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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
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
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