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Evacuation Demand
CE 4780 – Hurricane Engineering
Spring 2003
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Introduction
• Evacuation – what it is and why we do it.
• What it is – its ‘getting out of Dodge’
• Why we do it – avoid injury or death, sometimes to protect
property
• Pre-event and post-event evacuation.
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Types of Evacuation
• Pre-event evacuation:– When there is warning of an event– When negative effects are avoided by moving– When movement is possible and feasible– When information regarding the hazard and the
opportunity for evacuation are adequately conveyed.
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Types of Evacuation
• Post-event evacuation:– When conditions caused by the event are
lasting and harmful– When harmful conditions can be avoided by
moving away
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Travel Demand
• Term used in transportation to describe the amount of travel generated by people.
• Travel demand is expressed in terms of TRIPS and, in regular transportation planning, is expressed as the number of vehicles per day that will travel on individual links in the network.
• The demand on each link determines the needed size of the link.
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Evacuation Demand
• Is different from normal travel demand because trips are:– Less discretionary– Involves larger volumes of traffic– Timing is more important– More opportunity for intervention in travel
decisions (e.g. evacuation orders, routing directives.
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Evacuation Demand
• In normal travel demand, link volumes are important.
• In evacuation demand, link volume, the time when evacuation occurs, and the location from which it takes place, is important.
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Example
Zone 1
d
dd4
d1 d2 d3t1 t2t3
Zone 2 Zone 3
The load on the road network is dependent on the dynamic loading rates at each zone, the relative timing (sequencing) of the loading among zones, and the relative location of the zones.
road
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Evacuation Demand
• Is different from normal travel demand because the factors driving the decision to make a trip (evacuate) are different:– Normal trips are made in order to participate in
an activity (work, shop, school, recreation, etc.)– Evacuation trips are made to avoid danger and
are influenced by factors such as level of threat, vulnerability of the individual, imminence of threat, and opportunity to avoid danger.
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Evacuation Demand
• Evacuation demand = f(threat level, imminence of threat, vulnerability to threat, opportunity to evade threat)
• Some causal factors are static (e.g. vulnerability to threat) and others are dynamic (e.g. threat level).
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Why Do We Want to Estimate Evacuation Demand?
• To be able to “model” evacuation travel under alternative scenarios.
• With the ability to model we can:– Estimate impact of alternative policies and
strategies with different storm scenarios– Identify optimum contingency plans– Estimate impact of alternative investment
strategies
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Before we proceed into modeling, lets look at the
behavioral analysis that has been conducted in the past and what
has been learned.
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Behavioral Analysis
How people have behaved during past evacuations (revealed behavior)
Or
How they say they would behave under alternative hypothetical
situations (stated behavior)
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Revealed and Stated Behavior• Revealed behavior:
– Requires that an event first occur– The characteristics of the event are fixed– Not all information can be gathered (e.g. speed,
delay, route)
• Stated behavior, on the other hand:– Can be gathered at any time– Characteristics of event are not fixed– Even less information can be gathered than in
the revealed behavior case because variables describing scenarios must be limited.
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Revealed Behavior in the Past
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Past Incidence of Hurricanes on Central Gulf Coast
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Conclusion From Previous Slide
• No location more prone to hurricanes than another, other than in a regional sense.
• While general alignment of hurricane tracks are discernible, individual tracks are unpredictable.
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Evacuation Rates
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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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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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Louisiana-Mississippi 2002 Hurricane
Behavioral Response SurveyTelephone survey
Jan-Feb 2002
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Sample DesignLouisiana
• Orleans Parish N=400• Jefferson Parish N=400• SE Louisiana N=400
– St. Tammany So. of I-10/I-12 N=134– St. Bernard N=133– Plaquemines N=133
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Sample DesignMississippi
Hancock Harrison Jackson TOTAL
Cat 1-2 25 64 45 134
Cat 3-5 20 60 53 133
Non-surge 20 63 50 133
TOTAL 65 187 148
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Evacuation RatesGeorges and Hypotheticals
Jefferson Orleans SE La. Miss.
Georges 47 44 52 37*
Cat 3, So. 58 73 62 50
Cat 3, SW 48 60 53 42
Cat 4, So. 70 80 72 64
Cat 4, SW 62 72 66 53
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Destinations in Georgesfrom Louisiana
Jefferson Orleans SE La.
Own Parish 21 30 16
Other La.
