Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks

Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 22; H. 12; S. 7474 - 7484
Hauptverfasser:	Yao, Xin, Gao, Yong, Zhu, Di, Manley, Ed, Wang, Jiaoe, Liu, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Biological system modeling Convolution Data collection data imputation Data models graph convolution graph embedding Graph neural networks Gravity Neural networks Origin-destination flow Predictive models Sampling methods Spatial databases spatial interaction network Structured data Training
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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