Bayesian Calibration for Activity Based Models

Transportation activity-based simulators (ABMs) represent an individual traveler's activity patterns and trips throughout the day by using nested choice models. The generated trips are then simulated in a traffic flow simulator to learn system-level patterns. These behaviorally-realistic models require a high-resolution representation of network flows and, thus, are computationally expensive. The very same flexibility which makes these simulation models appealing, also makes their calibration problems intractable, with the number of simulations required to find an optimal solution growing exponentially as the input dimension increases [90, 70]. As a result, the use of these simulators is currently limited to what-if analysis. This paper focuses on calibrating the static choice model parameters used in activity-based simulators. The goal of calibration is to find values of the simulator's input parameters θ that minimizes the deviance between observed data and simulator's outputs.