Spatial detection of organization under evacuation situation

Cyrille Bertelle (1) , Antoine Dutot (1) , Michel Nabaa (1) , Damien Olivier (1) , Pascal Mallet (2)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 CODAH

Recent events have shown that our agglomerations are vulnerable in front of emergency situations. There are a lot of factors to consider and one of them is the structure of towns generated by road networks. One way to study these structures is to modelize the networks by graphs and to use the theory developed on them. Recently, some works have studied the detection of communities in large graphs. A community in a graph is a set of connected nodes strongly connected to each other and less connected to nodes from other communities. The aim of detection is to identify communities according to a predefined criteria but without specifying the number of communities and the size of communites. The known algorithms find communities but without taking into account the time. We propose a dynamic algorithm of communities detection and we explain how it can be applied on Le Havre agglomeration, for example, to estimate the vu

Mots clés: complex systems; risk management; organization detection; vulnerability; CODAH

Domaines: Intelligence artificielle [cs.AI]

Cyrille Bertelle, Antoine Dutot, Michel Nabaa, Damien Olivier, Pascal Mallet. Spatial detection of organization under evacuation situation. Agent Based Spatial Simulation international workshop, 2008, Paris, France. ⟨hal-00430666v1⟩