Recent advances in Bayesian modeling have led to stunning improvements in our ability to flexibly and easily model complex high-dimensional data. Flexibility comes from the use of a very large number of parameters without fixed dimension. Priors are placed on the parameters to avoid over-fitting and sensibly guide the search in model space for appropriate data-driven model choice. Modern computational, high dimensional search methods (in particular Markov Chain Monte Carlo) then allow us to search the parameter space. This paper introduces the application of BART, Bayesian Additive Regression Trees, to modelling trip durations. We have survey data on characteristics of trips in the Austin area. We seek to relate the trip duration to features of the household and trip characteristics. BART enables one to make inferences about the relationship with minimal assumptions and user decisions.
- Ensemble modeling
- Markov Chain Monte Carlo
ASJC Scopus subject areas
- Civil and Structural Engineering