Bayesian networks for travel demand generation
An application to Switzerland
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Author / Producer
Date
2023-09
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Bayesian Networks (BNs) are probabilistic graphical models representing conditional dependencies existing between variables of interest. Recent studies have employed BNs for population synthesis and daily activity plan generation. Those studies highlight the ability of BNs to efficiently detect the causality links between variables in an easily interpretable way. This short paper aims to propose a further application of BNs for both population and daily activity plan synthesis in Switzerland. We show that understanding the dependency structure linking the population characteristics and its mobility behaviour is key to generating representative synthetic activity patterns. Furthermore, we lay the foundations for the development of temporally transferable travel demand models.
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Publication status
published
External links
Editor
Book title
hEART 2023: 11th Symposium of the European Association for Research in Transportation
Journal / series
Volume
Pages / Article No.
8170
Publisher
European Association for Research in Transportation
Event
11th Symposium of the European Association for Research in Transportation (hEART 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Activity-based modeling; Bayesian networks; Synthetic populations; Travel demand generation
Organisational unit
03521 - Axhausen, Kay W. (emeritus) / Axhausen, Kay W. (emeritus)
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
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Related publications and datasets
Is part of: https://transp-or.epfl.ch/heart/2023.php