TY - JOUR
T1 - Online Transfer Learning with MLP-Assisted Graph Convolution Network for Traffic Flow Forecasting: A Solution for Edge Intelligent Devices
AU - Sun, Jingru
AU - Lu, Chendingying
AU - Sun, Yichuang
AU - Jiang, Hongbo
AU - Xiao, Zhu
PY - 2025/3/3
Y1 - 2025/3/3
N2 - Traffic flow prediction is crucial for intelligent transportation, aiding route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglect external influences, and practical issues like resource constraints and data sparsity on edge devices. We propose an Online Transfer Learning with Multi-layer Perceptron (MLP) assisted Graph Convolution Network (GCN) framework (OTL-GM), consisting of two parts: transferring source domain features to edge devices and using online learning to bridge domain gaps. Experiments on 4 datasets demonstrate OTL’s effectiveness, and compared with the model without OTL, convergence time of the OTL-model increased from 24.77% to 95.32%.
AB - Traffic flow prediction is crucial for intelligent transportation, aiding route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglect external influences, and practical issues like resource constraints and data sparsity on edge devices. We propose an Online Transfer Learning with Multi-layer Perceptron (MLP) assisted Graph Convolution Network (GCN) framework (OTL-GM), consisting of two parts: transferring source domain features to edge devices and using online learning to bridge domain gaps. Experiments on 4 datasets demonstrate OTL’s effectiveness, and compared with the model without OTL, convergence time of the OTL-model increased from 24.77% to 95.32%.
M3 - Article
SN - 2095-9184
JO - Frontiers of Information Technology & Electronic Engineering
JF - Frontiers of Information Technology & Electronic Engineering
ER -