RITS Laboratory Kaan Ozbay, Ph.D. Associate Professor, Rutgers University, Civil & Environmental Engineering Dept. 623 Bowser Road, Piscataway, NJ M. Anil Yazici Graduate Assistant, Rutgers University, Civil & Environmental Engineering Dept. 623 Bowser Road, Piscataway, NJ Modeling of Transportation Evacuation Problems for Better Planning of Disaster Response Operations

RITS Laboratory Evacuation? “mass physical movements of people, of a contemporary nature, that collectively emerge in coping with community threats, damages, or disruptions” by E. L. Quarantelli, founder of Disaster Research Center.

RITS Laboratory Strategies Against a Disaster Control of the threatening event itself Control of human settlement patterns Development of forecasting techniques and warning systems that generate a protective response by those whose threatened  Subjects of disaster preparedness Reference: Perry, R., Lindell, M., and Greene, M. (1981). Evacuation planning in emergency management. Lexington Books, Lexington, Mass.

RITS Laboratory Types of Evacuations Voluntary Recommended Mandatory  The issue of such evacuation orders involve legal aspects heavily Reference: Wolshon B., Urbina E., Levitan M., National Review of Hurricane Evacuation Plans and Policies, LSU Hurricane Center Report, 2001.

RITS Laboratory Evacuation Modeling 1970s first attempts mostly for hurricane evacuation 1979, a milestone: Nuclear accident in Three Miles island Evacuation studies focus on nuclear plant threats 1990s, emphasis is directed towards hurricanes again Recent Tsunamis and earthquakes in Asia brought the network connectivity issue into consideration What will happen to all those evacuated people?  Shelter/supply location-allocation. Selected References: Chester G. Wilmot and Bing Mei, “Comparison of Alternative Trip Generation Models for Hurricane Evacuation”, Natural Hazards Review, November 2004, pp Sherali, H. D., Carter, T. B. and Hobeika, A. G., “A Location-Allocation Model and Algorithm for Evacuation Planning under Hurricane/Flood Conditions”, Transportation Research Part B, Vol. 25(6), 1991, pp Chang S.E. and Nobuoto N., “Measuring Post Disaster Transportation System Performance: the 1995 Kobe Earthquake Comparative Perspective”, Transportation Research PartA, Vol35, 2001, pp

RITS Laboratory 3 Critical Questions What is the clearance time required to get the hurricane-vulnerable population to safe shelter? Which roads should be selected? What measures can be used to improve the efficiency of the critical roadway segments? Reference: Donald C. Lewis, “Transportation Planning for Hurricane Evacuations”, ITE Journal, August 1985, pp31-35

RITS Laboratory Evacuation Modeling, A Simple Scheme Destination and Route Assignment Evacuation Demand Shelters Demand Generation Contra-flow Supply Logistics Sensitivity of Behavioral Models Operational and Structural Aspects Assignment Under Link Capacity Uncertainties

RITS Laboratory Major Parameters Affecting Evacuation Demand under Hurricane Conditions Baker (1991) studies 12 hurricanes from 1961 to 1989 in almost every state from Texas through Massachusetts. –Risk Level (Hazardousness) of the area –Actions by public authorities –Housing –Prior perception of personal risk –Storm specific threat factor Reference: Earl J. Baker, “Hurricane evacuation behavior”, International Journal of Mass Emergencies and Disasters, Vol.9, No.2, 1991, pp

RITS Laboratory Evacuee Behavior Individual decision process consists –Whether to evacuate; –When to evacuate; –What to take; –How to travel; –Route of travel; –Where to go; and –When to return References: Alsnih R., Stopher P.R., “A Review of the Procedures Associated With Devising Emergency Evacuation Plans”, TRB Annual Meeting, Sorensen, J.H., Vogt, B.M., and Mileti, D.S. (1987), “Evacuation: An Assessment of Planning and Research”, Oak Ridge National Laboratory, report prepared for the Federal Emergency Management Agency Washington D.C. Relates to Evacuation Demand Relates to Traffic Assignment

RITS Laboratory Approaches for Determining Evacuation Demand Empirical, expertise based approaches Sigmoid response curves (S-Curves) Artificial Neural Network Models Hazard / Survival Models Logit Models References: Haoqiang Fu, “Development of Dynamic Travel Demand Models For Hurricane Evacuation”. PhD Thesis, Louisiana State University, Mei B., “Development of Trip Generation Models of Hurricane Evacuation”. MS Thesis, Louisiana State University, 2002.

RITS Laboratory Related Studies Carried Out by the Rutgers CEE Research Team Evacuation Demand Analysis –Ozbay K., Yazici M.A. and Chien S. I-Jy. “Study Of The Network-Wide Impact Of Various Demand Generation Methods Under Hurricane Evacuation Conditions”. Proceedings of the 85th Annual Meeting of the Transportation Research Board, Washington, D.C., –Ozbay K. and Yazici M.A., “Analysis of Network-wide Impacts of Behavioral Response Curves for Evacuation Conditions”, Proceedings of the IEEE ITSC 2006 Conference, DTA with Stochastic Network Link Capacities –Yazici M.A. and Ozbay K., “Determination of Hurricane Evacuation Shelter Capacities and Locations with Probabilistic Road Capacity Constraints”, Accepted for Presentation at the 86th Annual Meeting of the Transportation Research Board, Washington, D.C., Shelter Supply Logistics –Ozbay K. and Ozguven E.E., “A Stochastic Humanitarian Inventory Control Model for Disaster Planning”, Accepted for Presentation at the 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.

Simple Evacuation Network for Multiple Origin Single Destination Evacuation Routes Demand Origin Destination Source: NJ Office of Emergency Management

RITS Laboratory Multiple-Origin Multiple-Destination Cell Transmission Model Source: Yazici M.A. and Ozbay K., “Determination of Hurricane Evacuation Shelter Capacities and Locations with Probabilistic Road Capacity Constraints”, Accepted for Presentation at the 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.

RITS Laboratory Simple SO DTA Formulation SO DTA in Compact Format SO DTA with Probabilistic Capacity Constraints

RITS Laboratory Demand Sensitivity Analysis Cell Transmission Based (CTM) System Optimal Dynamic Traffic Assignment (SO DTA) is used. Choice of demand model changes the evacuation performance measures significantly (e.g. Clearance Times, Average travel times). Even using simplistic S-Curve only, under Rapid-Medium-Slow response, the results change significantly. Demand loading scheme plays a very important role.

RITS Laboratory Stochastic Link Capacity Analysis Singlr demand profile  S-Curve is used within CTM based SO DTA framework. Probabilistic link capacities are assigned to represent flooding, incidents etc. on the network during evacuation SO DTA formulation is extended with probabilistic capacity constraints and pLEP method proposed by Prekopa is used for solution. The network flows change considerably when probabilistic analysis is performed. The required capacity of the shelters also change with probabilistic assignment.

RITS Laboratory Summary of Important Findings The demand sensitivity analysis show that the choice of demand curves impact clearance and average travel times, especially in case of a link capacity reduction. The probabilistic SO DTA shows that overall network flows and the number of people arriving each shelter are mainly affected by the probability of link failures. The number of people in each shelter is the main component required for the determination of required supply (logistics) as well as the structural and operational aspects of these shelters.

RITS Laboratory Future Work Modify existing demand models based on available data to fit NJ facts. Run evacuation scenario using a micro- simulation model for comparison with the analytical results obtained from the SO-CTM model Extend the probabilistic link capacity analysis to include other stochasticities such as demand uncertainty. Test robustness of the results for a more accurate and real size network

RITS Laboratory Thank you