# Evacuation Management System for Major Disasters
###### Abstract  
Predicting and understanding mass evacuations are important factors in disaster management and response. Current modelling approaches are useful for planning but lack of real-time capabilities to help informed decisions as the disaster event evolves. To address this challenge, a real-time Evacuation Management System (EMS) is proposed here, following a stochastic approach and combining classical models of low complexity but high reliability. The EMS computes optimal assembly points and shelters and the related network of evacuation routes using GIS-based traffic, pedestrian and routing models including damaged assets or impassable areas. To test the proper operation performances of the EMS, we conducted a case study for the Gran Canaria wildfire (August 2019--Spain). Keywords:mass evacuation; disaster management; evacuation modelling; human behaviour; traffic modelling; routing; stochastic modelling; emergency response +
Footnote †: journal: Medical  
also have the possibility to explore the evacuation performance ahead of time including the impact of different responses, resources, and incident scenarios. This requires inputs from the situation to the model, which should run significantly faster than real-time. One of the main problems in developing such models is that they are likely to be less sophisticated and produce limited information due to time constraints. The challenge is to obtain a compromise between run times and providing enough detail in the models (consistent level of granularity) to allow sufficient accuracy. The real-time decision-support systems also require processing the outputs quickly enough (immediate results), and the information provided should be easy to interpret and with a high confidence level [19]. To address this challenge, this paper introduces an EMS, which is a GIS-based evacuation decision-support system developed within the ASSISTANCE project ([[https://assistance-project.eu/](https://assistance-project.eu/)]([https://assistance-project.eu/](https://assistance-project.eu/)), accessed on 28 July 2022) that has received funding from the European Union's H2020 research and innovation programme under grant agreement No 832576. The EMS is integrated by different modules following well-studied, traditional approaches in conjunction with a global stochastic approach in order to predict adaptative mass evacuation strategies including features such as assembly points, shelter locations, routing, and pedestrian and vehicular evacuation. Therefore, the main strength of this system lies in the ability to simulate comprehensive evacuation processes in the case of a major disaster by considering simple models that are not usually considered in mass evacuations, which are sufficiently accurate to be able to operate in real time in response stages and to obtain reliable results, unlike classical models that increase the accuracy of the results but require a higher computational load and simulation times, and can only be applied in the planning phases. These evacuation processes to manage the transit of the displaced population from households or assembly points to shelters using a particular routing evacuation approach is widely studied [9], but in our particular case, after analysing different historical large-scale disasters, we conclude that, in almost all cases, at some stage of the process, this ends up becoming a guided mixed evacuation from assembly points to shelters, in most cases partially assisted. Therefore, disaster relief shelters to provide private and secure short-term stay places for the displaced population have to be modelled. These shelters are not arbitrary selected, but are chosen via three criteria: (1) shelter purpose [20], which in our case will only consider a short-term stay (emergency or temporary shelters), (2) specific facilities and equipment considering applicability, accessibility and safety characteristics [21; 22] and (3) spatial location ensuring coverage, familiarity and crowding conditions [23].