Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

One of the actions usually conducted to limit exposure a hazardous event is evacuation area that subject effects itself. This involves modifications both demand (a large number users all want move together) and supply (the transport network may experience changes in capacity, unusable roads, etc.). In order forecast traffic evolution during an evacuation, natural choice adopt approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give deterministic prediction future conditions, whereas evacuations are considerable uncertainty. The aim present paper describe for decision support emergencies directly predicts time-evolution probability evacuating from area, formulated within discrete-time stochastic process modelling framework. applied small artificial case as well real-life network, where we estimate users’ probabilities reach desired safe destination analyze time dependent risk factors scenario.