42 29 48
Mississippi 15 24 17
Thru Miss.* 11 10 11
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Cat 3, So., Intended Destinations
Jefferson Orleans SE La.
Own Parish 23 38 23 Other La. 33 20 37 Miss. 15 16 17 Thru Miss. 9 7 12 TX/OK 10 10 4 Other 3 1 1 Don’t Know 9 8 7
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Cat 3, SW, Intended Destinations
Jefferson Orleans SE La.
Own Parish 25 38 24 Other La. 26 17 34 Miss. 17 19 18 Thru Miss. 17 11 12 TX/OK 5 4 3 Other 1 1 2 Don’t Know 11 11 8
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Cat 4, So., Intended Destinations
Jefferson Orleans SE La.
Own Parish 20 33 22 Other La. 30 18 31 Miss. 16 17 17 Thru Miss. 13 10 12 TX/OK 9 8 5 Other 1 2 2 Don’t Know 12 13 11
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Cat 4, SW, Intended Destinations
Jefferson Orleans SE La.
Own Parish 22 31 22 Other La. 27 17 31 Miss. 18 20 17 Thru Miss. 14 13 12 TX/OK 4 3 5 Other 1 1 2 Don’t Know 14 15 11
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Routes in Georges
Jefferson Orleans SE La. Miss.
I-10 E 7 27 16 27 I-10 W 53 45 27 13 I-12 E 3 3 6 0 I-12 W 3 12 15 2 I-55 N 30 17 19 4 I-59 N 7 15 16 4 I-49 N 3 3 3 0* US 49 2 2 <1 27*
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Cat 3, So., Intended Routes
Jefferson Orleans SE La. Miss.
I-10 E 15 23 19 21 I-10 W 44 48 33 14 I-12 E <1 2 4 0 I-12 W <1 4 7 1 I-55 N 29 15 19 6 I-59 N 8 12 21 10 I-49 N 5 2 4 0* US 49 0 2 0 50*
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Cat 3, SW, Intended Routes
Jefferson Orleans SE La. Miss.
I-10 E 22 30 27 19
I-10 W 29 36 24 12
I-12 E 0 2 5 0
I-12 W 1 4 5 0
I-55 N 34 18 16 9
I-59 N 10 14 17 14
I-49 N 3 4 5 0*
US 49 <1 <1 <1 58*
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Would Use Alternate Route if Asked by Officials
Jefferson
Orleans
SE La.
Miss.
84
85
77
88
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Would Avoid Interstates if Asked by Officials
Jefferson
Orleans
SE La.
Miss.
79
84
77
87
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Intended Use if I-10, I-55 One-Way
Jefferson Orleans SE La.
Def. Yes
48
55
52
Prob. Yes
30
25
29
Prob. Not
4
6
7
Def. Not*
9
8
6
Don’t Know
8
6
6
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Intended Use if I-10, I-59 One-Way
Jefferson Orleans SE La.
Def. Yes
39
50
47
Prob. Yes
27
28
28
Prob. Not
15
6
11
Def. Not*
11
9
8
Don’t Know
9
8
7
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Intended Use if I-10, I-49 One-Way
Jefferson Orleans SE La.
Def. Yes
39
48
46
Prob. Yes
30
26
26
Prob. Not
11
11
13
Def. Not*
9
8
7
Don’t Know
11
8
8
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Intended Use if I-55 One Way
Mississippi
Definitely Yes 36
Probably Yes 24
Probably Not 16
Definitely Not/Won’t Evac
14
Don’t Know 11
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Intended Use if I-59 One Way
Mississippi
Definitely Yes 36
Probably Yes 22
Probably Not 16
Definitely Not/Won’t Evac
14
Don’t Know 12
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Effect on One-Way Flow on Decision to Evacuate
Jefferson Orleans SE La. Miss.
Evac. More Likely 47 43 41 37
Evac. Less Likely 4 3 3 4
No Effect 42 49 50 54
Don’t Know 7 6 7 5
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Concerned About Being Trapped in Traffic in Georges
Jefferson
Orleans
SE La.
Miss.
41
46
35
27
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Heard Evacuation Information While on the Road in Georges
Jefferson
Orleans
SE La.
Miss.
38
37
38
27
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Type of Refuge Used in Georges
Jefferson Orleans SE La. Miss.
Public Shelter 9 7 9 8
Hotel/Motel 31 26 28 17
Friend/Relative 50 56 56 62
Other 90 11 7 13
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Type of Refuge Intended in Cat 3, So.
