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Behavioural choices in
evacuation during floods:
Azarel Chamorro Obra1
Wisinee Wisetjindawat2
Motohiro Fujita3
Nagoya Institute of Technology
Fujita Laboratory
A preliminary study in Metropolitan
Area of Valencia, Spain
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
1 Research Student
2 Assistant Professor
3 Professor
I. Metropolitan Area of
Valencia (MAV)
I. Location
II. Geography
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Europe
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METROPOLITAN AREA OF VALENCIA (MAV)
Location
Metropolitan Area of Valencia
(MAV)
Spain
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METROPOLITAN AREA OF VALENCIA (MAV)
Location
I. Metropolitan Area of
Valencia (MAV)
I. Location
II. Geography
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Metropolitan Area of Valencia
(MAV)
Metropolitan Area of Valencia (red)
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METROPOLITAN AREA OF VALENCIA (MAV)
Location
I. Metropolitan Area of
Valencia (MAV)
I. Location
II. Geography
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Metropolitan Area of Valencia
(MAV)
Alluvial plain
Several gullies
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METROPOLITAN AREA OF VALENCIA (MAV)
Geography
I. Metropolitan Area of
Valencia (MAV)
I. Location
II. Geography
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Metropolitan Area of Valencia
(MAV)
Alluvial plain
Several gullies
Large lagoon (Albufera)
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METROPOLITAN AREA OF VALENCIA (MAV)
Geography
I. Metropolitan Area of
Valencia (MAV)
I. Location
II. Geography
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Metropolitan Area of Valencia
(MAV)
Alluvial plain
Several gullies
Large lagoon (Albufera)
More than 1,500,000 inhabitants
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METROPOLITAN AREA OF VALENCIA (MAV)
Geography
I. Metropolitan Area of
Valencia (MAV)
I. Location
II. Geography
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Metropolitan Area of Valencia
(MAV)
Extreme phenomenon: Cold Drop
Beginning of Autumn (September-October).
Occasionally 200-800 l/m2 in few hours
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Cold Drop
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Historical Records
From year 1300 more than 48 large floods
were reported.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Historical records
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
1300 1400 1500 1600 1700 1800 1900 2000
1300 1400 1500 1600 1700 1800 1900 2000
From year 1300 more than 48 large floods
were reported.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Historical records
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
THE Flood 1957
1300 1400 1500 1600 1700 1800 1900 2000
From year 1300 more than 48 large floods
were reported.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Historical records
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Valencia Flood, 1957
From year 1300 more than 48 large floods
were reported.
Water heights in Valencia City, 1957
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Historical records
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Historical Records
In the last years,
vulnerability has
been greatly reduced.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Countermeasures
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
However, for long
return period floods,
the risk for the
inhabitants is still
there
In the last years,
vulnerability has
been greatly reduced.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Countermeasures
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
However, for long
return period floods,
the risk for the
inhabitants is still
there
In the last years,
vulnerability has
been greatly reduced.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
FLOOD HAZARD
Countermeasures
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
I. Cold Drop
II. Historical Records
III. Countermeasures
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Objective
To model the behavioural choices of the
inhabitants of the region in case of the issue of
an evacuation alert due to long return period
inundations:
1. To find a relationship between significant
variables and main decisions.
2. To assess the response from inhabitants
during an evacuation.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METHODOLOGY
Objective
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
I. Objective
II. Survey
III. Statistical Analysis
IV. Results
V. Discussion
VI. Conclusions
Methodology
Data source: Internet survey
Sample: University students of the MAV
609 accepted responses
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METHODOLOGY
Survey
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
I. Objective
II. Survey
III. Statistical Analysis
IV. Results
V. Discussion
VI. Conclusions
Data source: Internet survey
Sample: University students of the MAV
609 accepted responses
Survey scenario:
Evacuation alert has been issued due to
incoming floods expected for 2 or more days.
At least, heights from 50 cm are expected.
Individuals are initially in their homes.
Inhabitants have 12 hours to evacuate before
the storm.
