2HOW THANKSGIVING AND SUPER BOWL TRAFFIC CONTRIBUTE TO FLIGH.docx

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HOW THANKSGIVING AND SUPER BOWL TRAFFIC
CONTRIBUTE TO FLIGHT DELAYS Comment by Jeremy
Hodges: You should have a meaningful title that describes what
your study is about. Start with a word like “examine” or
“explore” to identify the type of study you conducted.
by
XXXXX

A Graduate Capstone Project Submitted to the College of
Aeronautics,
Department of Graduate Studies, in Partial Fulfillment
of the Requirements for the Degree of
Master of Science in Aeronautics
Embry-Riddle Aeronautical University
Worldwide Campus
May 2018
HOW THANKSGIVING AND SUPER BOWL TRAFFIC
CONTRIBUTE TO FLIGHT DELAYS
by
XXXXX
This Graduate Capstone Project was prepared under the
direction of the candidate’s Graduate Capstone Project Chair,
XXXXX, Comment by Jeremy Hodges: Dr. Jeremy Hodges
Worldwide Campus, and has been approved. It was submitted to
the

Department of Graduate Studies in partial fulfillment
of the requirements for the degree of
Graduate Capstone Project:
___________________________________________
XXXXXXXX. Comment by Jeremy Hodges: Jeremy Hodges,
PhD
Graduate Capstone Project Chair
________________
xxii
Date
xxii
ii
xxii

xxii
ixAcknowledgements Comment by Jeremy Hodges: Add any
acknowledgments here. You may use first person in this
section, but avoid it everywhere else.
I'd like to thank my legs, for always supporting me; my arms,
who are always by my side; and lastly my fingers, I can always
count on them.
Abstract
Scholar: XXXXX
Title: How Thanksgiving and Super Bowl Traffic Contribute
to Flight Delays
Institution: Embry-Riddle Aeronautical University
Degree: Master of Science in Aeronautics
Year: 2017
This study explores the effects of non-scheduled flights on
scheduled flight delays during Thanksgiving and Super Bowl
across 5 years. Flight delay data were collected from the Bureau
of Transport Statistics and the Federal Aviation Administration.
Super Bowl and Thanksgiving were chosen as the special events
of interest for this study as they provided complementary
datasets. Super Bowl showed an increase in non-scheduled
flights whereas Thanksgiving showed greater scheduled flight
operations. The results of this study concluded that scheduled
flights showed greater delays during Super Bowl when
compared to Thanksgiving. A significant interaction was also
found to exist between scheduled and non-scheduled flights
operating during the two special events. Both scheduled flight
delays and non-scheduled flight delays increased during Super
Bowl. However, during Thanksgiving this relationship did not
exist – scheduled flights had much fewer delays than non-
scheduled flights. Due to the increase in the number of non-
scheduled flight operations during Super Bowl, delays increased
thereby increasing operating costs for flights. The outcomes of
this study shed light on another aspect of airspace efficiency
that could be researched to reduce costs and improve user

experience. To mitigate these potential issues, it is important all
pilots receive special training to predict potential bottlenecks
and delays while operating at airports hosting special events.
Comment by Jeremy Hodges: The abstract should be a 100-
120 word synopsis of the entire paper. It is not an introduction
paragraph. Be sure to include text that relays your problem
statement, significance, literature review subject, methodology,
finding, conclusion, and recommendations.
Table of Contents
Page
Graduate Capstone Project Committee ii
Acknowledgements iii
Abstract iv
List of Tables viii
List of Figures ix
Chapter
I Introduction 1
Significance of the Study 2
Statement of the Problem 2
Purpose Statement 2
Research Question 3
Delimitations 3
Limitations and Assumptions 3
List of Acronyms 3
II Review of the Relevant Literature 5
Reasons for Delay 5
Congestion in air traffic 5
Extreme weather 6
Growth Resulting in Increased Delays 7
Cost of Flight Delay 8
Cost to airlines 8
Cost to passengers 9
Effects on other industries 9
Flight Delays and Non-Scheduled Flights 9
Special Events Traffic 10

