Lifestyle Segmentation Variables as Predictors of Home-Based Trips for Atlanta,Georgia,Airport

Abstract

This research investigated the influence of demographic and socio-economic factors on air travel demand by using a unique data set purchased from a credit-reporting agency. Linear regression models based on lifestyle segmentation variables were used to predict air passenger trips for Hartsfield–Jackson International Airport in Atlanta, Georgia. The study focused on predicting trips that originated from or terminated at residences in Atlanta's 13-county metropolitan area. The lifestyle regression models were compared with regression models based on income, because the latter were similar to the regression models currently used by the Atlanta Regional Commission to predict home-based airport passenger trips. The results provide directional evidence for using lifestyle clusters over income groups in predicting airport passenger trips. The evidence suggests that alternative data sources with adequate information for lifestyle segmentation can improve airport passenger models. The discussion points out the need for air passenger surveys to collect information about the number of annual air trips a surveyed individual takes.

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