Document [original]

Received 17 June 2016

Accepted 18 July 2016

Route Guidance Map for Emergency Evacuation

Lakshaya*, Amit Agarwalb, Nomesh B. Boliaa

aDepartment of Mechanical Engineering, Indian Institute of Technology Delhi, India,

Hauz Khas, New Delhi, India-110016

E-mail: lakshaytaneja01@gmail.com

bTransport Systems Planning and Transport Telematics, Technische Universität Berlin,

Germany 10587

Abstract

An efficient process of emergency evacuation must be guided. In the event of an evacuation instruction, a

significant amount of time is spent by evacuees looking for a place of relative safety or an exit. Due to the ensuing

stress and confusion evacuees try to follow others, consequently, all the exits are not used effectively. Therefore, it

is important to develop a route guidance map for the emergency. The focus of the map is to help both, the evacuees

and the authorities to perform evacuation efficiently. This paper presents a route guidance map for pedestrians that

aims an efficient evacuation in case of an emergency. An agent-based simulation framework is used for the

simulation of various scenarios to prepare the guiding map. A real world case study of Sarojini Nagar, Delhi is

presented to test the presented methodology. Eventually, several strategic recommendations are provided for

improving safety of existing infrastructure.

Keywords: Disaster preparedness, public safety, simulation, emergency, evacuation plan, strategic planning.

1. Introduction

In recent years, public safety and disaster preparedness

have become a prime focus for national authorities, urban

planners and civic agencies due to losses of human lives.

In year 2014, at least 32 people were killed and 26

injured in a stampede shortly after the celebration of

festival Dussehra in Patna, India (Express News Service,

2014). There are many similar examples across the world

(see Table 1 for similar examples), where due to lack of

efficient evacuation planning, people have suffered. The

recurring stampedes occur mainly at places of mass

gatherings for example religious places, railway stations,

sports/political/social events etc. There are many causers

* Corresponding author

and triggers for the crowd disaster including structural

design, fire, rumors, and sudden mass evacuation

(NDMA, 2014).

Evacuation is a process in which endangered people

are moved from a dangerous place to a safe place in order

to reduce the vulnerability during these dangerous

circumstances. In order to mitigate impacts of disasters,

proper evacuation planning is required. In many of the

past events, lack of evacuation planning has resulted in

loss of human lives, particularly in India (see Table 1).

Improper selection of exit or failure to avoid the obstacles

may lead to either serious injury or death. Therefore, a

proper route guidance map in terms of an “Evacuation

Plan” is required that can help evacuees to find the

suitable exits and the route to be followed to evacuate the

Journal of Risk Analysis and Crisis Response, Vol. 6, No. 3 (October 2016), 135-144

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Lakshay et al. / Route Guidance Map for Emergency Evacuation

endangered area in minimum time and with minimum

loss of life. This guiding map can also serve as a

reference for security staff to guide the evacuees on the

route to take for evacuation. This evacuation plan may

also suggest the structural improvements, which can be

helpful for further reduction of the evacuation time.

Limited research on developing evacuation plan has been

done and reported in literature. The ability to evacuate

people depends mainly on two factors viz.: structural

design and behavior. Inefficient design and panic

behavior may lead to overcrowding, which in turn may

lead to crushing, suffocation and trampling. Besides

planning the infrastructure efficiently, it is also essential

to understand the movement and flow behavior, which

may help planners and civic agencies to reduce the

severity. Evacuation, where there may be a transition

from normal behavior to irrational panic behavior, is

governed by factor of “nervousness” which leads to slow

down the crowd and tendency to follow others (Helbing

et al., 2002).

A great share of literature focus is on simulating a

single room evacuation pattern (Casadesús et al., 2009;

Takahashi et al., 1989; Taylor, 1996) where in true sense

little evacuation planning take place. On the other hand,

to evacuate a larger area, egress route have to be defined

first, which requires optimization techniques. In a similar

research direction, this study aims to investigate sudden

mass evacuation from a crowded place. This paper

presents a real-world case study for evacuation

preparedness due to disastrous events in large-scale

pedestrian areas. A majority of crowd disasters have

occurred at shopping malls, music concerts, and stadium

in developed countries (NDMA, 2014). With increasing

population, developing countries are also susceptible to

crowd disaster at such venues (NDMA, 2014). Therefore,

main focus of this study is to evacuate persons from

congested areas such as market places or mass gathering

venues. The objective is to make recommendations to

improve the evacuation time of all people in the

identified area. The key outcome is an “evacuation plan”

for designated sites. The event of potential bombing is

used as an example of the disaster where evacuation is

required. The methodology presented in this study is

applied to a market place, Sarojini Nagar, New Delhi,

however, it can be applied to any scenario wherever

evacuation is required. Also, in order to check the

applicability and robustness of the approach, the same

methodology is applied to two other areas namely Lajpat

Nagar and Laxmi Nagar, New Delhi.

