摘要
The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.
The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work, an algorithm based on kernel-k nearest neighbor （KNN） matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics （RSRTRC）. Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.
作者
徐东伟
王永东
贾利民
张贵军
郭海锋
XU Dong-wei WANG Yong-dong JIA Li-min ZHANG Gui-jun GUO Hai-feng(College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China State Key Laboratory of Rail Traffic Control and Safety （Beijing Jiaotong University）, Beijing 100044, China)
基金
Projects(LQ16E080012,LY14F030012)supported by the Zhejiang Provincial Natural Science Foundation,China
Project(61573317)supported by the National Natural Science Foundation of China
Project(2015001)supported by the Open Fund for a Key-Key Discipline of Zhejiang University of Technology,China
