1 Evacuation Demand CE 4780 – Hurricane Engineering Spring 2003

1

Evacuation Demand

CE 4780 – Hurricane Engineering

Spring 2003

2

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.

3

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.

4

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

5

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.

6

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.

7

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.

8

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

9

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.

10

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).

11

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

12

Before we proceed into modeling, lets look at the

behavioral analysis that has been conducted in the past and what

has been learned.

13

Behavioral Analysis

How people have behaved during past evacuations (revealed behavior)

Or

How they say they would behave under alternative hypothetical

situations (stated behavior)

14

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.

15

Revealed Behavior in the Past

16

Past Incidence of Hurricanes on Central Gulf Coast

17

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.

18

Evacuation Rates

19

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.

20

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

21

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.

22


• 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.

23


• 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.

24

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

25

Louisiana-Mississippi 2002 Hurricane

Behavioral Response SurveyTelephone survey

Jan-Feb 2002

Earl J. Baker presentation to S.E. Louisiana officials, 2002

Jay Baker

Excludes people without phones, people who won't participate

26

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


27

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


28

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


Jay Baker

Q4 42 43 44 45

29

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


Jay Baker

Q 15 17 18

30

Cat 3, So., Intended Destinations


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


Jay Baker

Q 42 b d e

31

Cat 3, SW, Intended Destinations




Jay Baker

Q 43 b d e

32

Cat 4, So., Intended Destinations




Jay Baker

Q 44 b d e

33

Cat 4, SW, Intended Destinations




Jay Baker

Q 45 b d e

34

Routes in Georges


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*


Jay Baker

Q 19I-49 responses assigned to US 49 in Miss

35

Cat 3, So., Intended Routes


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*


Jay Baker

Q 42 f

36

Cat 3, SW, Intended Routes


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*


Jay Baker

Q 43 f

37

Would Use Alternate Route if Asked by Officials

Jefferson

Orleans

SE La.

Miss.

84

85

77

88


Jay Baker

Q 46

38

Would Avoid Interstates if Asked by Officials

Jefferson

Orleans

SE La.

Miss.

79

84

77

87


Jay Baker

Q 47

39

Intended Use if I-10, I-55 One-Way


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


Jay Baker

Q 48 a"Def Not" includes "Wouldn't Evac"

40



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


Jay Baker

Q 48 b"Def Not" includes "Wouldn't Evac"

41



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


Jay Baker

Q 48 c"Def Not" includes "Wouldn't Evac"

42

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


Jay Baker

Q 49 a

43

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


Jay Baker

Q 49 b

44

Effect on One-Way Flow on Decision to Evacuate


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


Jay Baker

Q 50

45

Concerned About Being Trapped in Traffic in Georges

Jefferson

Orleans

SE La.

Miss.

41

46

35

27


Jay Baker

Q 34

46

Heard Evacuation Information While on the Road in Georges

Jefferson

Orleans

SE La.

Miss.

38

37

38

27


Jay Baker

Q 28

47

Type of Refuge Used in Georges


Public Shelter 9 7 9 8

Hotel/Motel 31 26 28 17

Friend/Relative 50 56 56 62

Other 90 11 7 13


Jay Baker

Q 14

48

Type of Refuge Intended in Cat 3, So.


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


Jay Baker

Q 42 a

49

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


Jay Baker

Q 52

50

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


Jay Baker

Q 53

51

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


52

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


53

Evacuation Demand Modeling

54

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.

55

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)

56

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

57

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

58

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

59

Logistic Regression Models

parameters

ariablesvtindependenxx

evacuateshhyprobability

wheree

ey

0

xx

xx

nn

nn

..,

..,

,1

1

21

....

....

110

110

60

Logistic Regression Models (2)

likelihood maximumwithfit

xxy

y

and

ey

y

nn

xx nn

...1

ln

,

1

10

...10

61

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

62

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

63

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

64

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

65

Comparison of Models

Observed Logistic regression

Cross-classification

Mean evacuation probabilities

37% 41% 56

Percent RMSE 0% 48% 63%

66

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

67

Time of departure

68

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

69

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%

70

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

71

Trip Distribution

• Common factors determining destination:– Relatives and friends (50-70%)– Hotels/motels (15-25%)– Public shelters (5-15%)

72

Trip Assignment

• Route selection paradigms:– Myopic behavior– User or System Optimal behavior– Combined myopic and imposed behavior– Imposed behavior according to evacuation plan

73

Trip Assignment

• Common methods:– Microsimulation– Static User Equilibrium

• Emerging methods– Dynamic traffic assignment

74

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

Documents

1 Evacuation Demand CE 4780 – Hurricane Engineering Spring 2003