Active Learning for Input Space Exploration in Traffic Simulators

Urban environments are systems of overwhelming complexity and dynamism, involving numerous variables and idiosyncrasies which not usually easy to model from a functional perspective. Simulation modeling is a common and well-accepted approach to study such systems, specially those that prove to be too complex to be analyzed by standard analytic methods. However, such urban simulation models can become computationally very expensive to run. To address this drawback, simulation metamodels can be employed to approximate the underlying simulation function. In this paper, we propose a batch-mode active learning strategy based on Gaussian Processes metamodeling that searches for the most informative data points in batches with respect to their corresponding predictive variances. These points are selected in such a way that they originate from different high variance neighborhoods. Eventually, this allows us to analyze the simulation output behavior with fewer simulation requests. Using an illustrative traffic simulation example, the results show that the proposed restricted batch-mode strategy is able to increase the simulation input space exploration efficiency in comparison with standard batch-mode strategies.