Table 2 shows several models that have been used in

the past to reduce the response time for an evacuation. In

a study by Flötteröd & Lämmel (2010), the authors

suggested to adopt dynamic traffic assignment model to

develop an evacuation plan for open spaces. In general,

Table 1. Past mishappenings due to improper evacuation modeling.

Year

Place

Reason

Casualties

1903

Iroquois Theatre fire, Chicago

(U.S)

No exit signs; No emergency lighting; Exit routes were

confusing (Disaster, 2015)

602

1913

Italian Hall disaster, Michigan

(U.S)

Escape from a falsely shouted of fire at a party

(HallDisaster, 2016)

1995

Dabwali, Haryana (India)

Synthetic tent caught fire, blocking main entrance (NDMA,

2014)

446

1997

Uphaar Cinema, Delhi (India)

Smoky cinema hall (NDMA, 2014)

2000

Night club Lisbon (Portugal)

Head for main exit and ignore alternative exit (Helbing et

al., 2002)

2006

Jamrat Bridge (Saudi Arabia)

Overcrowding and poor crowd management (Still, 2016)

363

2008

Chamunda devi temple, Jodhpur,

Rajasthan (India)

Stampede due to false rumors of bomb (NDMA, 2014)

249

2010

Love Parade (Duisburg)

Trying to escape the overcrowded tunnel (Still, 2016)

2011

AMRI hospital, Calcutta (India)

Basement fire, suffocation causing deaths (NDMA, 2014)

2014

Patna Stampede (India)

Mass exit from a single gate and rumors also that

live electric had fallen on ground.

(Express News Service,

2014)

2015

Mina Stampede

Blockage of route to Jamrat Bridge (Still, 2016)

2110

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dynamic traffic assignment relies on microscopic models.

Zheng et al. (2009) compares several modeling

approaches at different scopes, for e.g. a) cellular

automata models based on lattice gas or social force

models b) agent-based models based on cellular automata

or social force models etc. Most of these models are

detailed models which are resource hungry and need

higher computational time. However, the aim of the

present study is to develop and test a route guidance map

using an approach that is computationally efficient for

large-scale scenarios. Therefore, the present study uses an

evacuation planning approach in a multi-agent simulation

based framework (Lämmel et al., 2010). The multi-agent

systems are preferred for crowd simulation modeling

(Almeida et al., 2013).This approach has also been

applied to a real-world evacuation scenario of Patna city,

India (Agarwal and Lämmel, 2016) under mixed traffic

conditions. This simulation framework is suitable for

large scale scenarios due to its queuing model (see

Agarwal et al. (2015); Balmer et al., (2009) and also

section 2.1). Every person in the area under consideration

may not be familiar with the prevailing traffic conditions

and alternative exit routes during evacuation situation,

therefore, this study proposes an evacuation plan and

subsequently, investigates the response time when this

evacuation plan is used under different situations.

The rest of the paper is organized as follows. First, the

detail of simulation framework for the present study is

explained in Section 2. Section 3 exhibits the

methodology and the case study of Sarojini Nagar market

is illustrated in Section 4. Section 5 shows the impact of

“Evacuation Plan” and its usefulness. Finally, the last

section concludes the overall work and provides some

outlook for future work.

2. Evacuation Modelling

Three different modelling approaches can be applied to

an evacuation process (Schadschneider et al., 2009): a)

risk assessment, b) optimization and c) simulation.

Simulation of pedestrians is generally used for two

purposes: to gain insight on a particular situation and to

prove/disprove a hypothesis (Still, 2007). The output of a

simulation mainly includes: distribution of evacuation

time, evacuation curves (number of people evacuated

with respect to time), sequence of evacuation (snapshot

at a specific time), and identification of congestion

(Schadschneider et al., 2009). In this article, multi-agent

simulation framework (MATSim) is used to identify the

evacuation time, congested links and sequence of

evacuation. In this, all evacuees are modeled as

individual agents. A Geographical Information System

(GIS) based Risk Assessments Information, Planning

System toolkit (GRIPS) is used along with MATSim

(Taubenböck et al., 2009).

