Online transfer learning with an MLP-assisted graph convolutional network for traffic flow prediction: a solution for edge intelligent devices
Jingru SUN , Chendingying LU , Yichuang SUN , Hongbo JIANG , Zhu XIAO
Front. Inform. Technol. Electron. Eng ›› 2025, Vol. 26 ›› Issue (9) : 1692 -1710.
Online transfer learning with an MLP-assisted graph convolutional network for traffic flow prediction: a solution for edge intelligent devices
Traffic flow prediction is crucial for intelligent transportation and aids in route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices. We propose an online transfer learning (OTL) framework with a multi-layer perceptron (MLP)-assisted graph convolutional network (GCN), termed OTL-GM, which consists of two parts:transferring source-domain features to edge devices and using online learning to bridge domain gaps. Experiments on four data sets demonstrate OTL's effectiveness; in a comparison with models not using OTL, the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.
Online transfer learning / Traffic prediction / Intelligent edge devices
Zhejiang University Press
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