Discovering explicit scale-up criteria in crisis response with decision mining

In modern society, incidents such as road incidents or fires, occur daily, requiring institutions to develop appropriate management protocols to react quickly. When an incident requires coordination among different emergency services or it is estimated to have a significant impact on the population, crisis management processes are used. The overall aim of crisis management is to provide the right resources to manage the incident and return to a normal situation as soon as possible. However, the decision to scale up an incident to a crisis level is often left to the experience of operational commanders without explicit criteria or guidelines. In this research, we propose a framework combining data-driven decision-mining approaches with implicit knowledge formalization techniques to discover explicit criteria to support decision-makers in crisis response. We tested our approach in a case study at VRU, the safety region for the region Utrecht, in The Netherlands. The obtained results show that the approach has been able to extract criteria acknowledged by the decision-makers, which is the first step to developing appropriate guidelines to steer the decisional process of the incident scaling up.