Trip purpose imputation using GPS trajectories with machine learning
dc.contributor.author
Gao, Qinggang
dc.contributor.author
Molloy, Joseph
dc.contributor.author
Axhausen, Kay W.
dc.date.accessioned
2023-04-19T05:52:52Z
dc.date.available
2023-04-19T05:52:52Z
dc.date.issued
2021-11-13
dc.identifier.issn
2220-9964
dc.identifier.other
10.3390/ijgi10110775
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/608446
dc.identifier.doi
10.3929/ethz-b-000505634
dc.description.abstract
We studied trip purpose imputation using data mining and machine learning techniques based on a dataset of GPS-based trajectories gathered in Switzerland. With a large number of labeled activities in 8 categories, we explored location information using hierarchical clustering and achieved a classification accuracy of 86.7% using a random forest approach as a baseline. The contribution of this study is summarized below. Firstly, using information from GPS trajectories exclusively without personal information shows a negligible decrease in accuracy (0.9%), which indicates the good performance of our data mining steps and the wide applicability of our imputation scheme in case of limited information availability. Secondly, the dependence of model performance on the geographical location, the number of participants, and the duration of the survey is investigated to provide a reference when comparing classification accuracy. Furthermore, we show the ensemble filter to be an excellent tool in this research field not only because of the increased accuracy (93.6%) especially for minority classes, but also the reduced uncertainties in blindly trusting the labeling of activities by participants, which is vulnerable to class noise due to the large survey response burden. Finally, the trip purpose derivation accuracy across participants reaches 74.8%, which is significant and suggests the possibility of effectively applying a model trained on GPS trajectories of a small subset of citizens to a larger GPS trajectory sample.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Class noise
en_US
dc.subject
Data mining
en_US
dc.subject
Ensemble filter
en_US
dc.subject
Hierarchical clustering
en_US
dc.subject
Machine learning
en_US
dc.subject
Random forest
en_US
dc.subject
Trip purpose
en_US
dc.title
Trip purpose imputation using GPS trajectories with machine learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-08
ethz.journal.title
ISPRS International Journal of Geo-Information
ethz.journal.volume
10
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
ISPRS int. j. geo-inf.
ethz.pages.start
775
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
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::03521 - Axhausen, Kay W. (emeritus) / Axhausen, Kay W. (emeritus)
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::03521 - Axhausen, Kay W. (emeritus) / Axhausen, Kay W. (emeritus)
en_US
ethz.date.deposited
2020-11-11T09:09:16Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.identifier.internal
1574
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-04-19T05:52:55Z
ethz.rosetta.lastUpdated
2024-02-02T21:42:47Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/505634
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/450732
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Journal Article [130141]