Graph convolutional network approach applied to predict hourly bikesharing demands considering spatial, temporal, and global effects (PLoS ONE (2019) 14:9 (e0220782) DOI: 10.1371/journal.pone.0220782)

Tae San Kim; Won Kyung Lee; So Young Sohn

doi:10.1371/journal.pone.0266221

Graph convolutional network approach applied to predict hourly bikesharing demands considering spatial, temporal, and global effects (PLoS ONE (2019) 14:9 (e0220782) DOI: 10.1371/journal.pone.0220782)

Tae San Kim, Won Kyung Lee, So Young Sohn

Department of Industrial Engineering

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

The following information is missing from the Data Availability Statement: The meteorological data underlying this study are available from https://www.weather.go.kr/w/obs-climate/ land/past-obs/obs-by-day.do?stn=108&yy=2016&mm=4&obs=1. The following information is missing from the Funding statement: In addition to 2019R1H1A207, this research was partly supported by 'Geospatial Big Data Management, Analysis and Service Platform Technology Development' project for the MOLIT (The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA (Korea Agency for Infrastructure Technology Advancement) (18NSIP-B081011-05). This work is an updated and extended version of the early local presentation made at KOREA INFORMATION SCIENCE SOCIETY conference by the same authors: "Bike Sharing Demands Prediction based on GCN", Proceedings of the KISS Conference p832-834, 2018.

Original language	English
Article number	e0266221
Journal	PloS one
Volume	17
Issue number	3 March
DOIs	https://doi.org/10.1371/journal.pone.0266221
Publication status	Published - 2022 Mar

Bibliographical note

Publisher Copyright:
© 2022 San Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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General

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10.1371/journal.pone.0266221

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title = "Graph convolutional network approach applied to predict hourly bikesharing demands considering spatial, temporal, and global effects (PLoS ONE (2019) 14:9 (e0220782) DOI: 10.1371/journal.pone.0220782)",

abstract = "The following information is missing from the Data Availability Statement: The meteorological data underlying this study are available from https://www.weather.go.kr/w/obs-climate/ land/past-obs/obs-by-day.do?stn=108&yy=2016&mm=4&obs=1. The following information is missing from the Funding statement: In addition to 2019R1H1A207, this research was partly supported by 'Geospatial Big Data Management, Analysis and Service Platform Technology Development' project for the MOLIT (The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA (Korea Agency for Infrastructure Technology Advancement) (18NSIP-B081011-05). This work is an updated and extended version of the early local presentation made at KOREA INFORMATION SCIENCE SOCIETY conference by the same authors: {"}Bike Sharing Demands Prediction based on GCN{"}, Proceedings of the KISS Conference p832-834, 2018.",

author = "Kim, {Tae San} and Lee, {Won Kyung} and Sohn, {So Young}",

note = "Publisher Copyright: {\textcopyright} 2022 San Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

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Graph convolutional network approach applied to predict hourly bikesharing demands considering spatial, temporal, and global effects (PLoS ONE (2019) 14:9 (e0220782) DOI: 10.1371/journal.pone.0220782). / Kim, Tae San; Lee, Won Kyung; Sohn, So Young.

In: PloS one, Vol. 17, No. 3 March, e0266221, 03.2022.

Research output: Contribution to journal › Comment/debate › peer-review

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N2 - The following information is missing from the Data Availability Statement: The meteorological data underlying this study are available from https://www.weather.go.kr/w/obs-climate/ land/past-obs/obs-by-day.do?stn=108&yy=2016&mm=4&obs=1. The following information is missing from the Funding statement: In addition to 2019R1H1A207, this research was partly supported by 'Geospatial Big Data Management, Analysis and Service Platform Technology Development' project for the MOLIT (The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA (Korea Agency for Infrastructure Technology Advancement) (18NSIP-B081011-05). This work is an updated and extended version of the early local presentation made at KOREA INFORMATION SCIENCE SOCIETY conference by the same authors: "Bike Sharing Demands Prediction based on GCN", Proceedings of the KISS Conference p832-834, 2018.

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Graph convolutional network approach applied to predict hourly bikesharing demands considering spatial, temporal, and global effects (PLoS ONE (2019) 14:9 (e0220782) DOI: 10.1371/journal.pone.0220782)

Abstract

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