Periodic Stacked Transformer-based Framework for Travel Time Prediction

Travel time analysis and prediction play critical roles in developing Intelligent Transportation Systems (ITS), which have attracted significant interests from the research community. Deep learning-based methodologies have proven to be powerful tools in utilizing big data for predicting travel times. However, while most studies have focused on short-term predictions, predicting travel times over longer periods is equally important for wide applications like traffic management and route planning. Long-term prediction, which often receives less attention due to its complexity, remains a gap in current researches. To address this challenge, we propose the Periodic Stacked Transformer (PS-Transformer), a novel Transformer-based framework designed to enhance both short and long-term traffic predictions. PS-Transformer consists of two primary modules: the Segment Encoding Integration (SEI) and the Periodic Stacked Encoder-Decoder (PSED). SEI module extracts periodic patterns from traffic data, while PSED effectively captures short-term and long-term dependencies from temporal attributes. Additionally, PSED tackles error accumulation, a common issue in extended prediction periods, through its non-autoregressive decoder design. Our PS-Transformer is validated through a series of experiments on a real-world dataset, demonstrating its capability in multi-step predictions that provide forecasts over an extended duration. Empirical evaluation results show that PS-Transformer outperforms state-of-the-art methods in both short and long-term travel time predictions across various metrics, including MAE, RMSE, and SMAPE.