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http://hdl.handle.net/10397/82275
Title: | A sample-rebalanced outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting | Authors: | Cai, LR Yu, YD Zhang, SY Song, Y Xiong, Z Zhou, T |
Issue Date: | 2020 | Source: | IEEE access, 2020, v. 8, p. 22686-22696 | 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. | Keywords: | Intelligent transportation systems Road transportation Time series analysis Stochastic processes |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2020.2970250 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ 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 |
Appears in Collections: | Journal/Magazine Article |
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Cai_Sample-Rebalanced_Outlier-Rejected_K-Nearest.pdf | 2.25 MB | Adobe PDF | View/Open |
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