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Passenger spatiotemporal distribution prediction in airport terminals based on physics-guided spatio-temporal graph convolutional network and its effect on indoor environment prediction

Bibliographic Data

ID21232667
AuthorsZhiwei Li (0000-0001-7505-5899, Dalian University of Technology), Jili Zhang (0000-0002-0043-5106, Dalian University of Technology, corresponding author), Hua Guan (0000-0001-6329-6273, Guangdong Baiyun University)
Year2024
Volume106
Pages105375
Publication date2024-07-01
Open AccessYes
TypeARTICLE
VenueSustainable Cities and Society (JOURNAL)
Journal identifiersISSN: 2210-6707 • E-ISSN: 2210-6715
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2024.105375
OpenAlexW4393187078
LanguageEN
Citations received1
References cited37

Computer network · Graph · Transport engineering · Computer Science · Engineering · Evacuation and Crowd Dynamics · Mathematics · Traffic and Road Safety · Traffic Prediction and Management Techniques · Theoretical Computer Science

  • Spatiotemporal prediction of transportation hub passenger distribution with sparse observation data

    Open Access•Shulin He, X Ju et al.•Journal of Transport Geography•2025

  • Passenger spatiotemporal distribution prediction in airport terminals based on insect intelligent building architecture and its contribution to fresh air energy saving

    Open Access•Zhiwei Li, Jili Zhang et al.•Sustainable Cities and Society•2023

  • Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

    Open Access•Gang Zhong, Tingting Yin et al.•Transportation•2019

Citations per year1
Citation span2025 - 2025 (1)
Highly citedNo

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