Browsing by Subject "travel mode choice modeling"

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    Accommodating flexible substitution patterns in multi-dimensional choice modeling: Formulation and application to travel mode and departure time choice
    (Elsevier, 1998) Bhat, Chandra R.
    The nested logit model has been used extensively to model multi-dimensional choice situations. A drawback of the nested logit model is that it does not allow choice alternatives to share common unobserved attributes along all the dimensions characterizing the multi-dimensional choice context. This paper formulates a mixed multinomial logit structure that accommodates unobserved correlation across both dimensions in a two-dimensional choice context. The mixed multinomial logit structure is parsimonious in the number of parameters to be estimated and is also relatively easy to estimate using simulation methods. The mixed multinomial logit model is applied to an analysis of travel mode and departure time choice for home-based social-recreational trips using data drawn from the 1990 San Francisco Bay Area household survey. The empirical results underscore the need to capture unobserved attributes along both the mode and departure time dimensions, both for improved data fit as well as for more realistic policy evaluations of transportation control measures.
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    A multi-level cross-classified model for discrete response variables
    (Elsevier, 2000) Bhat, Chandra R.
    In many spatial analysis contexts, the variable of interest is discrete and there is spatial clustering of observations. This paper formulates a model that accommodates clustering along more than one dimension in the context of a discrete response variable. For example, in a travel mode choice context, individuals are clustered by both the home zone in which they live as well as by their work locations. The model formulation takes the form of a mixed logit structure and is estimated by maximum likelihood using a combination of Gaussian quadrature and quasi- Monte Carlo simulation techniques. An application to travel mode choice suggests that ignoring the spatial context in which individuals make mode choice decisions can lead to an inferior data fit as well as provide inconsistent evaluations of transportation policy measures.