Summary 11
III Methodology 12
Research Approach 12
Design and procedures 12
Apparatus and materials 13
Sample 13
Sources of the Data 14
Validity 15
Treatment of the Data 15
IV Results 17
Descriptive Statistics 17
Scheduled and Non-Scheduled Comparison 17
Super Bowl Delays and Thanksgiving Delays Phoenix (2014 –
2015) 18
Super Bowl Delays and Thanksgiving Delays New York (2013 –
2014) 20
Super Bowl Delays and Thanksgiving Delays New Orleans
(2012 – 2013) 20
Super Bowl Delays and Thanksgiving Delays Indianapolis (2011
– 2012) 21
Super Bowl Delays and Thanksgiving Delays Dallas (2010 –
2011) 22
Comparing Special Event and Type of Operations 22
V Discussions, Conclusions, and Recommendations 25
Discussions 25
Conclusions 26
Recommendations 27
References 29
List of Tables
Page
Table

1 Descriptive Statistics for Delays During Type of Operation
and Special Event 16
List of Figures
Page
Figure
1 Comparison Between Thanksgiving and Super Bowl for
Scheduled and Non-Scheduled Flights. 17
2 Departure Delays, Taxi Out Times, Taxi In Times, and
Total Flight Delays for Scheduled Flights Across Thanksgiving
and Super Bowl. 17
3 Average Total Delay Compared Between Scheduled and
Non-Scheduled Flights 18
4 Interaction Between Type of Operation and Special Event.
23
Chapter I
Introduction Comment by Jeremy Hodges: In this section, I
expect about 300-900 words developing the background of the
issue. Why is the issue important for studying? Support your
statements with recent references.
Flight delays associated with special events during the year
require additional planning by Air Traffic Control (ATC). The
National Airspace System (NAS) implements Special Traffic
Management Programs (STMPs) for special events such as
holiday travel during Thanksgiving and the Super Bowl (Krozel,
Hoffman, Penny, & Butler, 2003). Additional flights scheduled
by airlines during these special events saturate the airspace and
the airports in the vicinity of where the special event is taking
place. Congested airspace may lead to greater delays for the
traveling public. Specialists in the Air Traffic Control System

Command Center (ATCSCC) note that travel on the Sunday
following Thanksgiving increases significantly (Krozel et al.,
2003). Similarly, traffic increases following Super Bowl
Sunday. Therefore, exploring the interaction of delays
associated with specific types of flights operating during special
events may aid in optimizing the throughput of the NAS.
Comment by Jeremy Hodges: Throughout your entire text,
everything should be simply double spaced. No additional
spaces between paragraphs, tables or figures, unless appropriate
for starting a complete section or table/figure on a new page.
All your sentences should have two spaces after them.
Your paragraphs should be organized; a thesis statement with
supporting statements. 3-7 sentences is usually appropriate.
Don’t mix ideas in a run-on paragraph.
Avoid first person and possessives such as “our country”.
Tables and figures must be formatted in accordance with the
ERAU GCP guide. Examples are on page 63 and 71…do them
right the first time.
Citations…parenthetical citations always have a year.
Examples are on page 174 and 175 of the APA 6 manual.
Don’t ask questions as if you are having a conversation.
Present everything as your own ideas, and provide references
for those factual statements (especially those with numerical
data).
This study focused on scheduled airline flights and how they are
affected during special events. Factors that were considered in
this study included non-scheduled flights such as private or
charter flights. Clare and Richards (2013) state that traffic flow
management relies on predictions of demand in the airspace
system. This can become difficult to manage due to factors such

as unscheduled demand at these special events which may vary
yearly. Inefficient traffic flow may cause greater delays and
costs for scheduled air carrier service which propagate to
operations across the entire NAS. The Super Bowl and
Thanksgiving weekend were used to analyze the effects of non-
scheduled flights on scheduled operations across multiple years.
Significance of the Study Comment by Jeremy Hodges: Here
you will demonstrate why the study is of significance to the
field. This is solidifying your contribution to the field.
The significance of this study was to discover the effects on
scheduled flights during special events. This research can assist
the ATC system to manage, direct, and mitigate potential delays
and congestion associated with special events during the year.
The results of this study may also be able to assist airlines in
developing flight schedules that consider the effects of non-
scheduled flights. Using this information, airlines and the
Federal Aviation Administration (FAA) can decrease costs by
reducing delays. By exploring factors, such as types of flights,
stakeholders in aviation can make changes to mitigate or even
eliminate the negative impacts of flight delays during special
events.
Statement of the Problem Comment by Jeremy Hodges: This
section needs to be about 200-300 words, concretely articulating
the problem to be addressed. This section should have
supporting data from current references describing what the
problem is. End this section with a very specific statement such
as: The problem examined (or explored for qualitative studies)
in this study was…
Flight delays have become a prominent issue in the aviation
industry as it grows in traffic and density. A steady growth has
been occurring in the number of aircraft that are flying in the
NAS. With a thorough understanding of why delays occur,
appropriate modifications to the airspace system can be made to
increase capacity. There are many studies that look at flight
delays in the NAS, however, not many evaluate delays
associated with special events and effects of non-scheduled

