ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs

Aslam, N.S., Ibrahim, M.R. orcid.org/0000-0001-7733-7777, Cheng, T. et al. (2 more authors) (2021) ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs. Geo-spatial Information Science, 24 (4). pp. 711-721. ISSN 1009-5020

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Item Type: Article
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© 2021 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: Trip purpose prediction; smart card data; POIs; neural networks; machine learning
Dates:
  • Published: 15 October 2021
  • Published (online): 15 October 2021
  • Accepted: 21 September 2021
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)
Depositing User: Symplectic Publications
Date Deposited: 15 Jul 2024 12:20
Last Modified: 15 Jul 2024 12:20
Status: Published
Publisher: Taylor & Francis
Identification Number: 10.1080/10095020.2021.1985943
Related URLs:
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 11: Sustainable Cities and Communities
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