Author(s)

Xiyu Pang¹, Cheng Wang¹, Guolin Huang²

¹School of Information Engineering, Shandong Jiaotong University, Jinan, China.
²Department of Intelligent Traffic Engineering, Yigou Software Technolog Co. Ltd., Jinan, China.

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.

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

Pang, X. , Wang, C. and Huang, G. (2016) A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm. Journal of Transportation Technologies, 6, 200-206. doi: 10.4236/jtts.2016.64020.