long-Term Integrated Transformer (SLIT). SLIT is a Transformer-based encoder–decoder architecture, designed for the effective prediction of both short-term and long-term travel time durations. The architecture integrates the Enhanced Data Preprocessing (EDP) with the Short-Term and Long-Term Integrated Encoder–Decoder (SLIED). This harmonious combination enables SLIT to effectively capture the complexities of traffic data over varying time horizons. Extensive evaluations on a large-scale real-world traffic dataset demonstrate the excellence of SLIT compared with existing competitive methods in both short- and long-term travel time predictions across various metrics. SLIT exhibits significant improvements in prediction results, particularly in short-term forecasting. Remarkable improvements are observed in SLIT, with enhancements of up to 9.67% in terms of all evaluation metrics across various time horizons. Furthermore, SLIT demonstrates the capability to analyze traffic patterns across various road complexities, proving its adaptability and effectiveness in diverse traffic scenarios with improvements of up to 10.83% in different road conditions. The results of this study highlight the high potential of SLIT in significantly enhancing traffic prediction within ITS.