NHESSNatural Hazards and Earth System ScienceNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus GmbHGöttingen, Germany10.5194/nhess-15-687-2015A spatiotemporal optimization model for the evacuation of the population exposed to flood hazardAlaeddineH.houssein.alaeddine@hotmail.frhttps://orcid.org/0000-0001-6816-3621SerrhiniK.MaiziaM.NéronE.Laboratoire Citères de l'Université de Tours, Tours, FranceLaboratoire Informatique de l'Université de Tours, Tours, FranceH. Alaeddine (houssein.alaeddine@hotmail.fr)30March20151536877012December20145January2015–11March2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/15/687/2015/nhess-15-687-2015.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/15/687/2015/nhess-15-687-2015.pdf
Managing the crisis caused by natural disasters, and
especially by floods, requires the development of effective evacuation
systems. An effective evacuation system must take into account certain
constraints, including those related to traffic network, accessibility, human
resources and material equipment (vehicles, collecting points, etc.). The
main objective of this work is to provide assistance to technical services
and rescue forces in terms of accessibility by offering itineraries relating
to rescue and evacuation of people and property. We consider in this paper
the evacuation of an urban area of medium size exposed to the hazard of
flood. In case of inundation, most people will be evacuated using their own
vehicles. Two evacuation types are addressed in this paper: (1) a preventive
evacuation based on a flood forecasting system and (2) an evacuation during
the disaster based on flooding scenarios. The two study sites on which the developed
evacuation model is applied are the Tours valley (Fr, 37), which
is protected by a set of dikes (preventive evacuation), and the Gien valley
(Fr, 45), which benefits from a low rate of flooding (evacuation before and
during the disaster). Our goal is to construct, for each of these two sites,
a chronological evacuation plan, i.e., computing for each individual the
departure date and the path to reach the assembly point (also called shelter)
according to a priority list established for this purpose. The
evacuation plan must avoid the congestion on the road network. Here we
present a spatiotemporal optimization model (STOM) dedicated to the
evacuation of the population exposed to natural disasters and more
specifically to flood risk.
Introduction
This paper addresses the problem of the evacuation of people exposed to a risk
of flooding. Arranged with specific urban databases (flooding scenarios,
census of population, transport network, etc.), the model developed here
enables us to compute the evacuation routes to be taken by the affected
population while minimizing the total evacuation time. This optimization
model must take into account several constraints, such as accessibility
, roads capacity, capacity of safe areas,
vulnerability of the population exposed to risk (lists of priorities,
scheduling) and vulnerability of transport network (roads cut during floods)
.
The occurrence time of a flood event is known in advance (at least 48 h
before), a period during which the population concerned should exit the area
. The evacuation process must be prepared, i.e., fixing a departure date and escape route for each
household to the associated safe area.
Phases of the evacuation process .
The evacuation of stakes (buildings, nuclear centers, hospitals, etc.)
exposed to natural hazards (floods, earthquakes, tornadoes, volcanoes,
tsunamis, etc.) was and is a problem that occupies a high position in the
hierarchy of priorities of governments of multiple countries. The
US Department of Homeland Security defines the evacuation of an area as
“the organized, phased, and supervised withdrawal, dispersal, or removal of
civilians from dangerous or potentially dangerous areas, and their reception
and care in safe areas” (National Incident Management System, December 2008, p. 139).
The evacuation of areas prone to natural (such as floods) or technological
(such as nuclear risk) hazards can be one of the measures for the protection
of urban issues. It requires systems and decision support tools
mainly to protect the lives of people. The reorganization of routing traffic
in densely populated areas is a very important element for a massive
emergency evacuation. Safety and minimization of delays and total travel time
are the main aspects to be taken into account during an evacuation. This
reorganization of the traffic can be modeled and solved by tools of
operations research .
The total evacuation time can be defined as the time needed for an evacuation
process that includes the warning time, preparation time, travel time
between dangerous and safe areas and evacuation verification time
. show
that the complex process of evacuation includes several consecutive phases
(see Fig. ).
STOM evacuation model.
Missions and necessary tasks need to be coordinated among government
and nongovernmental organizations . The realization phase
itself consists of evacuating the affected population through the network
prepared for this purpose. This includes the paths computed from areas
to be evacuated (buildings, neighborhoods, etc.) to safety areas (shelters,
assembly points, etc.). Transportation planning that includes the design and
the evaluation of transport infrastructure (highways, streets, public
transport routes, etc.) is required to ensure that the entire population
exposed to hazard has the opportunity to safely leave the risk zone. This
includes the planning of routing and the organization of traffic circulation
on evacuation routes. As only a part of the population is evacuated using
their own vehicles, adequate transportation must be provided for the other
part of the population (e.g., nursing homes, hospitals, prisons).
