A Knowledge Distillation-Based Framework for Enhanced Long and Short Term Road Traffic Prediction

Accurate traffic congestion prediction is essential for optimizing urban traffic management and mitigating congestion and its consequences. However, conventional prediction models often struggle to simultaneously capture long-term periodic patterns and short-term fluctuations, leading to low prediction accuracy and computational inefficiencies. To overcome this limitation, we propose a knowledge distillation-based framework for enhanced long and short term road traffic prediction. The framework employs a teacher-student architecture, where the teacher model utilizes long-term historical data and a dynamic adjacency matrix to extract periodic traffic patterns, while the student model captures short-term variations and integrates distilled long-term knowledge to enhance responsiveness to sudden congestion changes. To resolve the dimensional mismatch between long-term and short-term feature representations, we introduce a feature alignment mechanism that reduces the dimensionality of high-dimensional intermediate outputs from the teacher model. Experimental evaluations demonstrate that our approach significantly outperforms baseline models, such as Graph Convolutional Gated Recurrent Units, Spatio- Temporal Graph Convolutional Networks and Long Short-Term Memory, in terms of Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error. Moreover, the proposed framework maintains high prediction accuracy even in scenarios with severe traffic fluctuations, offering an efficient and robust solution for traffic congestion forecasting.