MATSim has an evolutionary algorithm which

consists of mainly three steps as shown in Figure 1

Table 2. Method used in literature to reduce time.

Past study Model Description

Taylor (1996) Macroscopic

Find minimum time to evacuate building and optimal plan in terms of

exit usage.

Casadesús Pursals &

Garriga Garzón (2009)

Macroscopic Find the distribution of exit usage for minimum evacuation time.

Takahashi, Tanaka, &

Satoshi (1989)

Macroscopic Optimal exit from a room is chosen for evacuating.

Han, Yuan, Chin, &

Hwang (2006)

Macroscopic Routing for reducing total evacuation time.

Klüpfel (2003) Macroscopic Shows connection between choice of exit and individual egress time.

Stepanov & Smith

(2008) Microscopic Potential egress route described by Kth shortest path using distance,

travel time and level of congestion as objective function.

Abdelghany,

Abdelghany, &

Mahmassani (2014)

Microscopic Show that evacuation time reduces significantly by optimizing the

temporal distribution of evacuation and exit gate selection.

Kneidl, Thiemann,

Hartmann, & Borrmann

(2011)

Microscopic Find the probability of choosing a route to reduce the evacuation time.

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(Balmer et al., 2009; Horni, Nagel, & Axhausen, 2015).

In this iterative cycle, an agent learns and adapts to the

system. The minimal inputs are network and daily plans

of the individual agents.

• Execution (mobsim) - In this step, all the plans are

simultaneously executed using predefined mobility

simulation (mobsim) on the network. The network

loading algorithm is a queuing model (Cetin, Burri,

& Nagel, 2003; Gawron, 1998).1 The queue model

tracks every agent only at entry and exit and never in

between which makes it computationally efficient.

Hence, a large-scale scenario can be simulated in

reasonable computational time (Agarwal, Lämmel, &

Nagel, 2016a).

• Scoring - Various plans of an individual are

compared using a utility function. The utility

function consists of utility of performing an activity,

(dis)utility of traveling etc. All executed plans are

evaluated using the default scoring function

(Charypar & Nagel, 2005).

• Re-planning - For some of the agents, a new plan is

generated by modifying an existing plan depending

on the so-called innovative strategies (choice

modules). Several choice dimensions are available

for e.g. reroute, time mutation, mode choice etc. The

new plan is then executed in the next iteration. The

innovation is used until a fixed number of iterations

(for e.g. for 80% of the iterations). Therefore, rest of

the agents until innovation and all the agents after

innovation select a plan from their choice set

according to a probability distribution which

converges to the multi-nomial logit model.

1 Refer to (Agarwal et al., 2016b, 2015)for details about the queue

model and its extensions in the MATSim. For simplicity, the present

study uses first-in-first-out (FIFO) approach of the queue model.

3. Survey methodology

Typical crowd density at various sections of the road is

estimated as illustrated further. A travel count survey data

is conducted as follows to identify the initial person

density on each link.

1) On every link of the road network, three surveyors

are placed to count a) the number of persons present

initially at time t, b) number of persons entering and

c) number of persons leaving the link in 5 min time

bin.

2) Thus, number of persons on a road at any time t is

given by Equation (1)

() () () ()Nt t It Ot

= +−

(1)

where,

()Nt

is number of persons on a road at time t ;

I(t) is number of persons entering the link in time bin,

O(t) is number of person leaving in that time bin and

λ(t) is number of persons initially present on the link at

time t.

3) Thus, total number of people to evacuate from a link

is given by Equation (2), which includes the persons

on the road and also persons inside the shops. Let S(t)

be the number of people present inside shops on the

link in time bin t (counted by fourth surveyor). Then

the total number of persons to evacuate on the

concerned road TP(t) is:

() () ()TP t N t S t= +

(2)

4) Thus TP(t) is computed from the survey data. The

pre-evacuation coordinates of all agents are assigned

randomly on corresponding link.

5) In an evacuation problem, destination and route

choice are interrelated. For the simplicity, in the

present study, only one destination is used which

reduces the whole problem to one dimension only.

