Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

Yang, Y, Heppenstall, A orcid.org/0000-0002-0663-3437, Turner, A et al. (1 more author) (2020) Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systems, 83. 101521. ISSN 0198-9715

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: Crown Copyright © 2020 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Keywords: Bike-sharing; Traffic prediction; Graph theory; Urban dynamics; Sustainable transport
Dates:
  • Accepted: 26 June 2020
  • Published (online): 13 July 2020
  • Published: September 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
Funding Information:
FunderGrant number
NERC (Natural Environment Research Council)NE/S009124/1
ESRC (Economic and Social Research Council)ES/R007918/1
Depositing User: Symplectic Publications
Date Deposited: 16 Jul 2020 14:42
Last Modified: 04 Jan 2021 11:46
Status: Published
Publisher: Elsevier BV
Identification Number: https://doi.org/10.1016/j.compenvurbsys.2020.101521
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