CLUSTERING AND MULTINOMIAL LOGIT ANALYSIS OF FACTORS INFLUENCING HOUSEHOLD RESIDENTIAL LOCATION CHOICE IN THE WASHINGTON METROPOLITAN AREA

Abstract

The vast range of location alternatives and the preference heterogeneity have made it challenging to model, analyse, and predict the location choice process. In this study, we propose a two-step analytical model to focus on lowering the magnitude of these choices. The Transportation Planning Board's 2007-2008 household survey was used in the Washington metropolitan area, consisting of 3722 Traffic Analysis Zones (TAZ). First, location choice alternatives were clustered based on TAZs into homogeneous groups. These TAZs were categorised based on accessibility to public transport, population density, and employment density. Then, the Multinomial Logit (MNL) model was employed to allow the interpretation of the relationship between the clustered areas and the socio-economic characteristics. Four clustering algorithms were compared in terms of efficiency, and the mini-batch k-means performed the best based on the silhouette coefficient. Overall, households tend to prefer suburban areas as household size and the number of owned vehicles increase. Urban areas were selected with an increase in income, number of household workers, number of unemployed looking for a job, number of part-time employees, number of retirees, and the presence of university students. This paper contributes to the current trend of using unsupervised algorithms in the urban planning literature.

Keywords

• residential location choice; clustering; mini-batch k-means; discrete choice