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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1180

Title: Principal component analysis of urban traffic characteristics and meteorological data
Authors: Nagendra, S M Shiva
Khare, Mukesh
Keywords: Factor analysis
Data reduction
Air quality control region
Autocorrelation
Issue Date: 2003
Citation: Transportation Research Part D: Transport and Environment, 8(4), 285-297
Abstract: Principal component analysis (PCA) is used to analyze one-year traffic, emission and meteorological data for an urban intersection in the Delhi. The 1997 data include meteorological, traffic and emission variables. In urban intersections the complexities of site, traffic and meteorological characteristic may result in a high cross correlation among the variables. In such situations, PCA can provide an independent linear combination of the variables. Here it is used to analyze 1, 8 and 24 h average emission, traffic and meteorological data. It shows that four principal components for the 24 h average have the highest loadings for traffic and emission variables with a strong correlation between them. PC loadings for the 1 and 8 h data indicate the least variation among them.
URI: http://eprint.iitd.ac.in/dspace/handle/2074/1180
Appears in Collections:Civil Engineering

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