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Xu, Y.Y., Kong, Q.J. and Liu, Y.C. (2013) Short-Term Traffic Volume Prediction Using Classification and Regression Trees. IEEE Intelligent Vehicles Symposium (IV), 493-498.

has been cited by the following article:

• TITLE: A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

  AUTHORS: Xiyu Pang, Cheng Wang, Guolin Huang

  KEYWORDS: Three-Layer, Traffic Flow Forecasting, K-Nearest Neighbor Non-Parametric Regression

  JOURNAL NAME: Journal of Transportation Technologies, Vol.6 No.4, July 22, 2016

  ABSTRACT: Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.