DC Field | Value | Language |
---|---|---|
dc.contributor.author | Benkraouda, Ouafa | ko |
dc.contributor.author | Thodi, Bilal Thonnam | ko |
dc.contributor.author | Yeo, Hwasoo | ko |
dc.contributor.author | Menendez, Monica | ko |
dc.contributor.author | Jabari, Saif Eddin | ko |
dc.date.accessioned | 2020-07-18T00:57:07Z | - |
dc.date.available | 2020-07-18T00:57:07Z | - |
dc.date.created | 2020-07-10 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.citation | IEEE ACCESS, v.8, pp.104740 - 104752 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/275506 | - |
dc.description.abstract | We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from the Next Generation Simulation (NGSIM) program. Our results show that with probe vehicle penetration levels as low as 5 & x0025;, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model & x2019;s reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. We also provide a comparison against a widely used adaptive smoothing technique used for the same purpose and demonstrate the superiority of the proposed approach, even with probe vehicle lower penetration levels. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Traffic Data Imputation Using Deep Convolutional Neural Networks | - |
dc.type | Article | - |
dc.identifier.wosid | 000541044200016 | - |
dc.identifier.scopusid | 2-s2.0-85087158944 | - |
dc.type.rims | ART | - |
dc.citation.volume | 8 | - |
dc.citation.beginningpage | 104740 | - |
dc.citation.endingpage | 104752 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2999662 | - |
dc.contributor.localauthor | Yeo, Hwasoo | - |
dc.contributor.nonIdAuthor | Benkraouda, Ouafa | - |
dc.contributor.nonIdAuthor | Thodi, Bilal Thonnam | - |
dc.contributor.nonIdAuthor | Menendez, Monica | - |
dc.contributor.nonIdAuthor | Jabari, Saif Eddin | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | data expansion | - |
dc.subject.keywordAuthor | data imputation | - |
dc.subject.keywordAuthor | estimation | - |
dc.subject.keywordAuthor | filtering | - |
dc.subject.keywordAuthor | traffic dynamics | - |
dc.subject.keywordAuthor | traffic state estimation | - |
dc.subject.keywordPlus | VEHICLE TECHNOLOGY | - |
dc.subject.keywordPlus | INTERSECTION CONTROL | - |
dc.subject.keywordPlus | STATE ESTIMATION | - |
dc.subject.keywordPlus | PREDICTION | - |
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