Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14384
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dc.contributor.authorDo Lee, Won-
dc.contributor.authorCHO, Sungjin-
dc.contributor.authorBELLEMANS, Tom-
dc.contributor.authorJANSSENS, Davy-
dc.contributor.authorWETS, Geert-
dc.contributor.authorChoi, Keechoo-
dc.contributor.authorJoh, Chang-Hyeon-
dc.date.accessioned2012-11-16T12:25:47Z-
dc.date.available2012-11-16T12:25:47Z-
dc.date.issued2012-
dc.identifier.citationShakshuki, Elhadi; Younas, Muhammad (Ed.). Procedia Computer Science 10 (2012), p. 840-845-
dc.identifier.issn1877-0509-
dc.identifier.urihttp://hdl.handle.net/1942/14384-
dc.description.abstractAs a case study area, Seoul metropolitan area in Korea, where has been experiencing transportation problems including congestions and emissions, currently needs an alternative policy measure at the individual level, instead of large scale infrastructure constructions. Even though some researches based on an activity-based approach dealing with an individual travel behavior have been conducted in Korea, none of them used a simulation framework. Considering a genuine activity-based transportation demand forecast, there is no better option but to introduce the activity-based simulation framework. Among several activity-based transportation frameworks, Feathers will indeed be applied into the study area because it is the most viable and suitable simulation platform for that area in terms of a similar spatial dimension. Although, the application for the study area is possible on Feathers, there are potential problems including the prohibition of using individual and household data in census, an inconsistent administrative unit in Korea and inappropriate Flemish figures and patterns for the study area. To overcome these problems, IPF for synthesizing population and a comparative study of Flemish and the study area will be applied in this study. Moreover, we plan to integrate an agent-based mode with the TDM research in order to complement the activity-based model by predicting an adapted daily schedule to an individual circumstance.-
dc.language.isoen-
dc.relation.ispartofseriesProcedia Computer Science-
dc.rightsCopyright © 2012 Published by Elsevier B.V.-
dc.subject.otherSeoul metropolitan area; Transportation demand model; Activity-based model; Agent-based model; Feathers-
dc.titleSeoul activity-based model: an application of Feathers solutions to Seoul metropolitan area-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsShakshuki, Elhadi-
local.bibliographicCitation.authorsYounas, Muhammad-
local.bibliographicCitation.conferencedate27-28 August 2012-
local.bibliographicCitation.conferencenameThe 1st International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications-
local.bibliographicCitation.conferenceplaceNiagara Falls (Ontario) - Canada-
dc.identifier.epage845-
dc.identifier.spage840-
local.bibliographicCitation.jcatC1-
dc.relation.referencesArentze, T.A. and H.J.P. Timmermans. A theoretical framework for modeling activity travel scheduling decisions in non-stationary environments under conditions of uncertainty and learning. In Proceedings International Conference on Activity-Based Analysis 2004. Bruno Kochan, Tom Bellemans, Davy Janssens, Geert Wets. Assessing the Impact of Fuel Cost on Traffic Demand in Flanders using Activity-Based Models. Travel Demand Management (TDM) Vienna 2008. David A. Hensher, Kenneth J. Button. Handbook of Transport Modelling 2000, Pergamon. Davy Janssens, Geert Wets, Harry Timmermans, Theo Arentze. Modeling short-term dynamics in activity-travel patterns: the feathers model, Innovations in Travel Demand Modeling Conference 2007;71-77. Harry Timmermans, Theo Arentze and Chang-Hyeon Joh. Analysing space-time behavior: new approaches to old problems. Progress in Human Geography 2002; 26(2);175-190. Pendyala, R.M., Kitamura, R. Kikuchi, A., Yamamoto, T. and Fujji, S. FAMOS: Florida activity Mobility simulator. Proceedings of the 84th Annual Meeting of the Transportation Research Board 2005. Ryuichi Kitamura. Applications of models of activity behavior for activity based demand forecasting. Activity-based travel forecasting conference proceedings 1996. Metropolitan Trnasport Association. Seoul Metropolitan Household Survey Report. 2009. Vovsha, P. and M. Bradley. Advanced activity-based models in context of planning decisions. Transportation Research Record: Journal of the Transportation Research Board 2006; 1981;34-42.-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.bibliographicCitation.oldjcatC2-
dc.identifier.doi10.1016/j.procs.2012.06.109-
dc.identifier.isi000314400700103-
local.bibliographicCitation.btitleProcedia Computer Science 10 (2012)-
item.fulltextWith Fulltext-
item.contributorDo Lee, Won-
item.contributorCHO, Sungjin-
item.contributorBELLEMANS, Tom-
item.contributorJANSSENS, Davy-
item.contributorWETS, Geert-
item.contributorChoi, Keechoo-
item.contributorJoh, Chang-Hyeon-
item.accessRightsOpen Access-
item.fullcitationDo Lee, Won; CHO, Sungjin; BELLEMANS, Tom; JANSSENS, Davy; WETS, Geert; Choi, Keechoo & Joh, Chang-Hyeon (2012) Seoul activity-based model: an application of Feathers solutions to Seoul metropolitan area. In: Shakshuki, Elhadi; Younas, Muhammad (Ed.). Procedia Computer Science 10 (2012), p. 840-845.-
item.validationecoom 2014-
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