Since 1960 many studies have been conducted in the fields of optimization
and evacuation planning. Those works can be grouped into two: the
optimization approaches of an evacuation plan with specific objectives and
the evaluation process of existing evacuation plans to check and
validate them. While validation approaches are usually of the microscopic
type, optimization approaches are rather macroscopic (dynamic network
flow) or microscopic (traffic assignment) and manage the evacuees' movements
over time. Contrary to microscopic models , the
macroscopic methods do not take
into account human behavior and interaction between vehicles.
Indeed, evacuees are treated as a homogenous group where only common
characteristics are considered. These methods tend to minimize the total
evacuation time. Between the two previous levels (macroscopic and
microscopic), the mesoscopic methods allow us to
follow in real time the trajectory of each vehicle (its position in the
network). However, these methods do not take into account either the
behavior of evacuees or the interaction of vehicles with their environment.
We are interested in a spatiotemporal optimization model (STOM)
This study is a part of ACCELL project
funded by “la Région Centre, France” and the European Union (FEDER).
to
develop a two-stage mesoscopic model combining dynamic network flow
and traffic assignment models. This choice is a trade-off between the
accuracy of the microscopic model and the application of the macroscopic one
(modeling of large networks). The first stage concerns the development of an
evacuation scheduling system based on an established priority list, where we
evacuate, at each time slot (1 h, 0.5 h, etc.) and by priority order,
the maximum number of vehicles from each building not yet totally evacuated.
Roads capacity, predetermined evacuation paths and destination capacity must
be respected during the assignment of flow. Based on this result, a vehicles
pursuit model (VPM) is also developed in order to, first, convert the discrete
process (time slots) to a continuous one (time intervals) and, second,
avoid overlap between successive time intervals. This overlap may occur on
network roads because the sources from which flow is outgoing (incoming to
network roads) at each time slot vary over time. VPM minimizes the evacuation
departure times of buildings. The flow-dependent travel time on roads is
computed using a polynomial traffic model which is also
used to compute the capacity (maximal flow rate) on roads based on the
free-flow speed, jam density and number of lanes of roads.
Evacuation model reformulation
At time of evacuation, inhabitants of a site exposed to hazard must be
evacuated through the transport network to the assembly points equipped for
this purpose . This operation must be based on a plan
computed by an evacuation model under certain constraints (hazard,
accessibility, vulnerability, etc.). A definition of the evacuation plan,
applied to the case of the tsunami but easily applied to the case of
flooding, was given by the Intergovernmental Oceanographic Commission of
UNESCO.
The primary aim of an invoked tsunami evacuation plan should therefore be to
guide all affected persons along the evacuation routes towards safe places
(which are primarily supposed to be outside the reach of tsunami waves but
could also be inside the flooded area), also called assembly facilities or
emergency shelters, and in time (time span between alarm and arrival of first
wave taking into account for each person the distance to the next emergency
shelter) (SCHEER, VARELA, EFTYCHIDIS, 2012).
The flowchart in Fig. shows the steps of the evacuation
model STOM. The first step is the formation of the spatial input database
which includes an urban network database (highways and arterials), buildings
to be evacuated (Sect. ) and safety points
(Sect. ). The next step includes the formation of groups
of buildings by network nodes (Sect. ). The third
step concerns the reconfiguration of network according to the preferences of
decision-makers (reservation of few specific ways to rescue forces,
authorization of no entry, etc.). The fourth step of the model consists of
associating one or more shelters with each group of buildings
(Sect. ). The fifth step calculates the K-best
paths between each group of buildings and each safety point associated. The
sixth step computes an evacuation plan based on an evacuation scheduling
system and a VPM. The next step simulates and
checks the evacuation plan. Finally, a mapping of the final evacuation plan
is proposed.
The construction of the database (network, buildings, shelters, population)
required for the evacuation model is not addressed here. We focus in this
paper on showing only the veritable steps for constructing an evacuation plan.
Grouping of buildings
The first step is to assign each building to the nearest network node using
the airline distance from the centroids of buildings to the extremities of
arcs. This assignment results in the formation of groups of buildings by
network node. Note that we do not work here on either a microscopic scale
(buildings) or a macroscopic scale (neighborhood, area, etc.) but on an
intermediate scale, that is to say mesoscopic (group of buildings).
Figure shows this step on a part of the Tours valley where
buildings that are assigned to the same network node have the same color.
Assignment of buildings to nearest network nodes.
This assignment can be a source of difficulty in traffic if the distance
between the centroid of buildings and the nearest network nodes is relatively
large. The diagram in Fig. shows that this
distance (per interval classes) is low for the majority of buildings in the
Tours valley. Otherwise, intermediate nodes must be created in order to
minimize that distance.
Henceforth we define “building” as a group of buildings assigned to the same
network node.
