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dc.contributor.author
Genser, Alexander
dc.contributor.author
Makridis, Michail
dc.contributor.author
Kouvelas, Anastasios
dc.contributor.editor
Wang, Meng
dc.contributor.editor
Jaekel, Birgit
dc.contributor.editor
Lehnert, Martin
dc.contributor.editor
Zhou, Runhao
dc.contributor.editor
Li, Zirui
dc.date.accessioned
2023-06-20T13:45:23Z
dc.date.available
2022-12-02T08:44:40Z
dc.date.available
2022-12-02T09:20:12Z
dc.date.available
2023-05-12T13:29:58Z
dc.date.available
2023-06-20T13:45:23Z
dc.date.issued
2023
dc.identifier.isbn
978-3-95908-296-9
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/584466
dc.identifier.doi
10.3929/ethz-b-000584466
dc.description.abstract
Emerging sensors and intelligent traffic technologies provide extensive data sets in a traffic network. However, realizing the full potential of such data sets for a unique representation of real-world states is challenging due to data accuracy, noise, and temporal-spatial resolution. Data assimilation is a known group of methodological approaches that exploit physics-informed traffic models and data observations to perform short-term predictions of the traffic state in freeway environments. At the same time, neural networks capture high non-linearities, similar to those presented in traffic networks. Despite numerous works applying different variants of Kalman filters, the possibility of traffic state estimation with deep-learning-based methodologies is only partially explored in the literature. We present a deep-learning modeling approach to perform traffic state estimation on large freeway networks. The proposed framework is trained on local observations from static and moving sensors and identifies differences between well-trusted data and model outputs. The detected patterns are then used throughout the network, even where there are no available observations to estimate fundamental traffic quantities. The preliminary results of the work highlight the potential of deep learning for traffic state estimation.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
TUDpress
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Traffic state
en_US
dc.subject
Traffic prediction
en_US
dc.subject
Traffic models
en_US
dc.subject
Deep learning
en_US
dc.subject
Data assimilation
en_US
dc.title
Exploiting deep learning and traffic models for freeway traffic estimation
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.book.title
Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022
en_US
ethz.journal.title
Verkehrstelematik
ethz.journal.volume
9
en_US
ethz.pages.start
119
en_US
ethz.pages.end
128
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS 2022)
en_US
ethz.event.location
Dresden, Germany
en_US
ethz.event.date
November 30 – December 2, 20222
en_US
ethz.notes
Conference lecture held on December 2, 2022
en_US
ethz.publication.place
Dresden
en_US
ethz.publication.status
published
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::08686 - Gruppe Strassenverkehrstechnik
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
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::08686 - Gruppe Strassenverkehrstechnik
en_US
ethz.tag
Modeling and simulation
en_US
ethz.tag
Traffic operations
en_US
ethz.tag
ITS
en_US
ethz.relation.isPartOf
10.25368/2023.91
ethz.date.deposited
2022-12-02T08:44:40Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-06-20T13:46:05Z
ethz.rosetta.lastUpdated
2023-06-20T13:46:05Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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