DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Simon | ko |
dc.contributor.author | Byon, Young-ji | ko |
dc.contributor.author | Jang, Kitae | ko |
dc.contributor.author | Yeo, Hwasoo | ko |
dc.date.accessioned | 2018-01-30T04:15:54Z | - |
dc.date.available | 2018-01-30T04:15:54Z | - |
dc.date.created | 2017-01-29 | - |
dc.date.created | 2017-01-29 | - |
dc.date.issued | 2018-01 | - |
dc.identifier.citation | KSCE JOURNAL OF CIVIL ENGINEERING, v.22, no.1, pp.298 - 310 | - |
dc.identifier.issn | 1226-7988 | - |
dc.identifier.uri | http://hdl.handle.net/10203/238758 | - |
dc.description.abstract | Emerging technologies provide a venue on which on-line traffic controls and management systems can be implemented. For such applications, having access to accurate predictions on travel-times are mandatory for their successful operations. Transportation engineers have developed numerous approaches including model-based approaches. The model-based approaches consider underlying traffic mechanisms and behaviors in developing the prediction procedures and they are logically intuitive unlike datadriven approaches. Because of this explanation power, the model-based approaches have been developed for the on-line control purposes. For departments of transportation (DOTs), it is still a challenge to choose a specific approach that meets their requirements. In efforts to develop a unique guideline for transportation engineers and decision makers when considering for implementing modelbased approaches for highways, this paper reviews model-based travel-time prediction approaches by classifying them into four categories according to the level of details involved in the model: Macroscopic, Mesoscopic, CA-based, and Microscopic. Then each method is evaluated from five main perspectives: Prediction range, Accuracy, Efficiency, Applicability, and Robustness. Finally, this paper concludes with evaluations of model-based approaches in general and discusses them in relation to data-driven approaches along with future research directions. | - |
dc.language | English | - |
dc.publisher | KOREAN SOCIETY OF CIVIL ENGINEERS-KSCE | - |
dc.subject | CELL TRANSMISSION MODEL | - |
dc.subject | TRAFFIC FLOW | - |
dc.subject | NEURAL-NETWORKS | - |
dc.subject | MISSING DATA | - |
dc.subject | SIMULATION | - |
dc.subject | FRAMEWORK | - |
dc.subject | AUTOMATA | - |
dc.subject | METANET | - |
dc.subject | SYSTEM | - |
dc.subject | WAVES | - |
dc.title | Short-term Travel-time Prediction on Highway: A Review on Model-based Approach | - |
dc.type | Article | - |
dc.identifier.wosid | 000418391200034 | - |
dc.identifier.scopusid | 2-s2.0-85018351657 | - |
dc.type.rims | ART | - |
dc.citation.volume | 22 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 298 | - |
dc.citation.endingpage | 310 | - |
dc.citation.publicationname | KSCE JOURNAL OF CIVIL ENGINEERING | - |
dc.identifier.doi | 10.1007/s12205-017-0535-8 | - |
dc.contributor.localauthor | Jang, Kitae | - |
dc.contributor.localauthor | Yeo, Hwasoo | - |
dc.contributor.nonIdAuthor | Byon, Young-ji | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordAuthor | highway travel-time prediction | - |
dc.subject.keywordAuthor | model-based approach | - |
dc.subject.keywordAuthor | traffic simulation | - |
dc.subject.keywordAuthor | Traffic Management System (TMS) | - |
dc.subject.keywordAuthor | Intelligent Transportation System (ITS) | - |
dc.subject.keywordAuthor | On-line simulation | - |
dc.subject.keywordPlus | CELL TRANSMISSION MODEL | - |
dc.subject.keywordPlus | TRAFFIC FLOW | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | MISSING DATA | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | AUTOMATA | - |
dc.subject.keywordPlus | METANET | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | WAVES | - |
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