Safety points
Safe points, shelters or assembly points are spaces equipped to receive
evacuees before or during floods and for a long or short time .
They are determined on the basis of a multi-criteria analysis (vulnerability,
bridges, etc.) and with the support of local actors (departmental
directorates, territories, prefectures, etc.) .
Thus, safe points should provide sufficient reception
capacity in terms of people and vehicles, be located in areas providing a
rapid response medical assistance and humanitarian aid, etc. The
determination of shelters in our model and as we mentioned above is not
performed by an optimization approach (selection of a number of shelters
among a set of candidates) but directly by the decision-makers. It should be
noted that the location and the number of shelters may have significant
impact on the evacuation time which, in turn, depends on two elements:
the capacity of network and the travel time.
As for buildings, assembly points are also assigned to the closest network
nodes. From these assignments we can divide network nodes into three
categories: (1) nodes grouping buildings, or node-building; (2) nodes
associated with assembly points, or node-shelter; and finally (3) all the
remaining nodes that hold nothing.
Number of buildings according to their distance from the nearest
network nodes.
It should be noted that a node-building can be a crossing node for vehicles
coming from other node-buildings. In other words, those vehicles are not
authorized to stay in intermediate nodes at the evacuation .
The last assignment related to the construction of evacuation network
corresponds to the association of each building with one or more safety
points according to the following criteria: (1) distance to hazard, or “air-line
distance”; (2) proximity of buildings from safety points, or “shortest
path”;
(3) reception capacities of assembly points in terms of vehicles and people; and
(4) existence of at least one escape route, in which assignment of buildings to safe
points is changed according to flood evolution over time (road cuts).
Figure shows the assignment of buildings of the Tours valley to two safety points (north and south).
Assignment of buildings to safety points. Buildings in green are
assigned to the northern shelter (ZRO du Nord) and the remaining buildings
(in brown) are assigned to the southern shelter (ZRO du Sud).
Determination of evacuation routes
The last step to build the evacuation network is to determine the evacuation
routes to be taken by the affected population. These paths can be provided as
input data for the evacuation model STOM. Otherwise, a paths' computation
method is applied to compute a set of k-best paths between each building
and each safety point associated (see ). The
determination of paths is performed according to two main objectives: the
minimization of total clearance time and the maximization of the acceptance
degree of these paths by the evacuees. The first objective can be achieved by
computing a large number of paths between each origin and destination. However, because the
evacuees of an origin will not accept, firstly, a large number of paths and,
secondly, long paths in terms of travel time, we determine the paths and
their number by a compromise between the two objectives announced (for more
details, please see see ). It should be noted that the
transport graph excludes certain routes reserved for rescue forces,
firefighters, etc., and/or for the evacuation of major buildings (nuclear
infrastructure, hospitals, retirement homes, etc.). Moreover, a
polynomial traffic model is adopted here to compute the capacity (maximal
flow rate) of each road link based on jam density, free-flow travel time and
number of roads lanes. This traffic model enables to compute the
flow-dependent travel time on roads.
In case of evacuation during a disaster, egress routes change over time.
Anticipating the state of the network can be helpful to manage traffic and to
determine the safest access paths to the impacted areas for rescue services
. We show in the last section of this paper and on a real site
(Gien valley) the consideration of the aspect “road cut” in STOM.
Evacuation priority list
At time of massive evacuation, transportation network does not allow a
simultaneous evacuation of all persons located in the risk zone. This
requires a complex organizational system to minimize the total
evacuation time according to an established priority list.
identified several factors that influence this priority: the
distance of regions from the hazard (hurricane center), the flooding extent
and the population density. Based on this priority list, the authors
assigned a score per region, defining the level of risk. This level of zonal
risk is also established for each building identified by its gravity center
or centroid. However, using this method, all buildings in the same area or
region have the same risk level.
In this paper, evacuation of buildings is based on a priority list similar
to that developed by . However, in addition to criteria of distance
from hazard, flooding extent and population density, we take into account
other factors including age of the population to be evacuated and distance of
buildings from both dikes and shelters. The last paragraph of this section
gives an explanation for the establishment of one of the priority lists used in STOM.
Break levees and potential damage on the Tours valley.
In addition, our priority list also depends on the spatial scale (region,
city, area, building). Indeed, at each level or scale a building has its own
priority established according to priorities of higher levels and influenced
by one or more factors. A combination of factors can be set to determine the
priority of buildings at each level.
We present here one of the evacuation priority lists. It is based on the
most exposed areas which need to be removed at first. These are the areas
which lie just behind the dikes. The map in Fig. shows
dike failures and potential damage (in red) on the site of the Tours valley. The dike failures represented are only those previously experienced
since it is impossible to predict where a rupture will occur.
