Dynamic adaptive disaster simulation: developing a predictive model of emergency behavior using cell phone and GIS data

In this paper, we present our approach to developing a proofof-concept Dynamic Adaptive Disaster Simulation (DADS), a system capable of predicting population movements in large-scale disasters by analyzing real-time cell phone data. It has been difficult for existing computer models to accomplish such tasks-they are often too inflexible to make realistic forecasts in complex scenarios. This has led to reactive, uninformed emergency response tactics with disastrous consequences. DADS resolves these issues by continuously updating simulations with real-time data. It accomplishes this by tracing movements of cell phone users on a GIS space, then using geospatial simulation algorithms to infer regional preferences. Inferences are incorporated into agent-based simulations which model future population movements through fluid dynamics principles. Due to privacy concerns, this research utilized synthetic data that were generated to mimic the cell phone location data associated with a recent disaster. Validation techniques such as Manhattan distance show that the simulation is both internally and predictively valid. DADS can adaptively generate accurate movement predictions in disaster situations, demonstrating a modeling paradigm that is highly applicable to population modeling and to other disciplines of computer simulation.