Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82275
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dc.contributorSchool of Nursing-
dc.creatorCai, LR-
dc.creatorYu, YD-
dc.creatorZhang, SY-
dc.creatorSong, Y-
dc.creatorXiong, Z-
dc.creatorZhou, T-
dc.date.accessioned2020-05-05T05:59:23Z-
dc.date.available2020-05-05T05:59:23Z-
dc.identifier.urihttp://hdl.handle.net/10397/82275-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication L. Cai, Y. Yu, S. Zhang, Y. Song, Z. Xiong and T. Zhou, "A Sample-Rebalanced Outlier-Rejected $k$ -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting," in IEEE Access, vol. 8, pp. 22686-22696, 2020 is available at https://dx.doi.org/10.1109/ACCESS.2020.2970250en_US
dc.subjectIntelligent transportation systemsen_US
dc.subjectRoad transportationen_US
dc.subjectTime series analysisen_US
dc.subjectStochastic processesen_US
dc.titleA sample-rebalanced outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecastingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage22686-
dc.identifier.epage22696-
dc.identifier.volume8-
dc.identifier.doi10.1109/ACCESS.2020.2970250-
dcterms.abstractShort-term traffic flow forecasting is a fundamental and challenging task due to the stochastic dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a sample-rebalanced and outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting. In this model, we adopt a new metric for the evolutionary traffic flow patterns, and reconstruct balanced training sets by relative transformation to tackle the imbalance issue. Then, we design a hybrid model that considers both local and global information to address the limited size of the training samples. We employ four real-world benchmark datasets often used in such tasks to evaluate our model. Experimental results show that our model outperforms state-of-the-art parametric and non-parametric models.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2020, v. 8, p. 22686-22696-
dcterms.isPartOfIEEE access-
dcterms.issued2020-
dc.identifier.isiWOS:000519023500001-
dc.identifier.scopus2-s2.0-85081092704-
dc.identifier.eissn2169-3536-
dc.description.validate202006 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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