In Intelligent Transportation System, one of the most difficult tasks is to manage the mass amount of data and discover useful information from them, so data mining plays an important role in extracting temporal and spatial relations in a networked system. In this paper, we propose a novel method for traffic data analysis. Kernel Principal Component Analysis (KPCA) is used to reduce data dimensionality and extract features from them, then Self-Organizing Map (SOM) is applied in the unsupervised clustering of links. Subject interpretation and regression equations are used to analyze the clustering result. Case studies on real data from UTC-SCOOT System in Beijing prove that the proposed method is effective in extracting non-linear relations between different links and revealing hidden patterns in traffic flow data. The result yielded can support further analysis, like traffic parameter forecasting and traffic flow control.
Traffic Data Analysis Using Kernel PCA and Self-Organizing Map
2006 IEEE Intelligent Vehicles Symposium ; 472-477
2006-01-01
774490 byte
Conference paper
Electronic Resource
English
Traffic Data Analysis Using Kernel PCA and Self-Organizing Map
British Library Conference Proceedings | 2006
|Kernel-Based Self-Organizing Map Clustering
British Library Online Contents | 2005
|Self-organizing traffic signals using secondary extension and dynamic coordination
Online Contents | 2014
|Self-organizing traffic lights at multiple-street intersections
British Library Online Contents | 2012
|Engineering Index Backfile | 1930
|