Shelter locations are well-known by
inhabitants.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METHODOLOGY
Survey
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
I. Objective
II. Survey
III. Statistical Analysis
IV. Results
V. Discussion
VI. Conclusions
Statistical analysis: Logistic regression
Binary (for 2 options)
Multinomial (for 3 options)
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METHODOLOGY
Statistical analysis
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
I. Objective
II. Survey
III. Statistical Analysis
IV. Results
V. Discussion
VI. Conclusions
𝑃𝑖 =exp(𝑉𝑖)
𝑖𝑘 exp(𝑉𝑖)
𝑉𝑖 = 𝛼1𝑥𝑖,1 + 𝛼2𝑥𝑖,2 +⋯+ 𝛼𝑛𝑥𝑖,𝑛
4 main decisions in study:
𝑈1: Evacuation decision:
Leaving
Staying
𝑈2: Destination
Shelter
Others
𝑈3: Transportation
By car
Others
𝑈4: Departure time
Early departure
Regular departure
Late departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
METHODOLOGY
Statistical analysis
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
I. Objective
II. Survey
III. Statistical Analysis
IV. Results
V. Discussion
VI. Conclusions
Results: 𝑼𝟏: Evacuation decision
38%
62%
Evacuation decision
Staying Leaving
Model 1: Evacuating decision
N=609
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 1
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 1
Evacuating
Staying
Evacuating decision
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Dependent
variableIndependent variable α
T
value
U1:
Evacuating
Being a female 0.6328 3.72**
Have experienced floods -0.3766 -2.43**
Living in Valencia City 0.2937 2.15**
Living below 4th floor 0.3326 2.13**
Being high informed 0.4829 1.99**
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 1
Evacuating
Staying
Evacuating decision
N=609
Hit ratio: 63.22%I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
RESULTS
Model 1
Evacuating
Staying
Evacuating decision
N=609
Hit ratio: 63.22%
Dependent
variableIndependent variable α
T
value
U1:
Evacuating
Being a female 0.6328 3.72**
Have experienced floods -0.3766 -2.43**
Living in Valencia City 0.2937 2.15**
Living below 4th floor 0.3326 2.13**
Being high informed 0.4829 1.99**
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Evacuating
Staying
Evacuating decision
N=609
Hit ratio: 63.22%
Dependent
variableIndependent variable α
T
value
U1:
Evacuating
Being a female 0.6328 3.72**
Have experienced floods -0.3766 -2.43**
Living in Valencia City 0.2937 2.15**
Living below 4th floor 0.3326 2.13**
Being high informed 0.4829 1.99**
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 1
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Dependent
variableIndependent variable α
T
value
U1:
Evacuating
Being a female 0.6328 3.72**
Have experienced floods -0.3766 -2.43**
Living in Valencia City 0.2937 2.15**
Living below 4th floor 0.3326 2.13**
Being high informed 0.4829 1.99**
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 1
Evacuating
Staying
Evacuating decision
N=609
Hit ratio: 63.22%I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 1: Evacuating decision
Females are more likely to evacuate than males.
Researches conducted in US claimed that this is
due to “constructed gender differences and
perceived risk”1.
"Have experienced floods" is not a factor that
leads people to evacuate. It can be considered as
a belief of low need to evacuate (there has never
been an evacuation) and might also be due to
the young age of the respondents (lack of
experience).
