Abstract

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes (hubs), ensuring that the student model prioritizes the most information-dense spatiotemporal regions. Furthermore, we develop a multi-level distillation strategy that aligns both intermediate features and final predictions through contrastive learning and attention mechanisms. Extensive experiments conducted on four real-world datasets demonstrate that DHKD significantly reduces inference latency while maintaining state-of-the-art performance, validating its viability for deployment on resource-constrained Internet of Vehicles (IoV) edge devices.

Department(s)

Computer Science

Publication Status

Early Access

Keywords and Phrases

graph neural networks; knowledge distillation; Traffic forecasting

International Standard Serial Number (ISSN)

2327-4662

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026

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