Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, Dohyoung | - |
dc.contributor.author | Yu, Byeonghyeop | - |
dc.contributor.author | Jeon, Hyunjeong | - |
dc.contributor.author | Sohn, Keemin | - |
dc.date.available | 2019-03-08T06:57:26Z | - |
dc.date.issued | 2019-02 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.issn | 1939-9359 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3353 | - |
dc.description.abstract | Spatio-temporal dependencies are the key to predicting the traffic parameters of an urban arterial network. However, their inclusion in forecasting traffic states has been hampered due to both the absence of a robust model and the computational burden. Recently, an innovative way to tackle the problem was developed by adopting a convolutional neural network for map images representing a traffic state. Unlike previous studies that utilized map images only for input, the present study adopted images for both the input and the output of the proposed model. The results show that the performance of image-to-image learning is superior to that of existing models. IEEE | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TVT.2018.2885366 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Vehicular Technology, v.68, no.2, pp 1188 - 1197 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000458803200012 | - |
dc.identifier.scopusid | 2-s2.0-85058086568 | - |
dc.citation.endPage | 1197 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 1188 | - |
dc.citation.title | IEEE Transactions on Vehicular Technology | - |
dc.citation.volume | 68 | - |
dc.type.docType | Article in Press | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Deep convolutional neural network (CNN), machine learning | - |
dc.subject.keywordAuthor | spatio-temporal dependency | - |
dc.subject.keywordAuthor | traffic speed | - |
dc.subject.keywordPlus | TRAVEL-TIME PREDICTION | - |
dc.subject.keywordPlus | FLOW | - |
dc.subject.keywordPlus | MODEL | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.