Multi-Constraints and Single Objective Based Optimum Routes

Planning for Assisted Evacuation

A Geographic Information System Based Solution and Simulation

Md. Imran Hossain

Institute for Applied Computer Science, University of the Bundeswehr Munich,

Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany

1 RESEARCH PROBLEM

With the advancement of modern technologies it is

now more or less possible to predict almost all kind

of natural and manmade disasters in terms of their

time of occurrence, intensity and geographic area of

occurrence. However, recent large natural and

anthropogenic disasters have clearly shown various

shortcomings and failures in existing technologies

for efficient emergency response especially in the

domain of evacuation (Konečný, Zlatanova and

Bandrova, 2010). Depending on the magnitude,

especially when a massive destruction is expected

for a forthcoming disaster, evacuations sometime

become so obvious to minimize the casualties. When

an evacuation becomes obvious, the responsible

authorities usually announce the event through

different media and possibly with escape routes and

directions. Thus, in the event of evacuation, people

who have their own vehicle or have good access to

the available transport modes can evacuate by

themselves. On the other hand, there are always

special groups of people who are subject to severe

mobility restrictions in terms of lack of personal

transportation, limited financial resources,

unfamiliarity with the area and its road network,

physical and mental disabilities, language barrier

etc. (Zimmerman, Brodesky and Karp, 2007). These

groups of people are therefore at greatest risk of

casualties. The responsible authorities (public safety

agencies, police department etc.) for evacuation

provide special evacuation units (vehicles) to collect

and shift those special groups to a safe place which

is called herein as the assisted evacuation.

The route plan for each evacuation unit has

significant effects on the efficiency of such assisted

evacuation. The contemporary manual route

planning with unknown spatial evacuee distribution

hinders the performance of assisted evacuation in

many folds. First of all, the evacuation units have to

go through all the streets of a given area of

evacuation which usually lead to a substantial waste

of time and therefore become very inefficient

especially when the evacuation is bounded by huge

time pressure. Secondly, the manual process is

unable to give estimation for the required/optimum

number of evacuation units to cover all the evacuees

who need assistance. And finally it cannot provide

estimation for evacuees to be covered under certain

time and resource constraints. Therefore, with a

known spatial distribution of the evacuees,

automatic dynamic optimized route plans for the

evacuation units would certainly preside over the

any manual interventions in this case.

An incident manager, under any circumstances,

would be interested to cover all the evacuees of an

area which is under disaster thread. But this might

not happen in reality as the performance of assisted

evacuation of an area depends on the route plan of

each evacuation unit together with at least three

major factors or variables: 1) Total available time

(T): the time segment between the announcement of

an evacuation and the actual disaster event. 2) Total

available evacuation units (U) and 3) Number of

evacuees (E): the number of the evacuees who need

assistance. Therefore, a decision support system that

can deliver the optimized dynamic route plans for all

the evacuation units by fixing any two

variables/factors and keeping the third as a goal

would enable the incident manager for estimating

the required resources and to take the right decision

in a given evacuation scenario. Along with the

optimized route plans the decision support system

should answer the following questions as well.

1) How many evacuees (E) could be evacuated

under certain time (T) and resource (U) constraints?

2) How many evacuation units (U) would be

required to evacuate a certain number of evacuees

(E) under a certain time (T) constraint?

3) How long (T) would it take to evacuate certain

amount of evacuees (E) with certain number of

evacuation units (U)?

This research project is therefore intended to

develop such kind of decision support system (DSS).

The DSS would be further tested, verified and

validated by a suitable simulation technology.

Hossain M..

Multi-Constraints and Single Objective Based Optimum Routes Planning for Assisted Evacuation - A Geographic Information System Based Solution

and Simulation.

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2 OUTLINE OF OBJECTIVES

The overall aim of this research is to design, build

and validate a decision support system that can

produce optimal dynamic route plans for multiple

evacuation units involved in the assisted evacuation

with multiple constraints and a single objective. To

achieve this aim the following research objectives

are formulated.

