Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36322
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dc.contributor.authorLowe, Warnakulasooriya Umesh Ashenen_UK
dc.contributor.authorLades, Leonharden_UK
dc.contributor.authorCarroll, Páraicen_UK
dc.date.accessioned2024-10-10T00:08:15Z-
dc.date.available2024-10-10T00:08:15Z-
dc.date.issued2024-08-26en_UK
dc.identifier.urihttp://hdl.handle.net/1893/36322-
dc.description.abstractIntroduction Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. Methods This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. Findings The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. Conclusion The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available.en_UK
dc.language.isoenen_UK
dc.publisherSpringer Science and Business Media LLCen_UK
dc.relationLowe WUA, Lades L & Carroll P (2024) Personalizing travel behaviour change interventions using the trans-theoretical model and multimodality data. <i>European Transport Research Review</i>, 16. https://doi.org/10.1186/s12544-024-00666-wen_UK
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectTravel behaviour interventionsen_UK
dc.subjectMultimodality measurementsen_UK
dc.subjectStage modelen_UK
dc.subjectBehavioural changeen_UK
dc.titlePersonalizing travel behaviour change interventions using the trans-theoretical model and multimodality dataen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1186/s12544-024-00666-wen_UK
dc.citation.jtitleEuropean Transport Research Reviewen_UK
dc.citation.issn1866-8887en_UK
dc.citation.issn1867-0717en_UK
dc.citation.volume16en_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emaill.k.lades@stir.ac.uken_UK
dc.citation.date26/08/2024en_UK
dc.contributor.affiliationUniversity College Dublin (UCD)en_UK
dc.contributor.affiliationUniversity College Dublinen_UK
dc.contributor.affiliationUniversity of Melbourneen_UK
dc.identifier.isiWOS:001297237400001en_UK
dc.identifier.scopusid2-s2.0-85201971587en_UK
dc.identifier.wtid2045299en_UK
dc.contributor.orcid0000-0002-8369-2932en_UK
dc.contributor.orcid0000-0002-3114-0781en_UK
dc.date.accepted2024-08-06en_UK
dcterms.dateAccepted2024-08-06en_UK
dc.date.filedepositdate2024-10-08en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorLowe, Warnakulasooriya Umesh Ashen|0000-0002-8369-2932en_UK
local.rioxx.authorLades, Leonhard|0000-0002-3114-0781en_UK
local.rioxx.authorCarroll, Páraic|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2024-10-08en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2024-10-08|en_UK
local.rioxx.filenames12544-024-00666-w.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1866-8887en_UK
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