1 J.M. Bateman et al (2002)
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 1
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 2: Destination
N=376
27%
73%
Destination
Going to a shelter
Not going to a shelter
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Results: 𝑼𝟐: Destination50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
Model 2: Destination
Going to a shelter
Other placesI. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 2: Destination
N=376
Hit ratio: 77.66%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U2: Going to a
shelter
Floods for more than 4 days 0.3556 5.659**
Having children -0.8844 -3.774**
Being aware of threat -1.031 -2.521**
Having elders -0.5191 -2.154**
Going to a shelter
Other places
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 2: Destination
N=376
Hit ratio: 77.66%
Going to a shelter
Other places
Dependent
variableIndependent variable α T value
U2: Going to a
shelter
Floods for more than 4 days 0.3556 5.659**
Having children -0.8844 -3.774**
Being aware of threat -1.031 -2.521**
Having elders -0.5191 -2.154**
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 2: Destination
N=376
Hit ratio: 77.66%
Going to a shelter
Other places
Dependent
variableIndependent variable α T value
U2: Going to a
shelter
Floods for more than 4 days 0.3556 5.659**
Having children -0.8844 -3.774**
Being aware of threat -1.031 -2.521**
Having elders -0.5191 -2.154**
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U2: Going to a
shelter
Floods for more than 4 days 0.3556 5.659**
Having children -0.8844 -3.774**
Being aware of threat -1.031 -2.521**
Having elders -0.5191 -2.154**
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
Model 2: Destination
N=376
Hit ratio: 77.66%
Going to a shelter
Other placesI. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 2: Destination
The only variable that encourage inhabitants to
go to a shelter is “floods for 4 or more days”.
This probably means that only individuals who
do not have another place to go would go to
shelter.
Large families (“Having Children” and “Having
elders”) are prone to go to other places. The
reason could be the special care and necessities
required by them, and the belief that could be
not provided correctly in shelters.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 2
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 3: Transportation
N=376
72%
19%
7%
2%
Transportation
Car
Walking
Public
transportation
Others
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 3
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 3
Model 3: Transportation
By car
OthersI. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 3: Transportation
N=376
Hit ratio: 82.12%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α
T
value
U3: Leaving by
car
Going with the family 2.259 9.223**
Going to shelter -2.953 -9.698**
Living in “Horta Sud” 1.468 1.717*
Picking up a relative 0.4725 1.686*
By car
Others
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 3
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 3: Transportation
N=376
Hit ratio: 82.12%
By car
Others
Results: 𝑼𝟑: Transportation
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α
T
value
U3: Leaving by
car
Going with the family 2.259 9.223**
Going to shelter -2.953 -9.698**
Living in “Horta Sud” 1.468 1.717*
Picking up a relative 0.4725 1.686*
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 3
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 3: Transportation
N=376
Hit ratio: 82.12%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α
T
value
U3: Leaving by
car
Going with the family 2.259 9.223**
Going to shelter -2.953 -9.698**
Living in “Horta Sud” 1.468 1.717*
Picking up a relative 0.4725 1.686*
By car
Others
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 3
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 3: Transportation
“Going with the family” is in a high relationship
of car usage, since automobile is the most
efficient option when different members are
moving together.
Individuals who go to a shelter are not likely to
use the car, probably because the lack of
parking space and proximity.
**>95% confidence interval*>90% confidence interval
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 3
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
N=376
31%
47%
23%
0%
20%
40%
60%
80%
100%
Early Departure
(>10h)*
Regular Departure
(10-2h)*
Late Departure
(<2h)*
*Hours before storm
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
Early Departure
Regular Departure
Late Departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
N=376
Hit ratio: 65.42%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U4: Early
Departure
Going with the family 2.252 4.431**
Being a female 1.787 2.313**
Being well-prepared -1.328 -2.013**
Being aware of threat 2.189 1.908*
U4: Regular
Departure
Going with the family 3.28 6.620**
Being a female 1.485 1.935*
Being well-prepared -1.445 -2.254**
Being aware of threat 2.010 1.754*
Early Departure
Regular Departure
Late Departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
N=376
Hit ratio: 65.42%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U4: Early
Departure
Going with the family 2.252 4.431**
Being a female 1.787 2.313**
Being well-prepared -1.328 -2.013**
Being aware of threat 2.189 1.908*
U4: Regular
Departure
