Capturing the impact of uncertain events in emergency evacuation time estimation is an important issue for public officials to avoid unexpected delays and related losses of life and property. However, most of the current studies in evacuation planning only focus on exogenous uncertainties, such as flooding damage in a hurricane, but ignore uncertainties caused by endogenously determined risks, or so called flow-related risks. This paper proposes an analytical framework along with an efficient solution methodology to evaluate the impact of endogenously determined risks in order to estimate evacuation time. We incorporate the probability function of endogenously determined risks in a cell-based macroscopic evacuation model. A network flow algorithm based on the sample average approximation approach is used as part of the solution procedure. Finally, a sample network is used to demonstrate the salient features of the proposed stochastic model and solution procedure.
- Endogenously determined risks
- Evacuation planning
- Network flow algorithm
ASJC Scopus subject areas
- Computer Networks and Communications
- Artificial Intelligence