Toward the Understanding of Urban Travel Behavior Through the Classification of Daily Urban Travel/activity Patterns |
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Page 86
... number of clusters is based on the implicit hypothesis that gen- eral categories of urban travel behavior exist , and can be identified by analyzing empirical data regarding observed or reported behavior . A ... Number of Groups number 86 9.
... number of clusters is based on the implicit hypothesis that gen- eral categories of urban travel behavior exist , and can be identified by analyzing empirical data regarding observed or reported behavior . A ... Number of Groups number 86 9.
Page 87
Eric Ivan Pas. 0 1 I - 100 I Number of Groups number of objects being analyzed . 87 is initially considered as a cluster ... Number of Groups 1 Hypothesized General Relationship Between Percent Information Explained and Number of Groups.
Eric Ivan Pas. 0 1 I - 100 I Number of Groups number of objects being analyzed . 87 is initially considered as a cluster ... Number of Groups 1 Hypothesized General Relationship Between Percent Information Explained and Number of Groups.
Page 91
... group are maximally homogeneous and different from the objects in other groups . A number of issues arise in connection with the application of Ward's algorithm in this research . These issues , common to most hier- archical ...
... group are maximally homogeneous and different from the objects in other groups . A number of issues arise in connection with the application of Ward's algorithm in this research . These issues , common to most hier- archical ...
Common terms and phrases
activity behavior activity patterns Activityd analyzed approach Burnett and Hanson Chapter Charles River Associates classes of daily classification cluster centroids conceptual framework considered contingency table daily travel daily travel/activity behavior daily travel/activity patterns defined described differential weighting eigenroots eigenvectors employed employment status equation examined explanatory variables Figure gender group of representative home-based household hypothesized identified individual information explained inter-object linear logit model linkages log-linear model mean square difference measure method of principal mode mode choice multinomial logit number of clusters number of groups number of stops objects obtained parameters positive semi-definite primary sample real Euclidean space relationships response variable results reported roles root mean square secondary attributes secondary sample selected sequence set of travel/activity similarity index similarity matrix single representative pattern small number socio-demographic characteristics step sum of squares Table traffic analysis zone travel patterns travel/activity pattern types trip urban travel behavior urban travel demand Ward's algorithm Σ Σ