We divide each evacuation zone (two evacuation zones, see
Fig. ) into three subzones ([0, 300], [300, 900] and [900, +]), where, for example, [0, 300] is the area that lies between 0 and
300 m
from the river (see Fig. ). Moreover, for reasons of
organization and behavior of people, it is desirable to evacuate people per
neighborhood according to their exposures. Priority evacuation of two
buildings located in the same neighborhood at the same level (subzone) is
defined according to their distance to rivers. In other words, the priority
building is that which is closest to the river. The neighborhood with the largest
number of buildings located in the level [0, 300] will be evacuated first,
followed by the second priority neighborhood of level 300 and so on until the last
one. After evacuating the maximum number of buildings (depending on network
capacity) located in the first level 300, we turn to the level [ 900
and 900+], repeating successively the same procedure. We repeat these three
steps (levels 300, 900 and 900+) until the evacuation of all buildings in all
neighborhoods at all levels is completed. Planning the evacuation of buildings at risk
requires, therefore, the construction of priority lists specifically tailored
for each treated area.
Division of each of the two zones (relative to the two rivers) into
three subzones. Zone [0, 300] is the area that lies between 0 and
300 m from the river.
Flow and routing optimization
The construction of an evacuation plan is the result of a two-stage model
combining between discrete and continuous process. The first phase in this
model is to schedule the evacuation of buildings based on an established priority list.
The model assigns, at each time slot (1 h, 30 min, etc.)
and by priority order, the maximum number of vehicles from each building that is not
yet evacuated. This model is subjected to road capacity, predetermined evacuation
paths and destinations capacity (see evacuation scheduling
system, Fig. ). The second phase of this model focuses on
the conversion of the discrete process (time slots) to a continuous one (time
intervals) using a developed VPM. This model, which
is based on a polynomial traffic model enabling to compute flow-dependent
travel time on roads, aims to minimize the departure times of vehicles while
avoiding overlap between successive time intervals (see vehicles pursuit
model, Fig. ). The congestion on the evacuation network causes,
of course, a reduction of the flow entering the network, and therefore queues
will be formed on several roads. In STOM, we avoid such a situation
using two steps: (1) for each time slot, the capacity (fluid regime) of
each road is respected (flow assigned is lower than or equal to the
capacity). This capacity is computed using a polynomial traffic model of the
form q=kvf1-k2kj2,
where q is the flow,
vf is the free-flow speed, k the density and kj the jam density.
The maximal flow rate (or the capacity) qm is obtained when
dqdk= 0 (see , section traffic model).
(2) As flow in dynamic network changes over time (increasing of flow followed by
a decreasing) and as the set of buildings evacuated varies from one time slot
to another, congestion on some roads may occur. We handle this problem
by developing a pursuit vehicles model that computes for each origin the
minimal evacuation departures times to avoid any traffic jams in
the network (for further details, see ). This
two-stage model was presented in a previous paper (see
).
Applications and results
Tests and experiments, as we mentioned before, were performed on two selected
study sites: Tours valley (Fr, 37) and Gien valley (Fr, 45). The first
site, protected by a system of dikes, is subjected to
a precautionary evacuation due to several factors, among them the
relatively high hydraulic flow in the case of a flood-dike breach.
Complementary to the first site, we perform an evacuation during a possible crisis on the second site (the
Gien valley).
The size of the transportation graph is not limited to a certain threshold in
STOM due to the dynamic implementation of data structures. All figures in
this section, unless otherwise stated, are generated by STOM evacuation software
developed as part of this project
The tests are performed
on an HP Pavilion dv6, Windows 7, 4 GB RAM, Intel (R) Core (TM) i5-2410M,
2.30 GHz.
.
Descriptive indicators of the network
In this section, we provide the size of graphs of the two study sites as well
some descriptive indicators on the transport network of the site of Tours valley.
We represent the size of the graphs of the two sites through their connectivity index
β, which establishes the relationship between the number of links and
the number of nodes.
Many nodes of the graphs only serve to
describe the geometry of road (curves). In a graph where edges are straight
lines (crow flies), the number of nodes must be reduced. However, it should
be noted that these intermediate nodes have helped us a lot to build groups
of buildings by assigning each building to the nearest network node.
This
index indicates, to some extent, the connectivity of network. Normally it
ranges between 0.5 and 3 . The numbers below show that the
networks of the two sites of study (Tours and Gien) are approximately 220 %
connected :
β=LS,
where L is number of nodes and S is number of arcs.
βTours=44 81419 997= 2.241
βGien=58132567= 2.264
Capacity or maximal flow rate on roads is a very important element for
building
an evacuation plan. It is computed by a polynomial traffic model based on the
free-flow speed, jam density and number of lanes of road links.
Figure shows the capacity (number of vehicles per hour)
computed on the site of Tours valley.
Capacity of roads according to free-flow speed, jam density and
number of lanes on roads.
