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

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dc.contributor.authorNagendra, S M Shiva-
dc.contributor.authorKhare, Mukesh-
dc.identifier.citationTransportation Research Part D: Transport and Environment, 8(4), 285-297en
dc.description.abstractPrincipal 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.en
dc.format.extent153345 bytes-
dc.subjectFactor analysisen
dc.subjectData reductionen
dc.subjectAir quality control regionen
dc.titlePrincipal component analysis of urban traffic characteristics and meteorological dataen
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