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dc.contributor.authorGHOSH, BIDISHAen
dc.contributor.authorCAULFIELD, BRIANen
dc.date.accessioned2014-11-05T12:43:01Z
dc.date.available2014-11-05T12:43:01Z
dc.date.created2014en
dc.date.issued2014en
dc.date.submitted2014en
dc.identifier.citationDoorley, R., Pakrashi, V., Caulfield, B., Ghosh, B., Short-Term Forecasting of Bicycle Traffic Using Structural Time Series Models, 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China, 2014, 2014en
dc.identifier.otherYen
dc.identifier.urihttp://hdl.handle.net/2262/71754
dc.descriptionPUBLISHEDen
dc.descriptionQingdao, Chinaen
dc.description.abstractShort term forecasting algorithms are widely used for prediction of vehicular traffic flows for adaptive traffic management. However, despite the increasing interest in the promotion of cycling in cities, little research has been carried out into the use of traffic forecasting algorithms for bicycle traffic. Structural time series models allow the various components of a time series such as level, seasonal and regression effects to be modelled separately to allow analysis of previous trends and forecasting. In this paper, a case study at a segregated bicycle lane in Dublin, Ireland was performed to test the forecasting accuracy of structural time series models applied to continuous observations of cyclist traffic volumes. It has been shown that the proposed models can produce accurate peak period forecasts of cyclist traffic volumes at both 1 hour and fifteen minute resolution and that the percentage errors are lower for hourly forecasts. The inclusion of weather metrics as explanatory variables had varying effects on the forecasting accuracies of the models. These results directly aid the design of traffic signal control systems accommodating cyclists.en
dc.language.isoenen
dc.rightsYen
dc.subjectTransporten
dc.titleShort-Term Forecasting of Bicycle Traffic Using Structural Time Series Modelsen
dc.title.alternative17th International IEEE Conference on Intelligent Transportation Systemsen
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/caulfiben
dc.identifier.peoplefinderurlhttp://people.tcd.ie/bghoshen
dc.identifier.rssinternalid95903en
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeSmart & Sustainable Planeten
dc.subject.TCDTagCYCLINGen
dc.identifier.orcid_id0000-0003-3877-475Xen


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