How do traffic and demand daily changes define urban emergency medical service (uEMS) strategic decisions?: A robust survival model

Abstract

This paper presents a methodology to locate vehicle base stations using robust optimization to address daily traffic and demand changes, which are due to what we define as city dynamics. The model allows us to better understand how these daily changes affect an urban emergency medical service (uEMS) response system.The methodology incorporates two steps. The first step uses scenario-based optimization and survival function theory to locate vehicle base stations, whereas the second step uses agent-based simulation to assess the solution performance and compare it with less robust and non-survival prone solutions. The proposed models are tested for different situations using real data from the city of Porto.The results of the sensitivity analysis of the model show the relevance of understanding the dynamics of cities and how they impact uEMS response systems. Useful insights regarding the number of stations and the average response time are addressed together with the minimum number of stations required for different maximum response time limits and different survival coefficients.Finally, we conclude on how a robust solution improves response time by accounting for city dynamics, and how a heterogeneous survival based approach benefits victims’ by properly measuring the system response in terms of the victim’ outcomes.