A spatiotemporal transfer learning framework with mixture of experts for traffic flow prediction

For traffic management entities, the ability to forecast traffic patterns is crucial to their suite of advanced decision-making solutions. The inherent unpredictability of network traffic makes it challenging to develop a robust predictive model. For this reason, by leveraging a spatiotemporal graph transformer equipped with an array of specialized experts, ensuring more reliable and agile outcomes. In this method, utilizing Louvain algorithm alongside a temporal segmentation approach partition the overarching spatial graph structure of traffic networks into a series of localized spatio-temporal graph subgraphs. Then, multiple expert models are obtained by pre-training each subgraph data using a spatio-temporal synchronous graph transformer. Finally, each expert model is fused in a fine-tuning way to obtain the final predicted value, which ensures the reliability of its forecasts while reducing computational time, demonstrating superior predictive capabilities compared to other state-of-the-art models. Results from simulation experiments on real datasets from PeMS validate its enhanced performance metrics.