The preliminary steps for the construction of evacuation plan I:
(a) shows the hazard levels while (b) illustrates
the destruction zones and the assignment of priority to buildings.
The preliminary steps for the construction of evacuation
plan II: (a) grouping of original buildings and association of buildings
(groups of original buildings) to safety points. (b) Calculation
of three best paths between buildings and safe points. (c) Establishment
of priorities list according to destruction zones. (d) Buildings to be
evacuated.
Evacuation scheduling of the Tours valley. Each subfigure shows
the evacuated buildings and the paths taken during the slot itself.
Preventive evacuation of the Tours valley
The scenario of dam failure is not considered, as we mentioned previously,
in the Tours valley because of the very high flood flows on this site.
Given the impossibility of estimating the accessibility of roads during the
flood, we focus only on the preventive evacuation. The simulation of the
evacuation plan presented here is based on a priority list which is in turn
based on the exposure of people per district (see
Sect. ). The preliminary steps are illustrated by
Figs. and while the evacuation scheduling per time
slot is illustrated by Fig. . Figure a
shows the four levels of hazard on this site while
Fig. b illustrates the exposure of original buildings based on the
destruction zones relative to historical dam failures (see
Fig. ). The grouping of original buildings (by assigning
each original building to the nearest network node) and the association of
each building to one shelter is given in Fig. b. Figure b shows the
evacuation network constituent of the three best paths (K= 3) computed between each
origin and destination. The evacuation priority of buildings per districts based
on destruction zones is given in Fig. c (see also Sect.
and Fig. ).
In Fig. , the different colors of each assembly
point (rotation in the direction of clockwise) show the evacuation order of
buildings associated to it.
This visualization will be strengthened in the following by another
representation showing the importance of the evacuation based on the exposure
of people per district.
Evacuation scheduling based on exposures of districts
For illustration purposes, we focus only on the evacuation of the center of
the Tours valley: the city of Tours. Figure shows
the evacuation of buildings per districts according to the established priority list
(see Fig. ). The buildings are arranged in
descending order of evacuation priority and each color corresponds to a district.
Priority list for the evacuation of buildings in the districts of Tours (exposures of districts according to destruction areas). Each
building (x axis) is represented by a circle where its size indicates the
number of vehicles evacuated from this building. Each color represents a district.
Figure shows more clearly the evacuation of
neighborhoods by their levels of exposure. We note that, at each time slot,
the neighborhoods located in the first level of danger (band of 300 m located
directly behind the dike, see Fig. ) are first removed
(Beaujardin), followed by those of the second level (La Fuye Velpeau)
and finally the neighborhoods of the third level. The three different colors
(red, yellow and blue) represent the three areas of exposure (see Sect. ).
Representation of the evacuation of neighborhoods of the city of
Tours based on their exposures. Each building (x axis) is represented by a
circle where its size indicates the number of vehicles evacuated from this
building. Each color represents an exposure level ([0, 300]; [300, 900];
[900, +]).
Figure shows that the evacuation of all
neighborhoods is not consecutive (Beaujardin, Rabelais, etc.). However,
buildings with the same level of exposure (destruction zones) in each
neighborhood were evacuated in an almost sequential manner (Beaujardin,
Febvotte, etc.).
Evacuation during the flood of the Gien valley
The evacuation of the Gien valley can take place during a flood crisis
since it concerns a slow flooding. In this section we test the
evacuation model STOM during the crisis management in the Gien valley,
complementing its application in the Tours valley (preventive evacuation).
Dynamic of hazard
The dynamic of hazard over time on the second site is illustrated in
Fig. . We note the continued accessibility of certain
network roads even after 107 h from the beginning of the flood, allowing
for a possible evacuation of some buildings not yet evacuated.
Dynamics of the hazard on the Gien valley's road
network.
Construction of the evacuation plan at the beginning of the flood: (a) shows the buildings to be evacuated (in red) while
(b) shows assignment of buildings to safety points.
Construction of the evacuation plan at the beginning of the flood:
(a) illustrates the three best evacuation paths among buildings
and shelters while (b) shows the evacuation of the valley of Gien
(each color represents a time slot).
(a) shows the buildings to be evacuated (in red) and isolated
buildings (in blue) 125 h after the beginning of the flood, while (b) shows
assignment of buildings to safety points. (c) illustrates the three best
evacuation paths among buildings and shelters.
Evacuation of the valley of Gien (each color represents a time
slot): construction of the evacuation plan 125 h after the beginning
of the flood.
Simulation of the evacuation of the Gien valley: network almost submerged
This section is devoted to the simulation of the evacuation of the Gien valley for two time periods (beginning of the flood and 125 h
later
Database provided by DREAL Centre, Orléans, France, 45.
).