flights. Using delay data from the Department of Transportation
(DOT) and the FAA, the researcher will better understand
delays associated with scheduled and non-scheduled flights.
Purpose Statement Comment by Jeremy Hodges: Here you will
describe what you will do in this study about the problem you
described above.
The aim of this study was to compare flight delays of scheduled
flights during Thanksgiving versus Super Bowl, when there is a
greater rate of non-scheduled flights. By comparing delays
during these two events, a greater understanding of the effects
of non-scheduled flights on scheduled flights can be achieved.
Research Question (and/or) Hypothesis Comment by Jeremy
Hodges: You don’t need a lot of text here. For quantitative
studies (when you’ll be using a t-test or similar statistical test)
you’ll say something like: The guiding hypothesis for this
study was: Dependent variable A increases/decreases due to
variations in Independent Variable B. The null hypothesis was:
There is no relationship between Variable A and Variable B.
For qualitative studies (when you’re interviewing or doing a
historical study) you’ll provide an open ended question like: To
what extent does Variable A respond to changes in Variable B?
Using Airline Delay Data from the Bureau of Transport
Statistics (BTS) and Non-Scheduled Flight Delays from the
FAA, statistical tests were conducted to analyze if a significant
increase in delays occurs during special events. The question
posed in this study is to discover if delays exist for scheduled
flights due to an increase in non-scheduled flights during Super
Bowl compared to Thanksgiving.
Delimitations Comment by Jeremy Hodges: What is the study
specifically not about?
The study was limited to observing Thanksgiving weekend and
Super Bowl weekend. These special events were singled out as
they provided two complementary datasets. Thanksgiving
provides a similar number of total flights operating in the NAS
compared to Super Bowl, while, the Super Bowl provides a
greater number of non-scheduled flights compared to

Thanksgiving.
Limitations and Assumptions Comment by Jeremy Hodges:
Thoroughly discuss limitations of the study. What do you see
as a limitation to the generalization of the findings? Discuss
your assumptions that were necessary for completing the study,
such as participants responded honestly, data retrieved was
accurate, etc.
The airports used in the study were limited to locations in
the vicinity of the following Super Bowl. Total flight time for
non-scheduled flights was assumed to be from doors closed to
doors open. Therefore, delay data was calculated based on the
total flight time specified from the ATC system. Flights were
analyzed from 2010 to 2015 due to the limitations in acquiring
non-scheduled data from the FAA.
List of Acronyms
ATC Air Traffic Control
ATCSCC Air Traffic Control System Command Center
BTS Bureau of Transport Statistics
DOT Department of Transportation
FAA Federal Aviation Administration
GDP Gross Domestic Product
MIT Miles in Trail
NAS National Airspace System
NBAA National Business Aviation Association
STMP Special Traffic Management Program
TFMS Traffic Flow Management System
Chapter II
Review of the Relevant Literature Comment by Jeremy
Hodges: Your literature review should generally be 10-20
pages. You should have an introduction paragraph that
describes the areas around the central subject and how the
chapter is organized. Then proceed with an appropriate
synthesis of the literature on each topic…not just a summary of
each article you read. Develop a framework of how it all fits
together to support the known extant of literature on the
subject.

