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Active learning metamodeling for policy analysis

Application to an emergency medical service simulator
: Antunes, Francisco; Soares Amorim, Marco Raul; Pereira, Francisco C.; Ribeiro, Bernadete


Simulation modelling practice and theory 97 (2019), Art. 101947, 11 S.
ISSN: 1569-190X
European Commission EC
H2020; 745673; PRISM
PRobabilistic PRedictIon for Smart Mobility under stress scenarios
Fraunhofer IAO ()

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. In this work, the authors propose a metamodeling approach based on active learning that seeks to improve the exploration process of the simulation input space and the associated output behavior. A Gaussian Process (GP) is used as a metamodel to approximate the function inherently defined by the simulation model itself. The GPs can nicely handle the uncertainty associated with their predictions, which eventually can be improved with active learning through simulation runs. This property provides a practical and efficient way to analyze the simulator’s behavior and therefore, to assess the performance of policies regarding the underlying real-world systems and services. The authors illustrate the proposed methodology using an Emergency Medical Service (EMS) simulator. Two outputs are analyzed and compared, namely, the survival rate and response time averages. The medical emergency response time recommendation of 8 min is explored as well ist relation with the survival rate. The results show that this methodology can identify regions in the simulation input space that might affect the performance and success of medical policies with regards to emergency vehicle dispatching services.