Active learning metamodeling for policy analysis: Application to an emergency medical service simulator.

Simulation approaches constitute a well-established tool to model, understand, and predict the behavior of transportation systems, and ultimately to assess the performance of transportation policies. Due to their multidimensionality nature, such systems are not often approachable through conventional analytic methods, making simulation modeling the only reliable tool of study. Nevertheless, simulation models can turn out to be computationally expensive when embedded with enough detail. An immediate answer to this shortcoming is the use of simulation metamodels that are designed to approximate the simulators’ results.