Hybrid agent-based methodology for testing response protocols

Abstract

In recent years we have seen multiple incidents with a large number of people injured and killed by one or more armed attackers. Since this type of violence is difficult to predict, detecting threats as early as possible allows to generate early warnings and reduce response time. In this context, any tool to check and compare different action protocols can be a further step in the direction of saving lives. Our proposal combines features from continuous and discrete models to obtain the best of both worlds in order to simulate large and crowded spaces where complex behavior individuals interact. With this proposal we aim to provide a tool for testing different security protocols under several emergency scenarios, where spaces, hazards, and population can be customized. Finally, we use a proof of concept implementation of this model to test specific security protocols under emergency situations for real spaces. Specifically, we test how providing some users of a university college with an app that informs about the type and characteristics of the ongoing hazard, affects in the safety performance.

Vision and Crowdsensing Technology for an Optimal Response in Security: Project results

“…By using as reference values used in similar spaces, or obtained as mean values in experiments with social forces models, the max-flow value for the doors was set up to 2.5, and 20 for corridors. All the implementation has been described in [2] and the software is publicly available in [3].…”

“…In addition, to train the system, another dataset was generated with synthetic images from a video game designed using the Unity 3D engine, which allowed for the automatic generation and labelling of images in which a weapon appeared. Both datasets are publicly available 2 .…”