Fig. 1. Iterative appraoch of MATSim (Horni et al. 2015).

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The post-evacuation location coordinate (destination)

of each agent is modeled as a virtual point formed far

away from the center of the evacuation area. All the

exits are connected to this artificial node termed as

“super sink” (see Figure 2).

Fig. 1. Exit connected to a super sink (a virtual destination).

4. Scenario set up

Sarojini Nagar is located in the south west district of

Delhi. It is one of the most popular market in Delhi. This

was one of the site which was bombed in Delhi on 29

October 2005 and resulted in many deaths and major

injuries (NCTC, 2006). Thus, because of its past history,

it is chosen as a site for potential disaster location.

4.1. Case study: Sarojini Nagar market

The surveys were conducted between 4 September and 7

September, 2014 as illustrated in Section 3. Generally,

evacuation planning is composed of the following steps

(Lahmar, Assavapokee, & Ardekani, 2006):

1) Impact zone: In this step, the evacuation zone is

identified. It is generally dependent on the type of

emergency. In some cases only small area needs to

be evacuated while in some cases complete area

needs to be evacuated, in the present study, for the

case of potential bombing scenario, complete market

is considered as evacuation area. Figure 3 shows the

complete market of Sarojini Nagar market.

Fig. 3. Market area (in red) under consideration for evacuation.

2) Assignment of evacuees to shelter: After defining

area to be evacuated, next is to decide where to

evacuate people. In the case study all the exits are

assumed as a potential shelter. Once the agent is out

of the particular exit, he/she is assumed to be safe.

3) Traffic routing (determining driving direction at each

road): In this step, the best route to reach the shelter

is determined. Different strategies have been

considered in the case study presented in the Section

4.3.

4) Self-evacuation: Agents starts evacuating as soon as

warning is announced. They follow the evacuation

plan considered in various scenarios.

4.2. Simulation inputs

The Sarojini Nagar market area remains crowded most of

the time of the day. Motorized and non-motorized

vehicles are rarely used inside this market area and

therefore all vehicles are ignored in the present study.

Only the walk mode is considered in the simulation

framework.

• Network - The desired evacuation area of Sarojini

Nagar market is taken from Open Street Map

(OpenStreetMap, 2015). All exits of this area are

connected to a safe virtual destination, which is far

away from the centre of selected area. All exits have

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same exit capacities. It is assumed that travel time

from exit points to the safe virtual destination is zero.

The network is converted to desired format of the

simulation framework. The width of the streets are

measured during survey and eventually, due to heavy

encroachment, the effective width of link is

estimated as 4 m which results in a capacity of about

1000 PCU/h per direction. The roads in the Sarojini

Nagar market are partially tiled and partially

concrete and are in good condition. A poor condition

of the surface may increase the evacuation time.

• Plans - In the evacuation situation, only two types

of activities i.e. pre-evacuation and post-evacuation

are considered. The activity locations of these

activities are the locations of agents before and after

the evacuation respectively. All agents use walk

mode to travel between these activities. It is assumed

that all agents start moving out of the market area as

soon as warning is announced. Initial positions and

the density of the agents on each link is calculated

from the survey (see Section 3). In the

simulation, the speed of the agent is assumed as 6

km/h. Passenger car unit (PCU) of the agent is taken

as 0.08 (Tiwari, Fazio, & Gaurav, 2007). Overall,

about 8430 agents are evacuated from the market

area.

• Choice dimension - In this study, 20% of agents are

allowed to change their route until 80% of the

iterations. Simulation is run for 100 iterations. Rest

of the agents until 80% iterations and all agents after

that select a plan from their choice set only which

stabilizes the demand.

4.3. Scenarios

The first step is to identify the bottleneck links in order to

identify the cause and then propose necessary strategic

decisions to rectify and improve the overall evacuation

time. No single hypothetical scenario is expected to

perfectly emulate a real event that will occur in future.

Thus, different situations are considered for evacuation of

pedestrian in market place. These scenarios help in

generating the evacuation plan for an open space

environment. The following scenarios are considered.

• Scenario 1: (No Evacuation Plan): In absence of

any evacuation plan, all agents are left to themselves.

This would replicate the existing situation of the

market area.

• Scenario 2: (Shortest Path): In this scenario, it is

assumed that all agents will evacuate by running to

the nearest exit, taking the shortest path between

their current location and exit. This is recorded as the

“shortest path evacuation time”.

