TY - GEN
T1 - Risk based staged evacuation planning
AU - Li, J.
AU - Ozbay, K.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=83755196428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83755196428&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2011.6082993
DO - 10.1109/ITSC.2011.6082993
M3 - Conference contribution
AN - SCOPUS:83755196428
SN - 9781457721984
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2021
EP - 2026
BT - 2011 14th International IEEE Conference on Intelligent Transportation Systems, ITSC 2011
T2 - 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011
Y2 - 5 October 2011 through 7 October 2011
ER -