 Objective 1: To estimate the spatio-temporal

distribution of the evacuees who need

assistance.

 Objective 2: To divide a geographic area

defined by a set of geographic features

(buildings in this case) into multiple regions in

a way that each region is consists of almost

equal number of evacuees.

 Objective 3: To estimate time required for

evacuating different type of evacuees

considering the traffic situation and

surrounding environment.

 Objective 4: To develop dynamic routing

algorithm for optimum path generation that

further allows routing during run time.

 Objective 5: To design and build a decision

support system with the output of all previous

four objectives.

 Objective 6: To simulate some disaster cases

with appropriate simulation technology/ies to

test, verify and validate the decision support

system.

3 STATE OF THE ART

A spatial decision support system is an interactive,

computer-based system designed to support a user or

a group of users in achieving greater effectiveness in

decision-making while solving a semi-structured

(not completely programmable) spatial decision

problem (Malczewski 1999). There are quite a large

number of DSSs exist in the domain of Geographic

Information System (GIS). However, the author did

not find any decision support system that provides

exact solutions to the problem stated in the problem

statement part (section 1). However, quite a number

of literatures related to the objectives, especially

objective no. 1 to 4 are already available. Therefore,

the state of the art, herewith, is formulated according

to the different objectives of this research and is

given with the following subsections.

3.1 Spatio-Temporal Distribution of

Evacuees Who Need Assistance

Ahola et al. (2007) have developed a spatio-

temporal population model to support risk

assessment and damage analysis for Finnish Fire and

Rescue Services and the Finnish Defence Forces.

Their model uses a basic population and workplace

dataset maintained by the Helsinki Metropolitan

Area Council. With the model the authors prepare

population density map for day and night time for a

specific area.

Ural, Hussain and Shan (2011) have tried to map

the spatial distribution of population in a different

way mainly using a combination of aerial imagery

and GIS data. In their work they have extracted the

buildings from aerial imagery and then classified

those through City Zoning maps. Additional

ancillary geo-data has been used to filter out the

utility buildings. Finally, census block data has been

disaggregated and linked to the individual building.

Freire (2010) has used a dasymetric mapping

approach to refine population distribution in

Portugal. The author has calculated the maximum

day time and night time population for each 25 m

grid cell of a raster map.

It is clear that different approaches and

techniques are already available mainly for mapping

the spatial distribution of population. The temporal

aspects are considered in few cases but with a very

coarse temporal resolution. However, in this

research spatio-temporal distribution of the evacuees

who need assistance, need to be known. This could

be achieved through either enrichment and

modification of the available methodologies or

development of completely new methodology.

3.2 Regionalization/Zoning Systems

Automated zone design (AZD) or regionalisation is

a technique for which Shortt (Thrift and Kitchin,

2009) has given the overview of its concept,

terminology and methods. AZD is an umbrella term

for quite a number of approaches to create zones

from a set of basic building blocks following given

criteria. Among the automated zone design

algorithms automated zone design procedure (AZP)

is the most popular and widely used one. It was

introduced by Openshaw and Baxter (1977). The

AZP has been enhanced by Openshaw and Rao

(1994), Alvanides (2000) and Alvanides et al (2002).

Cockings et. al. (2011) used automated zone design

techniques to dynamically maintain existing zoning

systems. There are also a lot of other application of

AZP algorithm such as climate zoning, location

optimization and many more. The AZP algorithm

SIMULTECH2014-DoctoralConsortium

iteratively combinines and recombines sets of blocks

in order to create output zones which are optimised

based on a set of pre-specified design criteria

(Openshaw and Rao, 1994).

AZP is not applicable to the task defined in

objective 2 as firstly, AZP is applicable only to

continuous and connected feature sets whereas in

our case continuous and discrete feature sets must be

treated. Secondly, in AZP the geometric structure

and thematic attributes of the input building blocks

are destroyed to form a new zone out of them. This

should not be allowed in the present research work.