• Scenario 3: (Benchmark Evacuation with

encroachment): In this scenario, evacuation time is

identified based on Nash equilibrium (Lämmel,

Rieser, & Nagel, 2008). Evacuation time calculated

using this approach is termed as the “benchmark

evacuation time”. In reality, it is not possible to

achieve benchmark evacuation time (since this is a

result of several iterations of MATSim with learning

of each outcome) but it is useful to generate a

feasible evacuation plan and compare it with

benchmark time. Streets of the market area are

heavily encroached therefore, in this scenario, the

evacuation time is estimated with the existing

situation.

• Scenario 4: (Planned Evacuation): As discussed

before, in this scenario, an evacuation plan is

proposed aiming to achieve the evacuation time same

as benchmark evacuation time and in turn expecting

to be better than shortest path scenario or no

evacuation plan scenario. The resulting time is called

“planned evacuation time”. This scenario will result

in the development of a route guiding map. In case of

an emergency, these routes can be followed from the

current locations of all agents.

• Further, after analyzing the scenario based on the

link flow in peak hours, more recommendations such

as widening of bottleneck links by removing

encroachments and adding new emergency exits will

also help in further reduction in the evacuation time.

5. Results

It is clear that in the absence of any planning and signage

(Scenario 1), all agents may produce herding behaviour

which results in early degradation of network supply.

Thus, evacuation time will be higher than all other

scenarios due to sheer chaos.

In scenario 2 (shortest path evacuation plan)

everybody moves to their geographically nearest exit

point. This kind of plan is most easy to implement,

because of its unique solution. It is only required to put

sign at crossing of street network. The big disadvantage

of such strategy is that, it does not take congestion in

consideration. Congestion avoidance is important in case

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of evacuation. According to Schadschneider et al. (2009),

to reduce the congestion, two corrective actions can be

taken: change of geometry (wider escape paths) and

proper guidance through signage which helps in

improving orientation capability. Our methodology

intervenes at two levels: it develops signage for the

existing geometry and also makes recommendation on

specific geometry change for faster evacuation.

In the scenario 3 (benchmark scenario), the fastest

route is computed using iterative algorithm of MATSim.

This kind of plan (benchmark) can only be implemented

with proper training and repetitive mock drills, which is

not feasible in practice. Therefore, a practically feasible

evacuation plan is proposed in scenario 4, in which

consistent direction signs are placed at all relevant

locations. A snapshot of such a plan is shown in Figure 4.

Heuristic (colour scheme) and evacuation time of agents

help in making the evacuation plan. Routes that are closer

to exit but with high congestion are bypassed, agents

diverted to a route where there is lesser congestion. The

main advantages of this plan over shortest path plan are

that it considers the congestion effects into consideration

and it is easy to implement. This route guidance map will

also serve as a reference for concerned authorities to

provide evacuation route related instructions to evacuees.

Different scenarios (from Section 4.3) are compared

based on total evacuation time and average evacuation

time per person. The former is the total time to evacuate

all the agents out of the evacuation area. Statistically total

evacuation time is not a good measure for finding

effectiveness of a given evacuation strategy. Thus,

average evacuation time is required, which not only

minimizes the response time but at the same time also

maximizes the flow at given time (Hamacher & Tjandra,

2001). Table 3 shows the results obtained from these

scenarios. Clearly, as expected, benchmark scenario has

the least total evacuation time and average evacuation

time. This corresponds to a first-best condition in which

everyone knows the prevailing congestion conditions and

the best route to exit. Further, for the case of planned

scenario, the total and average evacuation time is

significantly shorter than shortest path scenario and

marginally higher than benchmark scenario.

The comparison of evacuation progress is shown in

Figure 5. It can be observed that evacuation time is the

same for all three scenarios until 50% of the agents are

evacuated. Afterwards, evacuation progress for the

shortest path scenario becomes slowest and evacuation

progress of the benchmark scenario become the fastest.

The links towards Exit A (see Figure 4) become

bottlenecks in the shortest path scenario (observation

from simulation output). Thus to make an effective use of

all the exits, some agents are diverted to another exit. The

procedure is repeated for all other exits. In this way,

planned scenario routing strategy is developed making

use of benchmark routing strategy. The evacuation

progress of planned scenario is marginally slower than

the benchmark scenario.

