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.