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dc.contributor.authorLee, Wei-Hsunen_US
dc.contributor.authorTseng, Shian-Shyongen_US
dc.contributor.authorTsai, Sheng-Hanen_US
dc.date.accessioned2014-12-08T15:09:41Z-
dc.date.available2014-12-08T15:09:41Z-
dc.date.issued2009-04-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2008.03.018en_US
dc.identifier.urihttp://hdl.handle.net/11536/7407-
dc.description.abstractMany approaches had been proposed for travel time prediction in these decades; most of them focus on the predicting the travel time on freeway or simple arterial network. Travel time prediction for urban network in real time is hard to achieve for several reasons: complexity and path routing problem in urban network, unavailability of real-time sensor data, spatiotemporal data coverage problem, and lacking real-time events consideration. In this paper, we propose a knowledge based real-time travel time prediction model which contains real-time and historical travel time predictors to discover traffic patterns from the raw data of location based services by data mining technique and transform them to travel time prediction rules. Besides, dynamic weight combination of the two predictors by meta-rules is proposed to provide a real-time traffic event response mechanism to enhance the precision of the travel time prediction. (c) 2008 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectKnowledge based systemen_US
dc.subjectSpatiotemporal data miningen_US
dc.subjectTravel time predictionen_US
dc.subjectIntelligent transportation system (ITS)en_US
dc.titleA knowledge based real-time travel time prediction system for urban networken_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2008.03.018en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume36en_US
dc.citation.issue3en_US
dc.citation.spage4239en_US
dc.citation.epage4247en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000263584100011-
dc.citation.woscount25-
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