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Trip Generation Model based on Geographically Weighted Regression  

Kim, Jin-Hui (연세대학교 도시공학과)
Park, Il-Seop ((주)한국스마트카드)
Jeong, Jin-Hyeok (연세대학교 도시공학과)
Publication Information
Journal of Korean Society of Transportation / v.29, no.2, 2011 , pp. 101-109 More about this Journal
Abstract
In most of the urbanized cities, socio-economic attributes tend to cluster as patterns of similarity in space, namely spatial autocorrelation, by agglomeration forces. The classical linear regression model, the most frequently adopted in the trip generation step, cannot sufficiently represent this effect. In order to take into account the effect properly, we need a model which adequately deals with the spatial dependence patterns. In this study, the Geographically Weighted Regression (GWR) model is adopted as an alternative method for the local analysis of relationships in multivariate data sets; that is GWR extends this traditional regression framework by estimating local rather than global parameters. This study shows the existence of spatial effects in the production and attraction of home base/non-home based trips through the GWR model using travel data collected in Daegu metropolitan area. Furthermore, LISA is employed to verify the fact that the local spatial autocorrelation exists.
Keywords
Geographically Weighted Regression; Spatial Autocorrelation; Local Indicator of Spatial Association; Spatial Dependence; Trip Generation Model;
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