Evacuation Demand
CE 4745 – Natural Hazards and the
Built Environment
Spring 2004
1

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
2

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.
3

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
5

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.
6

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
7

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.
8

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.
9

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.
10

Evacuation Rates
11

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
12

Evacuation Demand Modeling
13

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.
14

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
16

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
…
….
17

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
18

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
19

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
22

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
23

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
25

Time of departure
26

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
29

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
32

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
33