  We provide a method to solve optimization problem when objective function is
a complex stochastic simulator of an urban transportation system. To reach this
goal, a Bayesian optimization framework is introduced. We show how the choice
of prior and inference algorithm effect the outcome of our optimization
procedure. We develop dimensionality reduction techniques that allow for our
optimization techniques to be applicable for real-life problems. We develop a
distributed, Gaussian Process Bayesian regression and active learning models
that allow parallel execution of our algorithms and enable usage of high
performance computing. We present a fully Bayesian approach that is more sample
efficient and reduces computational budget. Our framework is supported by
theoretical analysis and an empirical study. We demonstrate our framework on
the problem of calibrating a multi-modal transportation network of city of
Bloomington, Illinois. Finally, we discuss directions for further research.
