Automobile Ownership Model that Incorporates Captivity and Proximate Covariance

The modeling effort in this paper is distinguished from previous automobile ownership models primarily by the use of dogit ordered generalized extreme value (DOGEV) model rather than the commonly used multinominal logit and ordered probit/logit models. Comparing to other models, the DOGEV model has two distinct features. First, it recognizes the ordinal nature of the automobile ownership levels (zero car, one car, two cars, and three or more cars) by allowing them to be correlated in close vicinity (i.e., proximate covariance - ownership levels that are close to each other in the ordering have error terms that are correlated). Second, it allows a household’s automobile ownership choice to be captive or constrained to a particular automobile ownership level and, therefore, avoiding potential misspecification of the choice sets for individual households. The modeling approach was based on a behavioral analysis that explained the factors influencing household automobile ownership decisions in the New York City area, a highly urbanized environment. The estimation results uncover the sensitivity of household automobile ownership choice to transit accessibility, urban forms, traffic congestion, parking cost and availability, and the levels of access to opportunity sites through non-motorized transportation. They also show that New York City automobile ownership data were well analyzed by the DOGEV model. Particularly, evidence of captivity and ordering (proximate covariance) in the choice set may suggest an additional source of misspecification in the existing automobile ownership literature.