Also it is required in our approach that the bounding

polygon of each region must not overlap with any

other region to offer a distinct separate area for each

evacuation unit.

3.3 Time Estimation for Evacuation

The evacuation time requirement is expected to vary

according to the type of evacuees who need

assistance and their surrounding environment. For

example the required time for evacuating a

physically disabled evacuee who lives at the 10

floor would be much higher than an evacuee who

just doesn’t have transportation access and resides at

a single storied building. A model has to be

developed in this research that can provide

information on required time for a specific type of

evacuee considering the surrounding environment.

Unfortunately, no literature has been found that

present such a model. Therefore, this type of model

has to be built from the scratch.

3.3 Dynamic and Optimum Routing

Algorithms

Hundreds of routing algorithms are available

nowadays for calculating paths between two or more

points of interest. Laporte (1992) provides a general

wide overview and classification of the vehicle

routing algorithms. Most of them till date are static

in nature means the underlying data are known in

advance. On the other hand, routing algorithms are

designed in a way that it can provide routed between

the points of interest at a minimum cost, generally

the total travel time or the total travelled distance.

In the premise of Dynamic Vehicle Routing

Problems (DVRPs) new orders dynamically arrive

when the vehicles have already started executing

their tours, which consequently have to be re-

planned at run time in order to include these new

orders (Montemanni et al., 2003).

The DSS to be built in this research is expected

to provide routes for each evacuation unit involved

in the assisted evacuation in a way that instead of

shortest or fastest path, it can produce an optimized

path to serve all the evacuee of the defined territory

of an evacuation unit in an optimized way. In

addition, the routing algorithm should support the

DVRP as spatial distribution of the evacuees could

change over time.

4 STAGE OF THE RESEARCH

The authors at this stage successfully achieved only

objective number 2. An algorithm for segmenting a

geographic area defined by a set of geographic

feature set into equitable regions has been

developed, implemented and published in the 17

AGILE Conference on Geographic Information

Science (Hossain and Reinhardt, 2014). A brief

summary of the work is given below.

4.1 the Task

A geographic area G defined by a feature set

consisting of n features with a numeric attribute A

has to be completely divided into N (N  | 2 ≤ N ≤

n) number of subsets/regions based on 3 criteria.

Input:

- Geographic area G = {f

| f

 F (set of features), f

has a numeric attribute A}

- N (N  | 2 ≤ N ≤ n) = number of required subsets

of G, N has to be defined by the user.

Output:

- N number of subsets R

(subsets/regions)

Criteria:

1) Region cannot be formed with splitted feature

means a feature of a region is not allowed to be in a

form like f

/m | m .

2) The sum of |A| (|A| is the value of attribute A) of

any region defined herein with O(R

)

= T ± d

3) The bounding polygon of any subset BNDline(R

)

do not overlap with the bounding polygon of any

other.

The value of T is calculated by equation 1and the

value of d is an element of set D. The value of d can

ranges from 0 to the maximum value of |A| of a

given feature set G (equation 2).



∑



|







(1)



D = {q



ℚ|0 ≤ q < MAX (|A|(G)) }

(2)

Multi-ConstraintsandSingleObjectiveBasedOptimumRoutesPlanningforAssistedEvacuation-AGeographic

InformationSystemBasedSolutionandSimulation

In general, the algorithm prioritizes forming

regions along the bounding line BNDline(G) of the

input feature set G. This approach prevents features

being unclassified and also prevent big differences

among the regions. A region R

is formed by

grouping features around the bounding line until

O(R

)

= T ± d. Once no region formation is possible

along the BNDline(G), another bounding line is

created for the features which are not classified into

regions and regions are again formed along the new

bounding line. This process continues until N-1

regions are formed. The N

region is formed with

remaining unclassified features after formation of N-

region and consequently it´s possible that the sum

of |A| may not be within T ± d in this case.

4.2 Steps of the Algorithm

The algorithm is described in more detail through

the following steps.

Step 1: Objective Function Calculation

Objective function returns the value of T on which

each region is formed. T is calculated through

equation 1.