The definition of a delayed flight according to the FAA (2015)
is the departure or arrival time exceeded by 15 minutes when
compared to the scheduled departure or arrival time. These
delays cause expenses to rise for airlines operating with very
thin margins. Additionally, passengers must bear the cost of
delays as well in the form of time lost in productivity. A
review of research conducted about the causes of flight delays
and the associated economic impacts will be reviewed in this
chapter. Furthermore, the impacts of non-scheduled flights on
the NAS will be examined to better understand airspace
congestion.
Reasons for Delay
Several factors can cause delays to increase in local
environments expanding to the entire country. These delays can
be caused by a variety of reasons ranging from late arriving
aircraft to passenger or baggage delays. With the growing
numbers of air traffic, airspace congestion is becoming a larger
issue to solve. The FAA (2009) forecast a 50% increase in flight
operations between 2010 and 2025. Unless new procedures are
put in place, the amount of delay encountered by aircraft will
only become worse as the traffic density increases.
Congestion in air traffic. Most delays are encountered around
bottlenecks in the airspace system. These usually occur due to
limited number of runways available for aircraft to utilize.
Glockner (1996) states that one method of reducing congestion
would be to increase landing capacity. Flights operating to
airports in cities hosting special events would experience these
delays. Events such as the Super Bowl, National Business
Aviation Association (NBAA) conferences, Oshkosh Fly-In, etc.
can cause excessive delays due to congestion.
Flow management tries to alleviate some of the delays
encountered due to overcrowding of terminal airspace or
airports. When aircraft reach a point of congestion, ATC starts
managing and routing them into a long airborne queue to
sequence them into the airport. Rerouting these aircraft or
placing them in holding patterns increases the amount of fuel

burned for these aircraft, thereby increasing the costs
(Glockner, 1996). Additionally, speed reductions and altitude
changes required by aircraft en route to a congested airspace
may increase fuel consumption and directly incur greater
expenses in the current system (Delgado & Pratz, 2014).
Therefore, understanding and recognizing potential congestion
during certain seasons or events can aid in reducing costs and
maintaining efficiency.
Another aspect of congestion stems from the limitations of
airport capacity at arrival and departure fixes. These fixes act as
gates for entry or exit points for the terminal environment of the
airport. Each airport has a limited number of fixes that can be
utilized in different runway configurations, resulting in limited
capacity for the airport to handle mass arrivals and departures.
Gilbo (1997) concludes effective utilization of airport capacity
is affected by under and overloaded fixes in the terminal area.
Aircraft arriving from one direction in large quantities can
cause an airport or a cluster of airports to experience significant
delays due to sector saturation.
Extreme Weather. A major expense associated with operating an
airline is the cost of flight delays (McCrea, Sherali, & Trani,
2008). Global weather patterns are constantly changing and can
be difficult to predict. Hurricanes can grow in strength over a
short period, making it difficult to compensate with the flight
schedule. Most extreme weather events require large in flight
reroutes or significant delay on the ground to maintain a safe
following distance in the air. To achieve this, ATC issues
ground holds for aircraft at the airport until sufficient distance
is created, allowing for greater spacing in the airspace system.
A loss of capacity due to extreme weather causes 93% of flight
delays at hub airports according to (Clarke, 1998).
These occurrences of severe weather conditions not only affect
the timely arrival or departures of aircraft, but also the
effectiveness of air traffic management systems controlling the
safe transit of passengers (Walker, Chakrapani, & Elmahboub,
2008). Due to the unpredictability of weather, delays associated

with it are harder to mitigate and plan for in advance. The
formation of thunderstorms as meteorological events and the
impact on aviation safety is another aspect that is of interest in
further research for reducing delays.
Growth Resulting in Increased Delays
Aviation is a growing industry requiring greater capacities
at airports and in the NAS. Evans & Schäfer (2011) state that
the current capacity will be exceeded in the coming years, even
with updates planned for the NAS. As the total capacity of the
airspace environment reaches close to complete saturation,
delays will increase resulting in greater costs for users.
Airspace will become more restrictive due to the increase in the
number of aircraft operating in the same amount of space. With
nearly 1/4 of all flights experiencing delays greater than 15
minutes in 2007, system saturation is slowly becoming an
apparent issue that needs to be dealt with soon (Peterson et al.,
2013). These delays will continue to increase until new
technology is implemented to increase NAS capacity.
Cost of Flight Delay
The dawn of the modern age of jet aircraft has resulted in
airlines accounting for a large part of the Gross Domestic
Product (GDP). According to the FAA (2015), the civil aviation
industry contributed up to 5.4% of the United States’ GDP. Due
to the aviation sector having such large impacts on the economy
of the country, greater efficiency would be an indispensable
factor for profitable airline operations. Flight delays not only
affect passengers traveling for business or leisure, but also
freight transportation around the country. Both cargo and
passengers contribute to the effect aviation as a form of
transportation has on the GDP of the country. Schumer (2008),
found that the total cost of air traffic delays in the United States
cost the economy $41 billion during the year 2007. The net
wealth for the United States would increase by $17.6 billion
with a 10% reduction in flight delay and by $38.5 billion with a
30% reduction (Peterson et al., 2013). This economic cost
includes the direct cost savings for airlines as well as the

