RITS
Laboratory
Modeling of Transportation Evacuation
Problems for Better Planning of Disaster
Response Operations
Kaan Ozbay, Ph.D.
Associate Professor,
Rutgers University,
Civil & Environmental
Engineering Dept.
623 Bowser Road, Piscataway,
NJ
kaan@rci.rutgers.edu
M. Anil Yazici
Graduate Assistant,
Rutgers University,
Civil & Environmental Engineering
Dept.
623 Bowser Road, Piscataway, NJ
yazici@eden.rutgers.edu
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.
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RITS
Laboratory
Strategies Against a Disaster
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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
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Reference:
Perry, R., Lindell, M., and Greene, M. (1981). Evacuation planning in emergency
management. Lexington Books, Lexington, Mass.
RITS
Laboratory
Types of Evacuations
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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.
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RITS
Laboratory
Evacuation Modeling
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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:
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Chester G. Wilmot and Bing Mei, “Comparison of Alternative Trip Generation Models for
Hurricane Evacuation”, Natural Hazards Review, November 2004, pp 170-178.
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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.439-452.
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Chang S.E. and Nobuoto N., “Measuring Post Disaster Transportation System
Performance: the 1995 Kobe Earthquake Comparative Perspective”, Transportation
Research PartA, Vol35, 2001, pp.475-494.
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RITS
Laboratory
3 Critical Questions
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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
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RITS
Laboratory
Evacuation Modeling,
A Simple Scheme
Operational and
Structural Aspects
Demand Generation
Evacuation
Demand
Contra-flow
Shelters
Destination
and Route
Assignment
Sensitivity of
Behavioral
Models
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Supply
Logistics
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.
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Risk Level (Hazardousness) of the area
Actions by public authorities
Housing
Prior perception of personal risk
Storm specific threat factor
Reference:
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Earl J. Baker, “Hurricane evacuation behavior”, International Journal of Mass
Emergencies and Disasters, Vol.9, No.2, 1991, pp 287-310
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RITS
Laboratory
Evacuee Behavior
• Individual decision process consists
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Relates to
Whether to evacuate;
When to evacuate;
Evacuation
What to take;
Demand
How to travel;
Relates to
Route of travel;
Traffic
Where to go; and
Assignment
When to return
References:
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Alsnih R., Stopher P.R., “A Review of the Procedures Associated With Devising
Emergency Evacuation Plans”, TRB Annual Meeting, 2004.
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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.
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Approaches for Determining
Evacuation Demand
RITS
Laboratory
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Empirical, expertise
based approaches
Sigmoid response
curves (S-Curves)
Artificial Neural
Network Models
Hazard / Survival 
Models
Logit Models
Pt   1
1 exp (t  H) 
References:
•Haoqiang Fu, “Development of Dynamic Travel Demand Models For Hurricane
Evacuation”. PhD Thesis, Louisiana State University, 2004.
•Mei B., “Development of Trip Generation Models of Hurricane Evacuation”. MS Thesis,
Louisiana State University, 2002.
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RITS
Laboratory
Related Studies Carried Out by the
Rutgers CEE Research Team
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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., 2006.
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,
2006.
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., 2007.
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.
Destination
Evacuation
Routes
Simple
Evacuation
Network for
Multiple
Origin Single
Destination
Demand
Origin
Source: NJ Office of
Emergency Management
RITS
Laboratory
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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

min
t
i xi
t
s. t. A  b,
T  Q ,
xi  0, yij  0, i, j    , t  T
t
t
SO DTA in
Compact Format
 xit 
where    t 
 yij 
min
t i xi
t
s.t. A  b,
T1  Q
PT2     p,
xi  0, yij  0,i, j    ,t  T
t
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t
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.
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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.
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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.
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RITS
Laboratory
Future Work
• Modify existing demand models based on
available data to fit NJ facts.
• Run evacuation scenario using a microsimulation 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
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RITS
Laboratory
Thank you