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dc.contributor.authorLi, Xiaojiang
dc.contributor.authorCai, Bill Yang
dc.contributor.authorQiu, Waishan
dc.contributor.authorZhao, Jinhua
dc.contributor.authorRatti, Carlo
dc.date.accessioned2021-10-27T20:09:23Z
dc.date.available2021-10-27T20:09:23Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/134831
dc.description.abstract© 2019 Elsevier Ltd The sun glare is one of the major environmental hazards that cause traffic accidents. Every year many traffic accidents are caused by sun glare in the United States. Providing accurate information about when and where sun glare happens would be helpful to prevent sun glare caused traffic accidents. In this study, we proposed to use the publicly accessible Google Street View (GSV) panorama images to estimate and predict the occurrence of sun glare. GSV images have view sight similar to drivers, which make GSV images suitable for estimating the visibility of sun glare to drivers. A recently developed convolutional neural network algorithm was used to segment GSV images and predict obstructions on sun glare. Based on the predicted obstructions for given locations, we further estimated the time windows of sun glare by calculating the sun positions and the relative angles between drivers and the sun for those locations. We conducted a case study in Cambridge, Massachusetts, USA. Results show that the method can predict the occurrence of sun glare precisely. The proposed method provides an important tool for people to deal with the sun glare and reduce the potential traffic accidents caused by the sun glare.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.isversionof10.1016/J.TRC.2019.07.013
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs License
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcearXiv
dc.titleA novel method for predicting and mapping the occurrence of sun glare using Google Street View
dc.typeArticle
dc.contributor.departmentSenseable City Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planning
dc.relation.journalTransportation Research Part C: Emerging Technologies
dc.eprint.versionOriginal manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/NonPeerReviewed
dc.date.updated2020-08-28T14:22:45Z
dspace.orderedauthorsLi, X; Cai, BY; Qiu, W; Zhao, J; Ratti, C
dspace.date.submission2020-08-28T14:22:49Z
mit.journal.volume106
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


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