Show simple item record

dc.contributor.authorHillel, Timothyen
dc.contributor.authorElshafie, Mohammeden
dc.contributor.authorJin, Yingen
dc.date.accessioned2018-09-27T14:12:57Z
dc.date.available2018-09-27T14:12:57Z
dc.date.issued2018-03en
dc.identifier.issn2397-8759
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/282845
dc.description.abstractUrban transport infrastructure is under increasing pressure from rising travel demand in many cities worldwide. It is no longer sustainable or even economically viable to cope with increased demand by continually adding capacity to transport networks. Instead, travel demand must be managed by encouraging passengers to adapt their travel behaviour. This approach necessitates a significantly deeper understanding of the seemingly random variations of passenger flows than is afforded by the current travel demand modelling techniques. This study presents a new modelling framework for predicting travel mode choice, through recreating and analysing the choice-set faced by the passenger at the time of day of their travel. A new data set has been developed by combining individual trip records from the London Travel Demand Survey (LTDS), with systematically matched trip trajectories alongside their corresponding mode alternatives from an online directions service and detailed estimates of public transport fares and car operating costs. The value of the data set is demonstrated by comparing two models of passenger mode choice based on stochastic gradient boosting trees, one using only the LTDS data and the other with our full data set. The models are then used to identify the key factors driving passenger mode choice.
dc.languageenen
dc.publisherInstitution of Civil Engineers
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleRecreating passenger mode choice-sets for transport simulation: A case study of London, UKen
dc.typeArticle
prism.endingPage42
prism.issueIdentifier1en
prism.publicationDate2018en
prism.publicationNameProceedings of the Institution of Civil Engineers - Smart Infrastructure and Constructionen
prism.startingPage29
prism.volume171en
dc.identifier.doi10.17863/CAM.30209
dcterms.dateAccepted2018-05-11en
rioxxterms.versionofrecord10.1680/jsmic.17.00018en
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2018-03en
dc.contributor.orcidHillel, Timothy [0000-0001-6872-2235]
dc.contributor.orcidElshafie, Mohammed [0000-0001-8307-7115]
dc.contributor.orcidJin, Ying [0000-0003-2683-6829]
dc.identifier.eissn2397-8759
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (1549360)
pubs.funder-project-idEPSRC (EP/N010221/1)
pubs.funder-project-idEPSRC (EP/N021614/1)
pubs.funder-project-idTechnology Strategy Board (920035)
pubs.funder-project-idOve Arup Foundation (unknown)
pubs.funder-project-idBritish Academy (CS110474)
pubs.funder-project-idEPSRC (EP/F034350/1)
pubs.funder-project-idDepartment for Transport (DfT) (T-TRIG July 2016)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International