  The lack of longitudinal studies of the relationship between the built
environment and travel behavior has been widely discussed in the literature.
This paper discusses how standard propensity score matching estimators can be
extended to enable such studies by pairing observations across two dimensions:
longitudinal and cross-sectional. Researchers mimic randomized controlled
trials (RCTs) and match observations in both dimensions, to find synthetic
control groups that are similar to the treatment group and to match subjects
synthetically across before-treatment and after-treatment time periods. We call
this a two-dimensional propensity score matching (2DPSM). This method
demonstrates superior performance for estimating treatment effects based on
Monte Carlo evidence. A near-term opportunity for such matching is identifying
the impact of transportation infrastructure on travel behavior.
