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Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale

Xucai Zhang, Yeran Sun Orcid Logo, Fangli Guan, Kai Chen, Frank Witlox, Haosheng Huang

Transportation Research Part C: Emerging Technologies, Volume: 143, Start page: 103854

Swansea University Author: Yeran Sun Orcid Logo

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DOI (Published version): 10.1016/j.trc.2022.103854

Published in: Transportation Research Part C: Emerging Technologies
ISSN: 0968-090X
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60967
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first_indexed 2022-09-21T11:40:11Z
last_indexed 2023-01-13T19:21:29Z
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spelling v2 60967 2022-08-30 Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale 10382520ce790248e1be61a6a9003717 0000-0002-6847-614X Yeran Sun Yeran Sun true false 2022-08-30 BGPS Journal Article Transportation Research Part C: Emerging Technologies 143 103854 Elsevier BV 0968-090X Crowd Information, Convolutional Neural Network; k-Nearest Neighbor; Gated Recurrent Unit; Training Time Cost 1 10 2022 2022-10-01 10.1016/j.trc.2022.103854 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Xucai Zhang and Fangli Guan are supported by CSC (China Scholarship Council) [202106380062, 202006270082]. 2024-07-17T08:58:21.2982937 2022-08-30T11:17:44.2067558 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Xucai Zhang 1 Yeran Sun 0000-0002-6847-614X 2 Fangli Guan 3 Kai Chen 4 Frank Witlox 5 Haosheng Huang 6 60967__25219__2555873e00964fd187bcc950572dc19a.pdf 60967.pdf 2022-09-26T09:58:15.9145156 Output 1836112 application/pdf Accepted Manuscript true 2023-08-16T00:00:00.0000000 ©2022 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
spellingShingle Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
Yeran Sun
title_short Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
title_full Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
title_fullStr Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
title_full_unstemmed Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
title_sort Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
author_id_str_mv 10382520ce790248e1be61a6a9003717
author_id_fullname_str_mv 10382520ce790248e1be61a6a9003717_***_Yeran Sun
author Yeran Sun
author2 Xucai Zhang
Yeran Sun
Fangli Guan
Kai Chen
Frank Witlox
Haosheng Huang
format Journal article
container_title Transportation Research Part C: Emerging Technologies
container_volume 143
container_start_page 103854
publishDate 2022
institution Swansea University
issn 0968-090X
doi_str_mv 10.1016/j.trc.2022.103854
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
published_date 2022-10-01T08:58:20Z
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