A Bayesian sample selection model with a binary outcome for handling residential self-selection in individual car ownership

Existing literature has applied the sample selection modeling approach to disentangle the influence of the built environment (BE) and residential self-selection (RSS) on travel behavior. However, a limitation of the existing sample selection models is that they can handle only continuous or ordinal outcomes. The contribution of this study is twofold. First, we develop a sample selection model that can handle binary travel behavior outcomes in the RSS context. When the travel behavior outcome is binary, this approach's potential parameter identification issue can become serious. We employ a non-flat prior and Watanabe-Akaike information criterion in the Bayesian framework to address this issue. Second, we apply this proposed model to travel survey data in Kumamoto City, Japan, to disentangle the BE influence of a neighborhood type and RSS on car ownership. The neighborhood type is defined as the neighborhood being either less than 1,500 m (A) or greater than 1,500 m (B) from a station. We reveal that the true influence of the neighborhood type results in a mere 2.1 percentage point decrease in the car ownership probability. Additionally, we find that the share of the total BE influence (including the RSS influence) owing to RSS on the householder's car ownership is 45.7%. The proposed model is a new and useful tool for quantifying the influence of BE and the relative influence of RSS on binary travel behavior.