Development of a Data-Driven Framework for Real-Time Travel Time Prediction

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dc.contributor.authorTak, Sehyunko
dc.contributor.authorKim, Sunghoonko
dc.contributor.authorOh, Simonko
dc.contributor.authorYeo, Hwasooko
dc.date.accessioned2016-11-09T05:29:31Z-
dc.date.available2016-11-09T05:29:31Z-
dc.date.created2016-03-06-
dc.date.created2016-03-06-
dc.date.issued2016-10-
dc.identifier.citationCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, v.31, no.10, pp.777 - 793-
dc.identifier.issn1093-9687-
dc.identifier.urihttp://hdl.handle.net/10203/213771-
dc.description.abstractTravel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long-term prediction in a real-time manner have been lacking. Existing methods do not fully utilize the advantages of the state-of-the-art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real-time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long-term (at least 6-hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k-nearest neighbor (Mk-NN) method which is compared with the conventional k-NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long-term travel time with shorter computation time.-
dc.languageEnglish-
dc.publisherWILEY-BLACKWELL-
dc.subjectNEURAL-NETWORK MODEL-
dc.subjectFREEWAY INCIDENT DETECTION-
dc.subjectVEHICULAR TRAFFIC FLOW-
dc.subjectNONPARAMETRIC REGRESSION-
dc.subjectMISSING DATA-
dc.subjectALGORITHM-
dc.subjectWEATHER-
dc.subjectDEMAND-
dc.subjectURBAN-
dc.subjectDELAY-
dc.titleDevelopment of a Data-Driven Framework for Real-Time Travel Time Prediction-
dc.typeArticle-
dc.identifier.wosid000383659200004-
dc.identifier.scopusid2-s2.0-84984891651-
dc.type.rimsART-
dc.citation.volume31-
dc.citation.issue10-
dc.citation.beginningpage777-
dc.citation.endingpage793-
dc.citation.publicationnameCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING-
dc.identifier.doi10.1111/mice.12205-
dc.contributor.localauthorYeo, Hwasoo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordPlusNEURAL-NETWORK MODEL-
dc.subject.keywordPlusFREEWAY INCIDENT DETECTION-
dc.subject.keywordPlusVEHICULAR TRAFFIC FLOW-
dc.subject.keywordPlusNONPARAMETRIC REGRESSION-
dc.subject.keywordPlusMISSING DATA-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordPlusWEATHER-
dc.subject.keywordPlusDEMAND-
dc.subject.keywordPlusURBAN-
dc.subject.keywordPlusDELAY-
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