Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/82275
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Nursing | - |
dc.creator | Cai, LR | - |
dc.creator | Yu, YD | - |
dc.creator | Zhang, SY | - |
dc.creator | Song, Y | - |
dc.creator | Xiong, Z | - |
dc.creator | Zhou, T | - |
dc.date.accessioned | 2020-05-05T05:59:23Z | - |
dc.date.available | 2020-05-05T05:59:23Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/82275 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This 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.rights | The 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.2020.2970250 | en_US |
dc.subject | Intelligent transportation systems | en_US |
dc.subject | Road transportation | en_US |
dc.subject | Time series analysis | en_US |
dc.subject | Stochastic processes | en_US |
dc.title | A sample-rebalanced outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 22686 | - |
dc.identifier.epage | 22696 | - |
dc.identifier.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2970250 | - |
dcterms.abstract | Short-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.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2020, v. 8, p. 22686-22696 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000519023500001 | - |
dc.identifier.scopus | 2-s2.0-85081092704 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.description.validate | 202006 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Cai_Sample-Rebalanced_Outlier-Rejected_K-Nearest.pdf | 2.25 MB | Adobe PDF | View/Open |
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