Stephane
thanks again for looking at this.
stephanehess wrote: ↑14 Jan 2022, 16:57
I'm still not clear what you are trying to do here exactly, or why you want a nested logit model for it. Nested logit is not a model of sequential choice behaviour. So it would not model the choice of tour type followed by mode, as the dependent variable in your model is the mode
Actually, the dependent variable is a combination of tour complexity and transport mode: simple car, simple transit, complex car, ...
Alternatively, instead of using the combined DVs as above, one could use a bivariate model with two separate dependent variables: (1) mode choice and (2) tour complexity, as Ye & Pendyala (2007) did. They write:
The relationship between these two aspects of travel behavior is represented in this paper by considering three different causal structures: one structure in which the trip chaining pattern is determined first and influences mode choice, another structure in which mode choice is determined first and influences the complexity of the trip chaining pattern, and a third structure in which neither is predetermined but both are determined simultaneously. The first two structures are estimated within a recursive bivariate probit modeling framework that accommodates error covariance. The simultaneous logit model is estimated for the third structure that allows a bidirectional simultaneous causality.
Is it possible to specify a bivariate model in Apollo?
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stephanehess wrote: ↑14 Jan 2022, 16:57
But making the coefficients nest specific will not help. The problem is that the attributes are the same
Ok, I think I finally get it. I was trying to follow what Hensher & Reyes (2000) did in their paper, where they applied MNL, NL and MMNL to Sydney HTS data to study trip chain choices. They combined different trip chaining patterns (H-W-H, H-W-NW-H, etc.) and two modes (car, PT) into 11 mode-chain combinations. So did I, by creating 6 alternatives from 2 levels of complexity and 3 modes.
What I did differently, however, is that I started with a "base" model with only travel time and cost (with a plan to expand it), and no other variables, and got stuck at the confusing outcomes that I obtained. As you noticed in a previous post, I tried to model a choice between e.g. transit on a simple tour and transit on a complex tour without actually having any variable affecting this choice, because I only had time and cost, which are identical in this case. Whereas Hensher & Reyes operated with characteristics of the individuals (age, income, ...) and alternative-specific coefficients, which made it possible to capture e.g. the effect of age on the choice of car for a simple tour and for a complex tour separately.
I need to modify the utility functions (= add sociodemographic variables), so that the utility of simple and complex can be different. I will test it with MNL and then NL and see whether it performs better.