Risk based staged evacuation planning

J. Li, K. Ozbay

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Determination of an optimal departure schedule is the key issue for a successful staged emergency evacuation for reducing unnecessary network congestion and decreasing the evacuation clearance time. However, flow-related stochastic events, such accidents and short-term traffic breakdowns, bring numerous challenges for developing a reliable evacuation plan with an optimal departure schedule. In order to assist the decision makers for evaluating potential planning risks, this study proposes a modeling framework and a corresponding solution approach that incorporates flow-related accidents and breakdowns into a staged emergency evacuation model. A bi-level stochastic programming problem is formulated. In the upper level, we introduce planning risk by using Mean-Mean Absolute Deviation (Mean-MAD), which is a common measure employed for risk-averse planning. In the lower level, System Optimum Dynamic Traffic Assignment (SO-DTA) formulation, proposed in [16], is modified as a tool for quantifying the performance of the proposed multistage stochastic traffic assignment approach. The applicability of the proposed framework and solution approach is illustrated by a numerical example designed to depict salient features of the proposed risk-based planning methodology.

Original languageEnglish (US)
Title of host publication2011 14th International IEEE Conference on Intelligent Transportation Systems, ITSC 2011
Number of pages6
StatePublished - 2011
Event14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011 - Washington, DC, United States
Duration: Oct 5 2011Oct 7 2011

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Other14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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