Road traffic data bring great challenges for data processing and traffic-state analysis. The feature extraction is an effective way to make full use of road traffic data. Here, the authors explore the temporal correlation of road traffic data in time series and a new non-linear feature extraction method for road traffic data, namely, kernel principal component analysis (PCA). The low-dimensional road traffic data are mapped into the high-dimensional space based on a kernel function, and then the PCA method is applied to conduct feature extraction in the high-dimensional space. To have a clear comparison, the authors compare the kernel PCA method with a linear PCA-based feature extraction method. The results show that the extracted features under kernel PCA are more useful than PCA and the kernel PCA method has a better compression effect than PCA under the same condition.
Kernel PCA for road traffic data non-linear feature extraction
IET Intelligent Transport Systems ; 13 , 8 ; 1291-1298
2019-05-14
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Metadata by IET is licensed under CC BY 3.0
Applying Locally Linear Embedding on Feature Extraction of Traffic Flow Data
British Library Conference Proceedings | 2010
|Space traffic characteristic Kernel-KNN matching road traffic state obtain method
Europäisches Patentamt | 2015
|Image Feature Extraction Based on Kernel ICA
IEEE | 2008
|TRAFFIC BIG DATA-BASED ROAD CAPACITY EXTRACTION METHOD
Europäisches Patentamt | 2020
|