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Generating activity-specific multimodal trips from aggregated data sources
SCHEFFER, Ariane Hélène Marie; CONNORS, Richard; NAKAO, Haruko et al.
2024hEART 2024: 12th Symposium of the European Association for Research in Transportation
Peer reviewed
 

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Keywords :
Dynamic demand estimation; utility maximization; Markov Chain Monte Carlo; activity-specific OD matrices
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
SCHEFFER, Ariane Hélène Marie ;  University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Engineering > Team Francesco VITI
CONNORS, Richard ;  University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Engineering > Team Francesco VITI
NAKAO, Haruko ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
VITI, Francesco  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
no
Language :
English
Title :
Generating activity-specific multimodal trips from aggregated data sources
Publication date :
June 2024
Event name :
hEART 2024: 12th Symposium of the European Association for Research in Transportation
Event organizer :
European Association for Research in Transportation
Event place :
Helsinki, Finland
Event date :
June 18-20, 2024
Audience :
International
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Development Goals :
11. Sustainable cities and communities
European Projects :
HE - 101103808 - ACUMEN - Ai-aided deCision tool for seamless mUltiModal nEtwork and traffic managemeNt
Funders :
European Union
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since 29 January 2025

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