Estimating panel effects in probabilistic representations of dynamic decision trees using Bayesian generalized linear mixture models

When collecting panel data, there is a need to acknowledge that responses do not represent independent measurements. The known apparatus in transportation research offers several opportunities to estimate panel effects for well-known and widely applied models such as hazard and dynamic logit models. However, the transportation research community is not endowed with a rich set of methods to account for panel effects in dynamic probabilistic decision tables, which have been used as a formalism for the representation of decision heuristics. Building on scarce prior work in statistics, the authors elaborate an approach to estimate panel effects in dynamic probabilistic decision trees with multinomial action states. The approach uses an iterative estimation procedure between CHAID-based probabilistic tree induction and Bayesian generalized linear mixture modeling. When extracting the dynamic probabilistic decision trees, it is assumed that the panel effects are known, while it is assumed that the fixed components are known when estimating the Bayesian generalized linear mixture model. This iterative process continues until convergence is reached. A Monte Carlo technique is used to navigate between aggregate choice probabilities and individual level multinomial choices. The suggested approach is illustrated using Plug-in Electric Vehicle (PEV) user’s charging station choice as an example. Results support the potential value of the suggested approach.