A Sample-Rebalanced Outlier-Rejected $k$ -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting

IEEE Access

Youyi Song¹  Teng Zhou²  Lingru Cai²  Shuangyi Zhang²  Zhi Xiong²  Yidan Yu² 

[1] Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong;Department of Computer Science, College of Engineering, Shantou University, Shantou, China;

关键词: Intelligent transportation systems;    road transportation;    time series analysis;    stochastic processes;   

DOI  :  10.1109/ACCESS.2020.2970250

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown