Evacuation Demand
CE 4745 – Natural Hazards and the
Built Environment
Spring 2004
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Why Do We Want to Estimate
Evacuation Demand?
• To be able to “recreate” (or mimic) evacuation travel under alternative scenarios.
• With this ability we can:
– Estimate impact of different storm scenarios
– Test alternative policies and strategies
– Identify optimum contingency plans
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Sample setting for development of policies and strategies t
1 d
1 d road t
2 d
2 t
3 d
3
Zone 1 Zone 2 Zone 3
The load on the road network is dependent on the dd4 timing (sequencing) of the loading among zones, and the relative location of the zones.
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Examples of policies
• Reverse laning
– Where?
– When to initiate and close?
• Evacuation orders:
– Type
– Timing
– Coordination with others
4
Example of Strategies
• Phased evacuation
• Dynamic routing
• Suppression of shadow evacuation through effective public announcements
• Dynamic information systems
• Development of contingency plans
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Factors Motivating Evacuation
•
1. Risk of flooding :
– High risk – elevation < 10 foot above sea level
– Moderate risk – elevation 10-15 feet above sea level
– Low risk – elevation > 15 feet above sea level
• Evacuation rates in high risk areas are often
3 times those in low risk areas.
• People in low risk areas may not need to evacuate at all – those that do are shadow evacuees.
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Factors Motivating Evacuation
•
2. Evacuation Orders:
– Precautionary or voluntary evacuation order
– Recommended evacuation
– Mandatory evacuation
• Dependent on means of dissemination
– Of those who hear a mandatory evacuation order, over 80% have evacuated in the past.
– Of those who do not hear, less than 20% have evacuated in the past
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Factors Motivating Evacuation
•
3. Housing:
– Mobile home dwellers are more likely to evacuate than persons in other home types.
– People in high-rise buildings are less likely to evacuate than those in regular houses, all else being equal.
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Factors Motivating Evacuation
•
4. Storm Threat Information:
• The National Hurricane Center issues storm advisories (storm watches and storm warnings).
• Storm watches are issued when a storm is expected to make landfall within 36 hours.
• Storm warnings are issued when a storm is expected to make landfall within 24 hours.
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Factors Motivating Evacuation
•
5. Storm severity:
• High correlation with evacuation orders and flooding.
• Few studies have been conducted following weak storms, so information on low storm severity is sparse.
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Evacuation Rates
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Factors Influencing Decision to not Evacuate
• Protect property from storm
• Protect property from looters
• Fulfill obligation to employer
• Sometimes, peer pressure from neighbors
• < 5% said they did not have transportation
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Evacuation Demand Modeling
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Historical Development
• Three-mile Island nuclear accident
(threatened meltdown) in 1979 introduced interest in modeling evacuation.
• Interest spread to other events such as chemical spills, hurricanes, and wildfires.
• Current interest is in security of transportation infrastructure and evacuation from the aftermath of terrorist attacks.
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Existing Hurricane Evacuation
Models
Simulation models
NETVAC (MIT, 1981)
DYNEV (KLD, 1982)
Analytical models
UTPP (PBS&J, 1985)
DTA (Janson, 1985)
MASSVAC (VP, 1985)
ETIS (PBS&J, 2000)
HURREVAC (COE, 1994)
OREMS (ORNL, 1999)
TransModeler (Caliper, 2000)
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Modeling the Decision to Evacuate
• Existing models:
Participation rate type
• Category and speed of storm
• Flooding potential
• Tourist occupancy
• Proportion of mobile homes
Logistic regression type
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Low flood
Med.
Flood
High flood
Participation Rate Models
• Cross-classification type models
Category 1, Slow Category 1, Fast …
Mobile home
Low tourist
High tourist
Regular home
Low tourist
High tourist
Mobile home
Low tourist
High tourist
Regular home
Low tourist
High tourist
…
….
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Logistic Regression Models y
1 e
0
1 x
1
....
n x n
e
0
1 x
1
....
n x n where , y
probabilit y hh evacuates x
1
,
0
, x
2
..
1
..
independen t parameters v ariables
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Logistic Regression Models (2)
1
y y
e
0
1 x
...
n x n and , ln
1
y y
0
1 x
...
n x n fit with maximum likelihood
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Logistic regression model of
Hurricane Andrew Evacuation
Variable
Significance
Constant
Mobile home
Single-family house
Evacuation order
Age of respondent
Proximity to water
1.80
2.32
-1.05
1.44
-0.04
0.80
0.02
0.00
0.02
0.00
0.00
0.00
Never married
Married
-1.3
-0.80
0.02
0.04
Number of observations (hhs) = 466
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Logistic regression model of
Hurricane Andrew Evacuation (2)
Variable
Mobile home
Single-family house
Evacuation order
Age of respondent
Proximity to water
Never married
Married
Odds Ratio 95% confidence limit
10.1
0.4
4.2
2.8-36.6
0.1-0.9
2.3-7.7
0.7
2.2
0.3
0.5
0.6-0.8
1.3-3.9
0.1-0.8
0.2-1.0
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Logistic regression model of
Hurricane Andrew Evacuation (3)
Evacuated
Observed
Not
Predicted
Evacuated Not
% correctly predicted
Overall % correctly predicted
14
12
8 63.6
26 68.4
66.7
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Participation Rate Model of
Hurricane Andrew (PBS&J model of
Parish
S.W. Louisiana
Evacuation Rate (%)
Cameron
Calcasieu
Jefferson Davis
Vermillion
Acadia
Lafayette
Iberia
Iberville
Observed
100
30
14
75
35
23
58
40
Predicted
100
66
37
67
54
15
99
45
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Mean evacuation probabilities
Percent
RMSE
Comparison of Models
Observed Logistic regression
Crossclassification
37% 41% 56
0% 48% 63%
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Time of Departure
• Response rates based on:
Past evidence
Stated intentions
Functions chosen using professional judgment
Estimates based on expected rate of diffusion of warning messages
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Time of departure
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Evacuation start time,
Hurricane
Andrew,
1992,
Louisiana
Observed Mobilization
120
100
80
60
40
20
0
3 9 15 21 27 33 39 45 51 57 63 69
27
81
Hour evacuation started
Mobilization Start Times
• Evacuation start times,
Hurricane
Andrew,
1992,
Louisiana
20%
15%
10%
5%
0%
3 9 15 21 27 33 39 45 51 57 63 69 81
Hour evacuation started
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Trip Distribution
• Professional judgment based on past evacuation patterns:
– Default dispersion factors for each county or evacuation zone
– Spreadsheet-based model
• Spatial interaction model such as the
Gravity model
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Trip Distribution
• Common factors determining destination:
– Relatives and friends (50-70%)
– Hotels/motels (15-25%)
– Public shelters (5-15%)
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Trip Assignment
• Route selection paradigms:
– Myopic behavior
– User or System Optimal behavior
– Combined myopic and imposed behavior
– Imposed behavior according to evacuation plan
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Trip Assignment
• Common methods:
– Microsimulation
– Static User Equilibrium
• Emerging methods
– Dynamic traffic assignment
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Crucial areas for research
• Spatial and temporal data:
– Route choice
– Destination
– Departure time
– Clearance time
– Volumes and speeds
• Real-time data
• Dynamic traffic assignment
– Large networks
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