Effectiveness of the approach: In order to see the

effectiveness of the route guidance map, the same

methodology has been applied to two other markets of

Delhi (India) namely, Lajpat Nagar and Laxmi Nagar.

Evacuation plan for these sites are developed. It can be

Fig. 4. Guiding map: A feasible solution.

Table 3. Average evacuation time per person and total

evacuation time

Scenario evacuation time (min)

average

total

Shortest path 10.05 23.05

Benchmark 8.94 16.46

Planned 9.45 17.32

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observed from Table 4 that planned scenario response

time is better than the shortest path evacuation strategy.

Thus, clearly, the methodology is transferable to any

scenarios where such kind of short-notice evacuation is

required.

6. Discussion

The present study shows the necessity of an evacuation

plan for improving safety and response efforts. The study

provides strategic and tactical recommendations to

improve the response time in case of an emergency

evacuation. Strategic recommendations include

increasing network supply side by making new routes or

by widening the existing roads. These strategic

recommendations help planners to decide the increase in

the capacity of roads, or where an emergency exit should

be made to further improve evacuation response.

Statistical and tactical recommendations deal with

effective utilization of existing capacity. This can be

achieved by properly routing the evacuee through a street

network in order to minimize danger and ensure safety.

The simulation returns the route assignment policy to

reduce congestion and improve response time, which

eventually will reduce the collateral damage. A policy

imperative from this study is that even a static plan would

help in reducing the evacuation time.

Further this work and methodology can also be used

to determine the maximum allowed safe occupancy for an

event in open area, a parameter that can be imposed by

decision makers by way of policy. For this, a safe level of

evacuation time must be determined through consultation

with relevant experts. The evacuation time consists of

two time components: reaction time and egress time

(Kuligowski, 2013). In the present study only latter is

considered and estimated whereas it is assumed that the

agents react instantly after the warning.

This lays a future research direction to incorporate

the different reaction times for different group of persons

depending on the factors such as age and sex similar to

the work by (Agarwal, Lämmel, & Nagel, 2016b) in

which the authors incorporated a uniform reaction time

for all drives in the queue model. Another important

observation of the study is that the egress time is highly

affected by heavy encroachments. Clearly, removing

these encroachments will ease some capacities and would

reduce evacuation time significantly.

In the literature, it has been argued that people tend

to misjudge the likelihood of a disaster event and range of

severity of its impact. This would in turn result in a

different outcome; such behaviors are out of the scope of

the present study.

7. Conclusion

Evacuation time is a critical factor for developing

evacuation strategies. In this work, a methodology to

prepare a guidance map was developed using an agent-

based simulation framework. Initial inputs were

calculated from different surveys. An event of potential

bombing was considered as an example of the disaster

where immediate evacuation is required due to a disaster

on the same location in the past. A real-world case study

of Sarojini Nagar market, Delhi was considered.

Different scenarios were considered and their total and

average evacuation time were compared. It was shown

that with the help of proper signages in planned scenario;

the total and average evacuation time would be

significantly lower than shortest path or no evacuation

plan scenarios and marginally higher than benchmark

scenario. The planned scenario routing map would help

Fig. 5. Plot of evacuation time for different scenario.

Table 4. Total evacuation time for Lajpat Nagar and

Laxmi Nagar markets.

Scenario

Total evacuation time

(min.)

Lajpat Nagar

market

Laxmi

Nagar

market

Shortest path 13.25 11.51

Benchmark 10.01 7.34

Planned 10.46 9.53

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the authorities (the security staff) to guide or push

evacuees. The effectiveness of the proposed methodology

was shown using the same approach for two more

markets of Delhi.

Future work includes an analysis of the robustness of

the suggested evacuation plan, particularly with respect to

distribution of people in the market. Development of a

dynamic evacuation plan, that is, one with message signs

that change dynamically with congestion distribution is

another possible extension. With this, accounting for

behavioral characteristics of agents, after developing

appropriate models for the same, can induce even more

realism in the recommendations.

Acknowledgments

This project is funded by the Human Settlement

Management Institute, Housing and Urban Development

Corporation Limited (HUDCO). The authors also would

like to thank Dr. Gregor Lämmel for his helpful

comments. Lastly the authors would like to acknowledge

the contribution of Prem Chand and Rama Shankar for

carrying out several supporting tasks throughout the

duration of the project.

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