Jefferson Orleans SE La. Miss.
Public Shelter 16 21 18 14
Hotel/Motel 32 25 25 17
Friend/Relative 30 37 38 53
Other/Don’t Know 22 17 19 16
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Effect of Hearing That Shelters, Lodging Are Full Before Evacuating
Mississippi Stay Home 15 Go to Frnd/Rel in Same Loc. 25 Go to Different Location 8 Go Farther in Same Direction 23 Leave Earlier to Avoid That 20 Don’t Know 9 Other 1
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Effect of Hearing That Roads Are Heavily Congested Before Evacuating
Mississippi Stay Home 18 Use That Route Anyhow 6 Use Different Route 31 Leave Early to Avoid That 34 Don’t Know 10 Other <1
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Summary
• 25% to 30% of SE La evacuees to go to or thru Mississippi
• Higher than average in storms from SW
• Higher than average in stronger storms
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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Summary
• People receptive to using alternate routes• People receptive to one-way routes• One-way routes could increase number
evacuating• 1/3 of evacuees already hearing evacuation
information via car radio after evacuating• Full roads, refuges could deter some from
leaving
Earl J. Baker presentation to S.E. Louisiana officials, 2002
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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 Analytical models
NETVAC (MIT, 1981) UTPP (PBS&J, 1985)
DYNEV (KLD, 1982) Standard rates
MASSVAC (VP, 1985) ETIS (PBS&J, 2000)
HURREVAC (COE, 1994)
OREMS (ORNL, 1999)
TransModeler (Caliper, 2000)
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Main Factors Prompting Evacuation
• Post-storm Behavioral Surveys suggest the main factors are: Storm severity
Storm proximity
Vulnerability to flooding
Evacuation orders
Type of housing
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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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Participation Rate Models• Cross-classification type models
Category 1, Slow Category 1, Fast …
Mobile home
Regular home
Mobile home
Regular home
…
Low tourist
High tourist
Low tourist
High tourist
Low tourist
High tourist
Low tourist
High tourist
….
Low flood
Med. Flood
High flood
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Logistic Regression Models
parameters
ariablesvtindependenxx
evacuateshhyprobability
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Logistic Regression Models (2)
likelihood maximumwithfit
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and
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Logistic regression model of Hurricane Andrew Evacuation
Variable Significance
Constant 1.80 0.02
Mobile home 2.32 0.00
Single-family house
-1.05 0.02
Evacuation order 1.44 0.00
Age of respondent -0.04 0.00
Proximity to water 0.80 0.00
Never married -1.3 0.02
Married -0.80 0.04
Number of observations (hhs) = 466
Likelihood ratio index = 0.25
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Logistic regression model of Hurricane Andrew Evacuation (2)
Variable Odds Ratio
95% confidence limit
Mobile home 10.1 2.8-36.6
Single-family house
0.4 0.1-0.9
Evacuation order 4.2 2.3-7.7
Age of respondent 0.7 0.6-0.8
Proximity to water 2.2 1.3-3.9
Never married 0.3 0.1-0.8
Married 0.5 0.2-1.0
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Logistic regression model of Hurricane Andrew Evacuation (3)
Predicted%
correctly predicte
d
Overall %
correctly predicte
d
Evacuated
Not
Observed
Evacuated 14 8 63.6
66.7Not 12 26 68.4
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Participation Rate Model of Hurricane Andrew (PBS&J model of
S.W. LouisianaParish Evacuation Rate (%)
Observed Predicted
Cameron 100 100
Calcasieu 30 66
Jefferson Davis 14 37
Vermillion 75 67
Acadia 35 54
Lafayette 23 15
Iberia 58 99
Iberville 40 45
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Comparison of Models
Observed Logistic regression
Cross-classification
Mean evacuation probabilities
37% 41% 56
Percent RMSE 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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Observed Mobilization
• Evacuation
start time,
Hurricane
Andrew,
1992,
Louisiana
Hour evacuation started
816963575145393327211593
Cum
ulat
ive
perc
ent e
vacu
ated
120
100
80
60
40
20
0
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Mobilization Start Times
• Evacuation
start times,
Hurricane
Andrew,
1992,
Louisiana3 9 15 21 27 33 39 45 51 57 63 69 81
Hour evacuation started
0%
5%
10%
15%
20%
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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