indirect effects of saved time for passengers and cargo.
Cost to airlines. Airlines in the United States have been able to
operate efficiently and effectively to keep operating costs down.
From 1995 to 2014, the average fare for an airline itinerary,
adjusted for inflation, has increased 34.1% from $292 to $392
according to BTS (2015). Amidst such tight cost control and
attention to efficiency in order to keep airline ticket prices low,
flight delays costs airlines a large percentage of their operating
costs. Schumer (2008) states that $19.1 billion of airline
operating costs were attributed to delays. This is a significant
cost for the aviation service providers to absorb and continue
offering reasonable rates for transportation of people and goods.
Cost to passengers. Delays affect airlines in operating costs
both directly and indirectly. A cost is also incurred to the
passengers traveling on these flights. Baik, Li, and Chintapudi
(2010) showed flight delays in the domestic market cost
passengers $5.2 billion in 2007. These passengers lost valuable
time and productivity for their employers, businesses, or leisure
activities due to flight delays (Lubbe & Victor, 2012). Costs
were most likely higher as these passengers most likely missed
connection flights or had cancellations leading to lost pre-paid
hotel accommodations, ground travel plans, and vacation time.
It is clear that flight delays have effects that transpire farther
than NAS and the airlines.
Effects on other industries. Many other industries rely upon the
airline industry, such as food service, lodging, retail, ground
transportation, and entertainment. When the aircraft is delayed
on the tarmac or in the air, passengers cannot spend that lost
time in businesses providing commerce. Because of delays,
these industries combined lost about $10 billion (Schumer,
2008). This number did not include delays caused to cargo
being transported by air, which would affect the manufacturing
industry more than anything else would. Overall, delays
increase costs to all entities involved in air transportation
ranging from the airlines to industries reliant upon air
transportation for delivering customers and cargo to their

destinations.
Flight Delays and Non-Scheduled Flights
Delay management utilizes many procedures and methods
to reduce or eliminate delays. Methods such as flow
management utilize schedules to maintain efficiency and on-
time performance. Non-scheduled flights that do not publish
schedules in advance may cause issues and reduce efficiency of
techniques such as flow management. Aircraft travelling in the
NAS need spacing separation imposed as Miles-in-Trail (MIT).
Ensuring that aircraft pass from one sector of airspace to
another with the appropriate spacing for safety causes flights
delays (Sheth & Gutierrez-Nolasco, 2014). Research conducted
on the effects of non-scheduled flights and delays associated is
very limited and further investigation is necessary.
Special Events Traffic
The NAS experiences seasonal, weekly, and daily trends in
aircraft movements. Looking at the aggregate statistics, certain
trends repeat on a yearly basis. Seasonal trends indicated an
increase in air travel during the summer months and during
December. This corresponds with holiday travel presenting
peaks towards the start of school for students in August.
Additionally, Krozel et al. (2003), state a significant impact on
the NAS originates from Thanksgiving and Christmas travel.
Weekly trends in the NAS display a tri-modal distribution
indicating after weekdays, Sunday has the most travel for the
week.
These seasonal and weekly trends become a factor when
associated with special events such as the Super Bowl or winter
travel. Travel during events that occur on Sunday result in an
increase in traffic during the Thursday and Friday prior and the
Monday after the event (Krozel et al., 2003). These events do
not cause a significant strain on the NAS, as they are localized
events. However, airports near the Super Bowl or college games
experience delays due to airspace congestion. Compare this to
increased travel during the summer or winter months, traffic
loads increase across the entire NAS. Localized events such as

