Incorporating variance and covariance heterogeneity in the Generalized Nested Logit model: an application to modeling long distance travel choice behavior

The value and need for the additional modeling complexity of the HGNL model is tested in the empirical context of mode and rail service class choice behavior for long distance intercity travel. An incremental modeling approach is adopted, i.e., we start from the simple MNL model and sequentially relax some of its restrictive assumptions to estimate progressively more flexible model structures. The statistical fit and behavioral appeal of the estimated models improve substantially with each additional relaxation, strongly supporting the concept of integrating isolated generalizations.