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Online transfer learning with an MLP-assisted graph convolutional network for traffic flow forecasting: a solution for edge intelligent devices
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Jingru SUN1, Chendingying LU1, Yichuang SUN2, Hongbo JIANG1, Zhu XIAO1. Online transfer learning with an MLP-assisted graph convolutional network for traffic flow forecasting: a solution for edge intelligent devices[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Online transfer learning with an MLP-assisted graph convolutional network for traffic flow forecasting: a solution for edge intelligent devices",
author="Jingru SUN1, Chendingying LU1, Yichuang SUN2, Hongbo JIANG1, Zhu XIAO1",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2401059"
}
%0 Journal Article
%T Online transfer learning with an MLP-assisted graph convolutional network for traffic flow forecasting: a solution for edge intelligent devices
%A Jingru SUN1
%A Chendingying LU1
%A Yichuang SUN2
%A Hongbo JIANG1
%A Zhu XIAO1
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2401059
TY - JOUR
T1 - Online transfer learning with an MLP-assisted graph convolutional network for traffic flow forecasting: a solution for edge intelligent devices
A1 - Jingru SUN1
A1 - Chendingying LU1
A1 - Yichuang SUN2
A1 - Hongbo JIANG1
A1 - Zhu XIAO1
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2401059
Abstract: 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) with multi-layer perceptron (MLP)-assisted graph convolutional 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 four datasets demonstrate OTLs effectiveness, and in a comparison with a model without OTL, convergence time of the OTL-model increased from 24.77% to 95.32%.
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