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dc.contributor.author
Anda, Cuauhtémoc
dc.contributor.author
Ordonez Medina, Sergio Arturo
dc.contributor.author
Axhausen, Kay W.
dc.date.accessioned
2020-09-25T06:55:35Z
dc.date.available
2020-09-25T06:51:16Z
dc.date.available
2020-09-25T06:55:35Z
dc.date.issued
2020-09
dc.identifier.uri
http://hdl.handle.net/20.500.11850/442517
dc.identifier.doi
10.3929/ethz-b-000442517
dc.description.abstract
Mobile phone data generated in mobile communication networks has the potential to improve current travel demand models and in general, how we plan for better urban transportation systems. However, due to its high-dimensionality, even if anonymised there still exists the possibility to reidentify the users behind the mobile phone traces. This risk makes its usage outside the telecommunication network incompatible with recent data privacy regulations, hampering its adoption in transportation-related applications. To address this issue, we propose a framework designed only with user-aggregated mobile phone data to synthesise realistic daily individual mobility — Digital Twin Travellers. We explore different strategies built around modified Markov models and an adaption of the Rejection Sampling algorithm to recreate realistic daily schedules and locations. We also define a one-day mobility population score to measure the similarity between the population of generated agents and the real mobile phone user population. Ultimately, we show how with a series of histograms provided by the telecommunication service provider (TSP) it is possible and plausible to disaggregate them into new synthetic and useful individual-level information, building in this way a big data travel demand framework that is designed in accordance with current data privacy regulations.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Future Cities Laboratory (FCL), Singapore ETH Centre
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Travel demand models
en_US
dc.subject
Mobile phone data
en_US
dc.subject
Generative models
en_US
dc.subject
Data privacy
en_US
dc.title
Synthesising digital twin travellers
en_US
dc.type
Working Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.title.subtitle
Individual travel demand from aggregated mobile phone data
en_US
ethz.journal.title
Arbeitsberichte Verkehrs- und Raumplanung
ethz.journal.volume
1559
en_US
ethz.size
26 p.
en_US
ethz.publication.place
Singapore
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::08058 - Singapore-ETH Centre (SEC) / Singapore-ETH Centre (SEC)
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems::03521 - Axhausen, Kay W. / Axhausen, Kay W.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems::03521 - Axhausen, Kay W. / Axhausen, Kay W.
en_US
ethz.date.deposited
2020-09-25T06:51:25Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.identifier.internal
1559
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-25T06:55:44Z
ethz.rosetta.lastUpdated
2020-09-25T06:55:44Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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