Understanding Pedestrian Dynamics using Machine Learning with Real-Time Urban Sensors

Asher, M., Oswald, Y. and Malleson, N. orcid.org/0000-0002-6977-0615 (2025) Understanding Pedestrian Dynamics using Machine Learning with Real-Time Urban Sensors. Environment and Planning B: Urban Analytics and City Science. ISSN 2399-8083

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Item Type: Article
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© The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Keywords: Machine learning; Urban Analytics; Random Forest; Footfall; Pedestrian dynamics; Modelling
Dates:
  • Published (online): 17 February 2025
  • Accepted: 13 January 2025
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)
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Grant number
EU - European Union
757455
Depositing User: Symplectic Publications
Date Deposited: 15 Jan 2025 13:33
Last Modified: 07 Mar 2025 16:06
Published Version: https://journals.sagepub.com/doi/full/10.1177/2399...
Status: Published online
Publisher: SAGE Publications
Identification Number: 10.1177/23998083251319058
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