Step 2: Selection of the Starting Feature for

the First Region

The starting feature for the first region is selected by

two criteria. Firstly, it has to be along the

BNDline(G) and secondly it has to be located in an

appropriate corner of G. Therefore the starting

feature is selected by firstly making an array of

features that touches the BNDline(G). Secondly a

feature is picked up from that array and distances are

calculated from that feature to all other features of

that array. The maximum distance is then stored

with each picked up feature. This process is carried

out for all features in the array. Finally, the feature

that has the maximum distance value compared to

A region (feature set with gray color) is formed by aggregating

features from G with starting feature based on closest distance

A feature (dark gray) from G is selected as a start feature for

subsequent region building if it is closest to the bounding

line of (thick dotted line) previously formed region and

touches the bounding line of the input feature set G

All the features along the bounding line (outer black line) of G

are classified into regions. A second bounding line (inner black

line) is formed for the unclassified features.

The features with gray color touch the bounding line (black

line). For each gray feature distance to other gray features are

measured and the maximum is stored. The maximum of gray

feature 1 (thicker line) is higher than the maximum of gray

feature 2 (thicker line). Thus the gray feature whose

maximum is the highest is selected as starting feature

Figure 1: Visual illustration of different steps of the algorithm.

SIMULTECH2014-DoctoralConsortium

other features in the array is selected as a starting

feature for the first region formation (fig. 1A).

Step 3: Formation of the First Region

At the beginning the first region R

is formed only

with the starting feature. Then the region is grown

by grouping features from G on the basis of

minimum distance, which means a feature from G is

allowed to be grouped with the starting feature if the

distance between them is a minimum compared to

the distance of other features in G (fig. 1B). This

grouping or region building is continued until O(R

)

= T ± d criteria is fulfilled. Since a feature in G is

not allowed to divide according to the underlying

data model, it is only possible to completely include

or exclude a feature to a region. Which means the

feature cannot be sliced. So, O(R

) cannot always be

exactly equal to T. The maximum possible deviation

of O(R

) with T for R

to R

N-1

will be thus the

maximum value of |A| of any given G.

Once a region R

is formed, a static variable

StatN is updated with the number of region formed

and the feature set on which the process will be

continued is obtained by G - R

. The process

terminates and goes out of scope when N-1 = StatN.

For example, if 3 regions are expected and 2 regions

have already been completed then remaining

features of G automatically form a region and the

process goes out of scope.

Step 4: Start Feature Selection for the

Subsequent Regions

As stated earlier, the algorithm prioritizes forming

regions along the bounding line BNDline(G) of the

input feature set G. Therefore, a start feature for any

subsequent region R

i+1

should be located next to the

former region R

and also should touch the

BNDline(G) (fig. 1C). These are two simple criteria

for selecting a start feature for any subsequent

region building.

Step 5: Repetition

Step 3 to 4 are repeated until no start feature is

returned by step 4 and the required number of

regions is still not achieved. A null feature return by

step 4 means all the features along the bounding line

of G are classified into regions. If this is the case, a

new bounding line is created for the set of non-

classified features (fig. 1D). The BNDline(G) which

is created in step2 is replaced by the new bounding

line and the process starts continuing from step 2.

4.3 Implementation and Results

The algorithm presented in section 4.2 has been

implemented using c# programming language and

ArcObjects library of ESRI. Figure 2 and 3 show the

result of 2 examples of an application of the

implemented algorithm. Each feature (polygon) in

both figures represents residential buildings and has

an attribute called population (no. of residents). The

maximum value of d of the input feature set was 21.

Figure 2: Input feature set divided into 3 equitable regions.

In figure 2, the expected number of equitable

regions was 3 based on the population attribute

which means the feature set has to be divided into 3

non-overlapping regions so that the total population

for each region remains approximately equal.

Figure 3: Input feature set divided into 7 equitable regions.

In figure 3 the expected region number was 7.