NBAA, Super Bowl, or college football games, etc., have
greater traffic due to on-demand aircraft that are not scheduled.
Large scale air traffic increases such as winter or summer
holiday travel is predominately scheduled traffic accounted for
by the airlines and other scheduled flight operations.
Summary
Many studies analyze the effects of large increases in traffic
volume, such as holiday travel, to better model flight schedules
and reduce delays. However, there is a lack of research
investigating the effects of flight delays for localized events
such as the Super Bowl or other events that affect flight delays
in metropolitan areas that are severed by a close grouping of
airports.
Comparing flight delays between localized events and the entire
NAS will provide further insight into certain causes of flight
delays not explored before. Results from research evaluating
delays for localized events can be used to evaluate current
procedures in place and the areas they lack in maintaining
efficiency. Furthermore, ATC service providers can use this
information to evaluate the circumstances that create the
greatest delays and put into place strategies that can actively
mitigate these bottlenecks.
Chapter III
Methodology
Research Approach Comment by Jeremy Hodges: Begin this
section with a statement like: The purpose of this quantitative
(qualitative) study was to examine (or explore) the relationship
between variable A and B. Describe the method used for the
study further in the remaining part of this paragraph.
This study used exploratory and quantitative methods to
compute the amount of delays scheduled air carriers experience
during special events. T-tests were conducted to compare means
between special events and types of flights to study the effects
on flight delays. Thanksgiving and Super Bowl were the
complementary special events that exhibited flight delays

associated with scheduled and non-scheduled flights. A greater
amount of scheduled traffic operates during the time of
Thanksgiving. On the other hand, a shift to more non-scheduled
flights is evident during Super Bowl. Analyzing the flight delay
experienced during these two special events could benefit
operators and service providers with a greater understanding of
delays. The flight delay data retrieved from the FAA website
and BTS were the primary sources for the analysis.
Design and procedures. This study was determined to be an
exploratory and quantitative study. Total flight delay was
measured for airlines and non-scheduled flights during two
special events through t-tests using the On-Time Arrival
Performance data from the FAA and BTS. This data contained
all scheduled and non-scheduled flights during Thanksgiving
and Super Bowl from 2010 to 2015. Super Bowl delays were to
be compared with Thanksgiving delays from the previous year
to minimize any external influences on traffic. The DOT
provides a 15-minute tolerance before a flight is considered
delayed. Despite the DOT definition for late flights, data were
analyzed based on flights exceeding scheduled total flight time,
i.e., a plane scheduled to complete a flight in 2 hours but
actually completing it in 2 hours and 1 minute would be
considered 1 minute late. Comment by Jeremy Hodges:
Discuss your research design methodology, quantitative or
qualitative and the procedures you used.
Flight delay data for airlines was collected and organized from
the BTS website. The data was limited to the airports in the
vicinity of the Super Bowl venue. Similarly, non-scheduled
flight data was collected and organized from the FAA Traffic
Flow Management System (TFMS) database for the same
airports as the scheduled flights. The data was collected for the
day before, the day of, and the day after the Super Bowl and for
the Saturday, Sunday, and Monday immediately after
Thanksgiving. These days of the week were chosen during
Thanksgiving because the Sunday following Thanksgiving is
one of the busiest travel days of the year. This approach

allowed for consistent traffic between the two special events.
Using flight delay data from Thanksgiving allowed for data that
provided a greater number of scheduled operations while flight
delay data for the Super Bowl provided greater number of non-
scheduled operations. Between the two events, each year
provided a similar number of total aircraft. T-tests were
conducted to measure the difference in delays between the
events and types of flights.
Apparatus and materials. This project used Microsoft Excel and
SPSS to organize, code, and analyze the flight delays. Microsoft
Excel was used to organize all the scheduled flights and non-
scheduled flights into separate sheets for Thanksgiving and
Super Bowl. This data was transferred to SPSS to conduct the
descriptive and statistical tests such as t-tests and ANOVAs.
Comment by Jeremy Hodges: What software or other
materials did you use for your analysis?
Sample Comment by Jeremy Hodges: What was your sample
data?
The sample for this study was all air traffic, scheduled and non-
scheduled flights, operating to the specific cities during
Thanksgiving and Super Bowl from 2010 to 2015. This data
compares total flight delay for scheduled and non-scheduled
flights. Data for each special event focused on flights on
Saturday, Sunday, and Monday to avoid any unintended
inference from traffic fluctuations between the two events and
different days of the week for travel. Travel on these 3 days was
important because both events have a substantial amount of
travel occurring during these times. The Super Bowl occurs on a
Sunday and results in an increase in aircraft arrivals and
departures around the event. Thanksgiving has historically had
the highest amount of travel on the Sunday and Monday
following the event. Both events provide complimentary
datasets. Most people travel back home after spending time with
their families on Thanksgiving and result in a surge of traffic
during the weekend and the start of the week. This results in
greater scheduled operations by the airlines for this event. Many