The evacuation in real time during the crisis is not simulated in STOM
because of the lack of dynamic information on the network status. However,
this simulation does not seem necessary on this site since it is not a mass
evacuation. The time slot chosen to illustrate the scheduling of the
evacuation is of 1.5 min because the total evacuation time
computed being less than 30 min (Figs. –). This duration is justified by the relatively low number
of buildings to be evacuated dispersed along this site. The evacuation paths
of buildings do not share many arcs (parallel evacuation per sector).
Figures a and a represent two
cases of buildings: the remaining buildings to be evacuated and the
buildings considered isolated. The latter are the buildings that can not be
evacuated (we assume they were evacuated earlier) after a certain time from
the beginning of the flood, because the area to which they belong is already
submerged
A network road is considered submerged (not applicable
for evacuation) if the water level is higher than or equal to 30 cm.
. The
area shown in blue on the network corresponds to HKW
Highest known
water. Provided by the Prevention Plan Flood Risk.
and not to the progress of
the level of water over time. We are interested in representing primarily the
isolated buildings (e.g., at t= 127 h) to show the dynamics of the hazard. Thus,
Figs. b and b illustrate the assignment of buildings to safety points with an
initial percentage of 50 %. This threshold (50 % of the total number of
evacuees) probably is not met due to network disconnection (areas submerged).
Figures a and c
show the three best paths (K= 3) computed between each origin and destination
pair. This number may suffer a reduction over time for the same reason evoked
previously (network disconnection). Finally, Figs. b and show the evacuation
scheduling per time slot (1.5 min).
The assignment of buildings to safe points is performed based on the
proximity of the latter, and the established priority list is based on the
distance of buildings from assembly points (from far to near). We note that
evacuation routes computed before or at the beginning of flooding change
depending on the evolution of the flood.
Figure c shows that evacuation paths of some buildings (bottom left) are much longer than
those of the same buildings at the beginning of flood (see Fig. a).
Moreover the capacity of shelters is not identical after 125 h (from the
beginning of the flood) because of the network disconnection as mentioned above
(see Figs. b and b for comparison). In other words, several buildings that are
closer to the shelter in the south must be evacuated to the north because there
is no available path.
Scenarios and validation of evacuation plan in case of incidents
The two curves in Fig. show the total evacuation
duration of the two study sites according to a potential reduction in network
capacity. The fall percentage of roads capacity aims to simulate the total
evacuation time in case of incidents. This is performed by reducing the
capacity of each road of evacuation network. The new capacity is equal to the
initial capacity minus the percentage of fall and multiplied by the initial
capacity. This simulation aims to determine the validation level of the
horizon time. For example, if after a little fall the evacuation time
computed exceeds the horizon time given, then the decision-makers should
apply some strategies: authorizing no-entry, increasing speed,
opening an additional safe area, etc. For reasons of graphical
representation, the total evacuation time of the Tours valley with a fall
equal to 99 % is set to 100 h, although it is more than 500 h in
reality. The analysis of these two curves shows that the total evacuation
time of the Tours valley remains reasonable with a fall of capacity less
than or equal to 80 %, while that of the Gien valley remains reasonable
with a fall less than or equal to 99 %.
Construction of the evacuation plan 125 h after the beginning of the floor:
evacuation scenarios in case of fall of network capacity.
Conclusions
The evacuation model STOM can be applied and adapted, if necessary, to any
other site requiring an evacuation, whether preventive or during the crisis.
To apply a pedestrian evacuation (stadiums, ships,
etc.), STOM requires a traffic model to compute a pedestrian travel
speed according to the number of pedestrians crossing the arc (corridor,
passage, etc.). In general, a given evacuation problem (preventive or during
the crisis) requires determining the evacuation network (fixed or progressive
depending on time), the issues to be evacuated and the safety points to which
evacuees will be directed and distributed .
The feasibility of an evacuation plan still faces two challenges related to
the behavior of evacuees: respect for evacuation routes and departure dates.
Simulation of several scenarios such as delay departure, noncompliance with calculated
evacuation paths and accidents allows us to verify to what extent
this plan remains robust in terms of total evacuation time. Such simulations
should update the evacuation model itself by establishing new constraints to
be respected.
Such simulations could also be used to inform people about this risk and
compliance with evacuation instructions. In addition, a higher number of
safety points will result in the diversity of k-best paths which in turn
enables us to minimize the total evacuation time and to guarantee a high
acceptance level of the evacuation plan by the concerned population. In this
case, the evacuation plan is similar to a parallel evacuation of several
urban areas (neighborhoods, islands, etc.).
As for the evacuation priorities, several tests were carried out according
to different established priority lists. Those were based on a combination
of several criteria such as proximity to the hazard of flooding, the age of
the population, etc. Total evacuation times provided remain very close to
the various priority lists. This would give the crisis management actors
more flexibility in prioritizing the evacuation order of the population and neighborhoods.