Going with the family 3.28 6.620**
Being a female 1.485 1.935*
Being well-prepared -1.445 -2.254**
Being aware of threat 2.010 1.754*
Early Departure
Regular Departure
Late Departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
N=376
Hit ratio: 65.42%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U4: Early
Departure
Going with the family 2.252 4.431**
Being a female 1.787 2.313**
Being well-prepared -1.328 -2.013**
Being aware of threat 2.189 1.908*
U4: Regular
Departure
Going with the family 3.28 6.620**
Being a female 1.485 1.935*
Being well-prepared -1.445 -2.254**
Being aware of threat 2.010 1.754*
Early Departure
Regular Departure
Late Departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
N=376
Hit ratio: 65.42%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U4: Early
Departure
Going with the family 2.252 4.431**
Being a female 1.787 2.313**
Being well-prepared -1.328 -2.013**
Being aware of threat 2.189 1.908*
U4: Regular
Departure
Going with the family 3.28 6.620**
Being a female 1.485 1.935*
Being well-prepared -1.445 -2.254**
Being aware of threat 2.010 1.754*
Early Departure
Regular Departure
Late Departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Model 4: Departure time
N=376
Hit ratio: 65.42%
**>95% confidence interval*>90% confidence interval
Dependent
variableIndependent variable α T value
U4: Early
Departure
Going with the family 2.252 4.431**
Being a female 1.787 2.313**
Being well-prepared -1.328 -2.013**
Being aware of threat 2.189 1.908*
U4: Regular
Departure
Going with the family 3.28 6.620**
Being a female 1.485 1.935*
Being well-prepared -1.445 -2.254**
Being aware of threat 2.010 1.754*
Early Departure
Regular Departure
Late Departure
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
Results: 𝑼𝟒: Departure time
Model 4: Departure time
Families are more likely to departure in the
central hours (regular departure).
As expected, individuals who consider
themselves “aware of threat” try to evacuate as
soon as possible.
On the contrary, those who think that are “well-
prepared” are prone to leave near the storm
beginning (late departure).
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
RESULTS
Model 4
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
I. Model 1
II. Model 2
III. Model 3
IV. Model 4
V. Discussion
VI. Conclusions
In summary
Experience is not a key factor to lead people to
evacuate. Nevertheless, it is necessary to take in
account that the sample is compounded by
young people who probably do not have enough
experience.
Family characteristics are the most important
personal attributes for those who decide to
evacuate. This variable greatly affects the
“destination”, “transportation” and “departure
time” decision.
Those who are more aware of threat and high
informed have safer attitudes: they are prone to
evacuate more and faster.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
DISCUSSION
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Conclusions
If a successful evacuation want to be achieved
in future events, it is necessary to focus on the
consciousness related variables (the only ones
that can be externally influenced). Then it
would be necessary to:
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
CONCLUSION
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Conclusions
If a successful evacuation want to be achieved
in future events, it is necessary to focus on the
consciousness related variables (the only ones
that can be externally influenced). Then it
would be necessary to:
Raise the awareness level.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
CONCLUSION
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Conclusions
If a successful evacuation want to be achieved
in future events, it is necessary to focus on the
consciousness related variables (the only ones
that can be externally influenced). Then it
would be necessary to:
Raise the awareness level.
Provide more information about floods.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
CONCLUSION
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Conclusions
If a successful evacuation want to be achieved
in future events, it is necessary to focus on the
consciousness related variables (the only ones
that can be externally influenced). Then it
would be necessary to:
Raise the awareness level.
Provide more information about floods.
Training inhabitants to be prepared for
future events.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
CONCLUSION
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Conclusions
From the point of view of the behavioural
attitude of the surveyed, it can be stated that an
evacuation would be a feasible measure in case
of large flood.
In further researches a more representative
sample of the whole population should be
surveyed in order to extrapolate results.
However, this study provides a good starting
point.
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
CONCLUSION
I. Metropolitan Area of
Valencia (MAV)
II. Flood Hazard
III. Methodology
IV. Results
V. Discussion
VI. Conclusions
Behavioural choices in
evacuation during floods:
Azarel Chamorro Obra1
Wisinee Wisetjindawat2
Motohiro Fujita3
Nagoya Institute of Technology
Fujita Laboratory
A preliminary study in Metropolitan
Area of Valencia, Spain
50th PROCEEDINGS OF INFRASTRUCTURE PLANNING
1 Research Student
2 Assistant Professor
3 Professor