individuals travel to the Super Bowl the day before or on the
day of the game, consequently increaseing non-scheduled
traffic. Many people are traveling to be back home in time for
the start of the week following the event resulting in increased
traffic on Monday. This results in greater non-scheduled traffic
in the form of charter or private aircraft flying.
Sources of the Data Comment by Jeremy Hodges: Where were
your data located and how did you retrieve it?
Data for this study was collected from two different sources.
Scheduled airline flight delay data was obtained from the BTS
website using the On-Time Performance table. Non-scheduled
flight data was acquired from the FAA using their TFMS data
that tracks flight delays in the NAS. This data from the FAA
was collected by contacting the Air Traffic Control System
Command Center (ATCSCC).
Validity Comment by Jeremy Hodges: Discuss the validity of
the study, specifically, why this study, data, and analysis
method valid to evaluate the problem.
The data collected for this study is assumed to be reliable
and valid as it is from a government source. The total flight
delay times were calculated from this data, and the reported
information is assumed to be reasonable. Scheduled flight times
are presumed to be reported appropriately by the airlines for all
flights they operate. The non-scheduled flight data was
calculated based on the flight plan filed by the crew. The flight
times for these non-scheduled aircraft are assumed to be filed
with the ATC system accurately and valid for this study. Based
on the flight time data from these flight plans, total flight
delays were calculated for non-scheduled aircraft.
Treatment of the Data Comment by Jeremy Hodges: How did
you organize and manipulate the data to resolve your research
question or hypothesis?
The raw data gathered from BTS and the FAA TFMS system
was organized in excel to compare consistently. The only

common factor between both sets of data was total flight time.
This was the factor that would be focused on for both events
and types of flights. Total flight delay for both sets of data was
generated by finding the difference in time between scheduled
and actual. Total flight delay was calculated only for scheduled
flights which were not cancelled or early.
Descriptive statistics were computed for scheduled and non-
scheduled flights. Additional data available to be computed for
the scheduled aircraft included departure delay and arrival
delay. This data was computed by subtracting the actual time of
departure or arrival from the scheduled times. Flights that were
cancelled or arrived early were coded as zeroes.
The data was imported into SPSS after all the data was
calculated, organized, and coded. Descriptive statistics and t-
tests were run to determine total number of flights for type of
traffic and event. The average delay was computed for each year
between Super Bowl and Thanksgiving as well as types of
flights. Once all these statistical tests were run, an ANOVA was
run to find any significant interaction between the types of
flights and between Super Bowl and Thanksgiving.
Chapter IV
Results Comment by Jeremy Hodges: I usually see students
try to cut corners in these last chapters…providing the bare
minimum. Remember, you are graduate students, this is the
culmination of your program and you are to contribute
something to the field of study you are in. Don’t try to get by
writing only two paragraphs in a chapter. Provide a good
introduction paragraph and summary paragraph in each section.
Descriptive Statistics
The dependent variable for this study included total delay,
departure delay, taxi out time, and taxi in time. Table 1 shows
the descriptive statistics for delays occurring during Super Bowl
and Thanksgiving for scheduled and non-scheduled flights.
Table 1 Comment by Jeremy Hodges: Follow this example of

a properly formatted table.
Descriptive Statistics for Delays During Type of Operation and
Special Event
Special Event
Type of Operation
N
Min
Max
Mean
SD
Super Bowl
Scheduled
11882
0
157.00
5.50
12.85
Unscheduled
9314
0
1502.00
4.94
36.26
Total
21196
0
1502.00
5.25
25.89
Thanksgiving
Scheduled
14668
0
93.00

2.05
5.47
Unscheduled
4499
0
765.78
3.81
26.99
Total
19167
0
765.78
2.47
13.95
Total
Scheduled
26550
0
157.00
3.59
9.66
Unscheduled
13813
0
1502.00
4.57
33.53
Total
40363
0
1502.00
3.93

