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In an urban disaster it is important to efficiently evacuate people to safety. We use evolution strategies and a probability model to route the population by optimizing their safety. The algorithm is designed to use the strengths of evolutionary computing to repeatedly optimize an evacuation under the dynamics of a disaster such as accidents blocking critical roadways, bridge collapses, debris closures, changes in safety, and people not following evacuation directions. Our model is unconcerned with specific evacuation routes but rather evolves a robust cloud of probabilities to represent best directions of escape. We show that maintaining a population of diverse solutions may allow for rapid adaptation as a disaster unfolds. The core optimization algorithm is tested using challenging test cases as well as real-world data.

Evolving a real-time evacuation for urban disaster management