  We consider the problem of calibration and uncertainty analysis for
activity-based transportation simulators. Activity-Based Models (ABMs) rely on
statistical modeling of individual travelers' behavior to predict higher-order
travel patterns in metropolitan areas. Input parameters are typically estimated
from traveler surveys using maximum likelihood. We develop an approach that
uses a Gaussian Process emulator to calibrate those parameters using traffic
flow data. Our approach extends traditional emulators to handle the
high-dimensional and non-stationary nature of transportation simulators. We
introduce a deep learning dimensionality reduction model that is jointly
estimated with Gaussin Process model to approximate the simulator. We
demonstrate the methodology using several simulated examples as well as by
calibrating key parameters of the Bloomington, Illinois model.
