Emergency Medical Services (EMS) constitute a crucial pillar of today's cities by providing urgent medical responses to their citizens. Their study is often conducted via simulation, as the assessment of planning decisions is generally unfeasible in the existing systems. However, such models can become computationally expensive to run. Thus, metamodels can be used to approximate the simulation results. In this work, a simulation metamodelling strategy supported on an active learning scheme is proposed to analyse the survival rate of a simulated EMS. The exploration process is guided through a series of grids towards simulation input regions whose output results match a specific survival rate defined a priori. This provides an efficient way of exploring the search space by channelling the computational effort to the most important input values, supporting the advantages of these methodologies in the EMS field, where their application is still seldom to the best of our knowledge.
|Journal||Transportmetrica A: Transport Science|
|Publication status||Accepted/In press - 2023|
Bibliographical noteFunding Information:
We acknowledge the support of the Portuguese public funding agency, Fundação para a Ciência e a Tecnologia (FCT, I.P.), under grant number PD/BD/128047/2016. Additionally, we would like to extend our thanks to the anonymous reviewers for their insightful comments and suggestions.
© 2022 Hong Kong Society for Transportation Studies Limited.
- Active learning
- emergency medical service
- emergency response
- gaussian processes
- simulation metamodelling