DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tak, Sehyun | ko |
dc.contributor.author | Kim, Sunghoon | ko |
dc.contributor.author | Oh, Simon | ko |
dc.contributor.author | Yeo, Hwasoo | ko |
dc.date.accessioned | 2016-11-09T05:29:31Z | - |
dc.date.available | 2016-11-09T05:29:31Z | - |
dc.date.created | 2016-03-06 | - |
dc.date.created | 2016-03-06 | - |
dc.date.issued | 2016-10 | - |
dc.identifier.citation | COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, v.31, no.10, pp.777 - 793 | - |
dc.identifier.issn | 1093-9687 | - |
dc.identifier.uri | http://hdl.handle.net/10203/213771 | - |
dc.description.abstract | Travel 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.language | English | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.subject | NEURAL-NETWORK MODEL | - |
dc.subject | FREEWAY INCIDENT DETECTION | - |
dc.subject | VEHICULAR TRAFFIC FLOW | - |
dc.subject | NONPARAMETRIC REGRESSION | - |
dc.subject | MISSING DATA | - |
dc.subject | ALGORITHM | - |
dc.subject | WEATHER | - |
dc.subject | DEMAND | - |
dc.subject | URBAN | - |
dc.subject | DELAY | - |
dc.title | Development of a Data-Driven Framework for Real-Time Travel Time Prediction | - |
dc.type | Article | - |
dc.identifier.wosid | 000383659200004 | - |
dc.identifier.scopusid | 2-s2.0-84984891651 | - |
dc.type.rims | ART | - |
dc.citation.volume | 31 | - |
dc.citation.issue | 10 | - |
dc.citation.beginningpage | 777 | - |
dc.citation.endingpage | 793 | - |
dc.citation.publicationname | COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING | - |
dc.identifier.doi | 10.1111/mice.12205 | - |
dc.contributor.localauthor | Yeo, Hwasoo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | NEURAL-NETWORK MODEL | - |
dc.subject.keywordPlus | FREEWAY INCIDENT DETECTION | - |
dc.subject.keywordPlus | VEHICULAR TRAFFIC FLOW | - |
dc.subject.keywordPlus | NONPARAMETRIC REGRESSION | - |
dc.subject.keywordPlus | MISSING DATA | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | WEATHER | - |
dc.subject.keywordPlus | DEMAND | - |
dc.subject.keywordPlus | URBAN | - |
dc.subject.keywordPlus | DELAY | - |
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