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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Principal component analysis of urban traffic characteristics and meteorological data


    Contributors:


    Publication date :

    2003-01-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Urban expressway traffic accident meteorological factor sensitivity analysis method

    WANG JIANJUN / LU XIAOJUAN / WANG SAI et al. | European Patent Office | 2021

    Free access

    Principal Component Clustering Analysis Method Applied to Road Traffic Safety

    Jia, Yuanhua / Wu, Guifu / Chang, Sheng | ASCE | 2011


    Air traffic control sector traffic capacity detection method based on principal component analysis

    ZHANG JIANPING / LIU WEIDONG / LIU DAN et al. | European Patent Office | 2015

    Free access