Efficient Traffic Prediction Through Spatio-Temporal Distillation
This work addresses efficiency problems for real-life traffic prediction applications, offering a significant speed-up over existing methods.
The paper tackled the scalability and over-smoothing issues in graph neural networks for traffic flow prediction by proposing LightST, a knowledge distillation method that transfers spatio-temporal knowledge from a teacher to a student model, resulting in 5X to 40X speed improvements while maintaining superior accuracy.
Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations through a graph-based message-passing framework. Although GNNs have shown great promise in handling traffic datasets, their deployment in real-life applications has been hindered by scalability constraints arising from high-order message passing. Additionally, the over-smoothing problem of GNNs may lead to indistinguishable region representations as the number of layers increases, resulting in performance degradation. To address these challenges, we propose a new knowledge distillation paradigm termed LightST that transfers spatial and temporal knowledge from a high-capacity teacher to a lightweight student. Specifically, we introduce a spatio-temporal knowledge distillation framework that helps student MLPs capture graph-structured global spatio-temporal patterns while alleviating the over-smoothing effect with adaptive knowledge distillation. Extensive experiments verify that LightST significantly speeds up traffic flow predictions by 5X to 40X compared to state-of-the-art spatio-temporal GNNs, all while maintaining superior accuracy.