Of course, the validation of the evacuation plan computed also requires the
realization of real evacuation exercises by policymakers. From a scientific
point of view, these exercises should validate not only the provided results
but also the model input data. Among
others, the total evacuation time, a measure of the risk, should meet the deadlines of a preventive evacuation. If
this condition is not achieved, in order to minimize the total evacuation
time decision-makers can apply strategies to change direction of movement,
increase speed on certain network roads, open additional safe areas,
etc. Finally, the construction procedure of evacuation plan should be
regularly updated according to the evolving issues located in flood areas.
Lastly, mapping of evacuation plan carried out by STOM evacuation software
requires further development in order to make it more operational
. Research on this topic is ongoing.
Acknowledgements
We would like to thank LA REGION CENTRE (France) and
FEDER (Europe) for funding this research. Also our thanks for the editor
Thomas Glade and the two anonymous referees for assessing this paper. Edited by: T. Glade
Reviewed by: two anonymous referees
References
Alaeddine, H., Maïzia, M., Serrhini, K., and Néron, E.: A mezoscopic vehicles
pursuit model for managing traffic during a massive evacuation,
University of Tours, Tours, France, 2014a.
Alaeddine, H., Néron, E., Serrhini, K., and Maïzia, M.: A novel polynomial
algorithm for the lexicographic maximum dynamic flow problem with several
sources applied to evacuation planning, University of Tours, Tours, France, 2014b.
Alaeddine, H., Serrhini, K., Maïzia, M., and Néron, E.: Finding the K-best
paths in evacuation network, University of Tours, Tours, France, 2014c.
Alaeddine, H., Serrhini, K., Néron, E., and Maïzia, M.: Traffic assignment
algorithms for planning a mass vehicular evacuation, University of Tours, Tours, France, 2014d.
Ardekani, S., Ghandehari, M., and Nepal, S.: Macroscopic speed-flow models for
characterization of freeway and managed lane, The University of Texas,
Department of Civil Engineering, Arlington, USA, 2011.
Bhaduri, S.: Transport and regional development, Indian Council of Social
Science Research, Concept Publishing Company, India, 1992.
Bormanna, A., Kneidl, A., Koster, G., Ruzika, S., and Thiemannb, M.:
Bidirectional coupling of macroscopic and microscopic pedestrian evacuation
models, Safety Science, 50, 1695–1703, 2012.
Bretschneider, S. and Kimms, A.: Pattern-based evacuation planning for urban
areas, Eur. J. Operat. Res., 216, 57–69, 2012.
Bretschneidera, S. and Kimms, A.: A basic mathematical model for evacuation
problems in urban areas, Transport. Res. Pt. A, 45, 523–539, 2011.
Caloz, R. and Collet, P. D.: Analyse spatiale de l'information géographique,
Presses polytechniques et universitaires romandes, Presses Polytechniques et
Universitaires Romandes, collection Ingénierie de l'Environnement, Lausanne, France, 2011.
Chalmet, L., Francis, R., and Sanders, P.: Network Models for Building
Evacuation, Management Science, 28, 86–105, 1982.
Chapelon, L. and Leclerc, R.: L'accessibilité ferroviaire des villes
françaises en 2020, Paris: La documentation française, Coll. Dynamique
des territoires, Paris, France, 2007.
Dixit, V.: Hurricane evacuation : Origin, route and destination, M. Tech.,
Indian Institute of Technology, Delhi, 2005.Fuchs, S., Heiss, K., and Hübl, J.: Towards an empirical vulnerability
function for use in debris flow risk assessment, Nat. Hazards Earth Syst. Sci.,
7, 495–506, 10.5194/nhess-7-495-2007, 2007.
Geurs, K. and Wee, B.: Accessibility evaluation of land-use and transport
strategies: review and research directions, J. Transport Geogr., 12, 127–140, 2004.
Hamacher, H. and Tjandra, S.: Mathematical Modelling of Evacuation Problems: A
State of Art, Beritche des Fraunhofer Istitut Techno- und
Wirtschaftsmathematik Nr. 24, internal report, Germany, 2001.
Hamacher, H. and Tufekci, S.: On the use of lexicographic min cost flows in
evacuation modeling, Department of Industrial and System Engineering,
University of Florida, Gainesville, Florida, 1987.
Kaisar, E., Hess, L., and Palomo, A.: An Emergency Evacuation Planning Model
for Special Needs Populations Using Public Transit Systems, J. Publ.
Transport., 15, 45–70, 2012.
Lammel, G., Grether, D., and Nagel, K.: The representation and implementation of
time-dependent inundation in large-scale microscopic evacuation simulations,
Transport. Res. Pt. C, 18, 84–98, 2010.
