Clustering Daily Metro Origin-Destination Matrix in Shenzhen China
The development of information technology gives rise to explosive growth of the amount of data.As a result,a more effective data mining method in pattern recognition is called into existence,which can properly reflect the inherent daily activity structure of metro travelers.This study is aimed to enrich the traditional clustering methods and provide practical information in dealing with traffic volume variation to the metro system operations.In this study,daily metro origin-destination (OD) data come from smart card records of Shenzhen,China,which cover 290 days and 118 stations.Principal component analysis (PCA) and singular value decomposition (SVD) are applied to conduct dimensionality reduction.Affinity propagation is then chosen to cluster the dimensionality reduced matrix to identify demand patterns of the metro OD matrix.Eleven representative categories are clustered and shown.
metro OD matrix,PCA,Affinity propagation,cluster analysis
Chao YANG Fenfan YAN Xiangdong XU
School of Transportation Engineering, key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Caoan Road, Shanghai, 201804, China
国际会议
重庆
英文
422-432
2015-03-21（万方平台首次上网日期，不代表论文的发表时间）