Toward the Understanding of Urban Travel Behavior Through the Classification of Daily Urban Travel/activity Patterns |
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Page 5
... Activity Category Distribution of Stops by Time of Day Principal Coordinates Analysis Results Second Phase Analysis . 126 126 · 127 • 128 129 • - 131 5. 8 Representative Patterns ... Activity Pattern Types ( 12 Cluster Solution , Primary ...
... Activity Category Distribution of Stops by Time of Day Principal Coordinates Analysis Results Second Phase Analysis . 126 126 · 127 • 128 129 • - 131 5. 8 Representative Patterns ... Activity Pattern Types ( 12 Cluster Solution , Primary ...
Page 18
... activity behavior may be explained by particular in- dividual attributes . The methodology developed and implemented ... patterns . The output of this step is a description of each daily travel / activity pattern in a multidimensional ...
... activity behavior may be explained by particular in- dividual attributes . The methodology developed and implemented ... patterns . The output of this step is a description of each daily travel / activity pattern in a multidimensional ...
Page 36
... activity patterns are undertaken by many different people . A fundamental premise of the framework described below is that the variation in daily travel / activity behavior may usefully be decomposed into two components . First , we ...
... activity patterns are undertaken by many different people . A fundamental premise of the framework described below is that the variation in daily travel / activity behavior may usefully be decomposed into two components . First , we ...
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