Real-time road traffic state prediction based on kernel-KNN

Abstract

The real-time and accurate prediction of road traffic states is the basis of information service for road traffic participants. An algorithm based on kernel K-nearest neighbors (kernel-KNN) is presented to predict road traffic states in time series in this paper. First, representative road traffic state data are extracted to build the road traffic running characteristics reference sequences. Then, kernel function of the road traffic state data sequence in time series is constructed. The current and referenced road traffic state data sequences are matched, based on which k nearest referenced road traffic states are selected and the road traffic states are predicted. Several typical road links in Beijing are considered for a series of case studies. The final experiments results prove that the road traffic states prediction approach based on kernel-KNN presented herein is feasible and can achieve a high level of accuracy.