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Binkun LIU, Yu KANG, Yang CAO, Yunbo ZHAO, Zhenyi XU. Transfer learning with spatiotemporal graph convolution network for city flow prediction[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Transfer learning with spatiotemporal graph convolution network for city flow prediction",
author="Binkun LIU, Yu KANG, Yang CAO, Yunbo ZHAO, Zhenyi XU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300571"
}
%0 Journal Article
%T Transfer learning with spatiotemporal graph convolution network for city flow prediction
%A Binkun LIU
%A Yu KANG
%A Yang CAO
%A Yunbo ZHAO
%A Zhenyi XU
%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.2300571
TY - JOUR
T1 - Transfer learning with spatiotemporal graph convolution network for city flow prediction
A1 - Binkun LIU
A1 - Yu KANG
A1 - Yang CAO
A1 - Yunbo ZHAO
A1 - Zhenyi XU
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.2300571
Abstract: Recently, deep learning based city-flow prediction has been extensively used in the establishment of smart cities. These methods are data-hungry, making them unscalable to areas lacking data. Although transfer learning can use data-rich source domains to assist target domain cities in city flow prediction, the performance of existing methods cannot meet the needs of actual use, because the long-distance road network connectivity is ignored. To solve this problem, we propose a transfer learning method based on spatiotemporal graph convolution, in which we construct a co-occurrence space between the source and target domains, and then align the mapping of the source and target domains’ data in this space, to achieve the transfer learning of source city-flow prediction model on the target domain. Specifically, a dynamic spatiotemporal graph convolution module along with a temporal encoder is devised to simultaneously capture the concurrent spatiotemporal features, which implies the inherent relationship among the road network structures, human travel habits, and city bike flow. Then, these concurrent features are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence space. The target domain features are thereby aligned with the source domain features in the co-occurrence space by using a Mahalanobis Distance loss, to achieve cross-city bike-flow prediction. The proposed method is evaluated on the public bike flow datasets in Chicago, New York, and Washington in 2015, and significantly outperforms state-of-the-art techniques.
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