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《Acta Electronica Sinica》 2015-05
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A Spectral Clustering Method for Big Trajectory Data Mining with Latent Semantic Correlation

LIAO Lü-chao;JIANG Xin-hua;ZOU Fu-min;HE Wen-wu;QIU Huai;School of Information Science and Engineering,Central-South University;Fujian Key Laboratory for Automotive Electronics and Electric Drive ,Fujian University of Technology;Fujian Transport Information &Telecommunications Center,Fujian Communication Department;  
To facilitate traffic understanding,planning and management optimization,we present a new spectral clustering method( TSSC) for big trajectory data mining based on latent semantic correlation. First,a matrix model is proposed to represent vehicle trajectories and the underlying road network with a grid-vehicle matrix,which is then transformed to a low-dimensional semantic subspace with random projection. Second,through matrix decomposition we extract hidden characteristics of the mass trajectory data and construct a similarity matrix for road network cells. Third,we adopt and implement a fast spectral clustering method to discover road network clusters based on the similarity matrix in the semantic space. Finally,we evaluate our approach with a large trajectory data set collected by the Fujian Communications Department,which has 19,719 vehicles and a total mileage of more than 14 million kilometers. Experiment results show that the approach can efficiently cluster the road network with traffic context semantic information derived from massive trajectory data. The approach is capable to discover inherent characteristics of complex road networks and provide insights for traffic planning and management optimization.
【Fund】: 国家自然科学基金(No.61304199 No.41471333 No.61101139);; 福建省高校杰出青年科研人才计划(No.JA14209);; 福建省自然科学基金(No.2013J01214 No.2012J01247);; 福建省科技重大专项专题项目(No.2011HZ0002-1 No.2013HZ0002-1);; 福建省交通科技计划(No.201318)
【CateGory Index】: TP311.13
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