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dc.contributor.author
Gao, Kun
dc.contributor.author
Yang, Ying
dc.contributor.author
Zhang, Tianshu
dc.contributor.author
Li, Aoyong
dc.contributor.author
Qu, Xiaobo
dc.date.accessioned
2021-03-24T13:47:29Z
dc.date.available
2021-03-18T04:38:43Z
dc.date.available
2021-03-24T13:47:29Z
dc.date.issued
2021-04-22
dc.identifier.issn
0950-7051
dc.identifier.other
10.1016/j.knosys.2021.106882
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/475211
dc.description.abstract
Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods. © 2021 Elsevier
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Travel behavior
en_US
dc.subject
Machine learning
en_US
dc.subject
Intelligent transport system
en_US
dc.subject
Behavioral theory
en_US
dc.title
Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
en_US
dc.type
Journal Article
dc.date.published
2021-02-18
ethz.journal.title
Knowledge-Based Systems
ethz.journal.volume
218
en_US
ethz.pages.start
106882
en_US
ethz.size
16 p.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-03-18T04:38:53Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-03-24T13:47:39Z
ethz.rosetta.lastUpdated
2022-03-29T05:58:05Z
ethz.rosetta.versionExported
true
ethz.COinS
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