Lim, G., Zangeneh, S., Baharnemati, M., and Assavapokee, T.: A capacitated
network flow optimization approach for short notice evacuation planning,
Eur. J. Operat. Res., 223, 234–245, 2012.
Lindell, M. and Prater, C.: Behavioral Assumptions in Evacuation Time Estimate
Analysis, International Conference on Urban Disaster Reduction, Taiwan, China, Taipei, 2007.
Mathis, P., Chapelon, L., Serrhini, K., Baptiste, H., Decoupigny, F., Khaddour,
O., Larribe, S., L'Hostis, A., Decoupigny, C., and Appert, M.: Graphs and
Networks: Multilevel Modeling, Geographical Information System Series, ISTE, France, 2007.
Matisziw, T. and Murray, A.: Modeling s–t Path Availability to Support Disaster
Vulnerability Assessment of Network Infrastructure, Comput. Operat. Res., 36, 16–26, 2009.Meyer, V., Kuhlicke, C., Luther, J., Fuchs, S., Priest, S., Dorner, W., Serrhini,
K., Pardoe, J., McCarthy, S., Seidel, J., Palka, G., Unnerstall, H., Viavattene,
C., and Scheuer, S.: Recommendations for the user-specific enhancement of flood
maps, Nat. Hazards Earth Syst. Sci., 12, 1701–1716, 10.5194/nhess-12-1701-2012, 2012.
Naghawi, H.: Transit-based emergency evacuation modeling with microscopic
simulation, PhD BS, The University of Jordan, MS, The University of Jordan, Jordan, 2010.
Naser, M. and Birst, S.: Mesoscopic Evacuation Modelin for Small-to
Medium-Sized Metropolitan Areas, Advanced Traffic Analysis Center, Upper
Great Plains Transportation Institute, North Dakota State University, Fargo, 2011.
Patouillard, S., Auger, N., and Maurin, J.: Les renforcements de digues en
Loire moyenne, mise en perspective des techniques et expérimentation, Digues
maritimes et fluviales de protection contre les submersions, Deuxième
colloque national, Aix en Provence, 2013.Plattner, Th.: Modelling public risk evaluation of natural hazards: a conceptual
approach, Nat. Hazards Earth Syst. Sci., 5, 357–366, 10.5194/nhess-5-357-2005, 2005.
Powell, W., Jaillet, P., and Odoni, A.: Stochastic and dynamic networks and
routing, in: Handbooks in Operations Research and
Management Science – Network Routings, 8, chapter 3, edited by: Ball, M. O.,
Elsevier, North-Holland, 1995.
Saadatsresht, M., Mansourian, A., and Talaei, M.: Evacuation planning using
multiobjective evolutionary optimization approach, Eur. J. Operat. Res., 198, 305–314, 2009.
Sharma, H. and Binda, P.: Modeling in Resource Management and Environment
Through Geomatics, Concept, 2007, xiv, p. 304, figs, tables, Concept Publishing Co, 2007.Southworth, F.: Regional Evacuation Modeling: A State-of-the-Art Review, Oak
Ridge National Laboratory, USA, 1991.
Stage: Plan d'évacuation massive du val de Tours en cas de crue majeure de la
Loire, Stage collectif dirigé par M. Serrhini Kamal; Réalisation:
Buttin, C., Chevalier, S., Gerard, P., Hautin, F., Mereau, Q., EPU DA4,
Université françois-Rabelais de Tours, Ecole polytechnique, Département
Aménagement, France, 2011.
Stage: Plan d'évacuation de la population du val de Gien en cas
d'inondation majeure de la Loire. Stage collectif dirigé par M. Kamal
Serrhini; Réalisation: Bouyneau, M., Ludwig, J., Odier, H., Ramond, L., EPU DA4,
Université françois-Rabelais de Tours, Ecole polytechnique, Département
Aménagement, France, 2013.Stepanov, A. and Smith, J.: Multi-objective evacuation routing in
transportation networks, Eur. J. Operat. Res., 198, 435–446, 10.1016/j.ejor.2008.08.025, 2009.
Trani, A.: Introduction to Transportation Engineering, Traffic Flow Models,
Virginia Polytechnic Institute and State University Blacksburg, Virginia, 2009.Versini, P.-A., Gaume, E., and Andrieu, H.: Assessment of the susceptibility
of roads to flooding based on geographical information – test in a flash flood
prone area (the Gard region, France), Nat. Hazards Earth Syst. Sci., 10, 793–803,
10.5194/nhess-10-793-2010, 2010.
Yusoff, M., Ariffin, J., and Mohamed, A.: Optimization approaches for
macroscopic emergency evacuation planning: A survey, Conference Proceeding:
Information Technology, 2008, ITSim 2008, International Symposium, Kuala Lumpur, Malaysia, 2008.