Both figures show a distinct division of the feature

set into regions. None of the region in both figure

overlap with others. The important point to be noted

here is that region no.0 in both figures differs

significantly from other regions in terms of total

population and the difference goes beyond the

MAX(d) in figure 7. The differences among other

regions are minimal and within MAX(d).

Multi-ConstraintsandSingleObjectiveBasedOptimumRoutesPlanningforAssistedEvacuation-AGeographic

InformationSystemBasedSolutionandSimulation

Region 0 is in fact the last region formed with

the remaining feature set once N-1 regions are

formed. If the other regions formed with a positive

value of d (section 3, step 3) then the effect goes on

to the last N

region (region 0) which is forced to be

formed with a total value deduced by the cumulative

positive d of the former regions. Thus only the N

region’s O(R

) may not be equal to T ± d. The

maximum difference between the last region’s O(R

)

with other regions O(R

) is thus expected to be

higher with the increased no. of regions. However,

this problem can be solved with a constraint that two

consecutive regions should form with +d and –d

simultaneously which restricts region formation with

always +d or –d.

5 METHODOLOGY

The methodology of this research is presented by

figure 4. The DSS which is the core of the present

research is foreseen to be composed of four different

components. The first component is a model which

would provide the spatio-temporal distribution of the

evacuees who need evacuation assistance. The

second component is an algorithm by which a given

geographic area would be divided into multiple

equal regions in terms of the number of evacuees

and their corresponding required evacuation time to

assign each evacuation unit to a region.

Development of the second component is already

done for which a brief summery is given in the

section 4. The third component is a model to

estimate the required time for evacuating a specific

type of evacuee considering his/her surrounding

environment. And the fourth component is again an

algorithm which would provide dynamic route plan

for each evacuation unit with optimized path.

The outcome/usage of the DSS is shown by the

thick blue downward arrow in figure 4. Three

possible outcomes are shown here. An incident

manager can go for any one among the possible

three. All the outcomes have a common feature

which is the optimized dynamic route plans for the

evacuation units. Along with this, an incident

manager can fix two constraints at a time and can

get a decision regarding an objective. For example if

Evacuation

scenario

Simulation

Results

ResultAnalysis

Figure 4: A gross methodology of the research.

SIMULTECH2014-DoctoralConsortium

no. of evacuation unit and the no. of evacuees are

fixed then the incident manager will get as outputs

the required time and the route plans.

Once the DSS is build, a suitable simulation

framework would be created for testing, validation

and verification of the DSS. Decision regarding the

technological aspects behind the simulation is not

yet decided. It might be an agent based simulation,

statistical simulation or any other depending on the

suitability and purpose of the DSS.

Some evacuation scenarios would then be

simulated by combining the DSS with the simulation

framework. The result of the simulation would be

analysed. Any drawbacks or shortcoming that may

become identified with the analysis will then be

adjusted in the DSS (the components of the DSS).

6 EXPECTED OUTCOME

The main outcome of this research is a multiple

constraints based decision support system for a

single evacuation objective supported by optimal

dynamic route plans for multiple evacuation units

involved in the assisted evacuation. With the

decision support system an incident manager, among

the three variables: time, resources and evacuees,

could make estimation for one variable while fixing

the other two, supported by optimized dynamic

routing plans for the evacuation units. Moreover,

this research would create some further by-products

which are listed below.

1) Methodology for estimating the spatio-

temporal distribution of the evacuees who need

evacuation assistance.

2) An algorithm for segmenting a geographic

area into equitable regions

3) Methodology for estimating the time

requirement for evacuating different types of

evacuees considering the traffic situation and

surrounding environment.

4) An advanced algorithm for optimized routing.

The routing algorithm is also expected to be

dynamic which means it can provide alternative

updated route plans during run time.

5): A simulation framework for the testing,

varifiying and validation of the DSS with some

disaster cases.

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Multi-ConstraintsandSingleObjectiveBasedOptimumRoutesPlanningforAssistedEvacuation-AGeographic

InformationSystemBasedSolutionandSimulation