21.13
Note. Mean, Min, Max, and SD are measured in minutes. Min =
Minimum, Max = Maximum, SD = Standard Deviation. Data
from 2010-2015 were used for this analysis.
Scheduled and Non-Scheduled Comparison
The total number of scheduled and non-scheduled flights by
years are displayed in Figure 1. The years are divided by
Thanksgiving and Super Bowl. Figure 2 displays the average
departure delay, taxi out times, taxi in times, and the total delay
encountered by scheduled flights. Average scheduled and non-
scheduled flight delays are presented in Figure 3.
Figure 1. Comparison Between Thanksgiving and Super Bowl
for Scheduled and Non-Scheduled flights. TG = Thanksgiving,
SB = Super Bowl. Comment by Jeremy Hodges: Follow this
example for a properly formatted caption for a figure.
Figure 2. Departure Delays, Taxi Out Times, Taxi In Times, and
Total Flight Delays for Scheduled Flights Across Thanksgiving
and Super Bowl. TG = Thanksgiving, SB = Super Bowl
Figure 3. Average Total Delay Compared Between Scheduled
and Non-Scheduled Flights. TG = Thanksgiving, SB = Super
Bowl.
Super Bowl Delays and Thanksgiving Delays Phoenix (2014 –
2015)
The null hypothesis was that there is no difference in total delay

for scheduled flights between Super Bowl and Thanksgiving.
The assumption of equality of variance was tested. Levene’s
test of equality of variance was significant (p < .05), and thus,
an adjustment to degrees of freedom was made. The mean total
delay for Super Bowl (M = 6.719, SD = 12.996) was longer
than the mean total delay for Thanksgiving (M = 2.473,
SD = 5.878). An independent samples t-test was significant,
t(3102.89) = 14.542, p < .001. Therefore, the null hypothesis
was rejected.
for non-scheduled flights between Super Bowl and
Thanksgiving. The assumption of equality of variance was
tested. Levene’s test of equality of variance was significant (p <
.05), and thus, an adjustment to degrees of freedom was made.
The mean total delay for Super Bowl (M = 6.982, SD = 57.384)
was longer than the mean total delay for Thanksgiving (M =
3.541, SD = 22.710). An independent samples t-test was
significant, t(3180.27) = 2.425, p < .05. Therefore, the
null hypothesis was rejected.
Super Bowl Delays and Thanksgiving Delays New York (2013 –
2014)
was rejected.
.05), and thus, an adjustment to degrees of freedom was made.

was longer than the mean total delay for Thanksgiving (M =
4.271, SD = 29.964). An independent samples t-test was not
significant, t(2419.688) = 1.152, p > .05. Therefore, the null
hypothesis was retained.
Super Bowl Delays and Thanksgiving Delays New Orleans
(2012 – 2013)
was rejected.
tested. Levene’s test of equality of variance was not significant
(p < .05) and no adjustments were made to degrees of freedom.
was shorter than the mean total delay for Thanksgiving (M =
4.575, SD = 33.034). An independent samples t-test was not
significant, t(2037) = -0.871, p > .05. Therefore, the null
Super Bowl Delays and Thanksgiving Delays Indianapolis (2011
– 2012)
test of equality of variance was not significant (p < .05) and no
adjustment was made to degrees of freedom. The mean total

SD = 7.039). An independent samples t-test was not significant,
t(1063) = 0.242, p > .05. Therefore, the null hypothesis was
retained.
.05) and an adjustment was made to degrees of freedom. The
mean total delay for Super Bowl (M = 5.588, SD = 25.565)
was shorter than the mean total delay for Thanksgiving (M
= 2.896, SD = 28.037). An independent samples t-test was not
significant, t(293.727) = 1.416, p > .05. Therefore, the null
Super Bowl Delays and Thanksgiving Delays Dallas (2010 –
2011)
was rejected.
tested. Levene’s test of equality of variance was not significant
(p < .05) and no adjustment was made to degrees of freedom.
was longer than the mean total delay for Thanksgiving
(M = 3.143, SD = 21.559). An independent samples t-test was
not significant, t(3398) = 0.297, p = > .05. Therefore, the
null hypothesis was retained.
Comparing Special Event and Type of Operation

The objective was to find if there was a difference in delays
based on the Type of Operation and Special Event. The null
hypothesis was that there was no difference in delay times
based on type of operation and special event. Levene’s test of
equality of variances was significant (p < .05), indicating a
violation of homogeneity of variance. Due to a significant
Levene’s statistic (p < .05), the significances of the main effects
and interaction should be interpreted with caution.
A 2 (Type of Operation: Scheduled, Non-scheduled) x 2
(Special Event: Super Bowl, Thanksgiving) two-way between-
subjects ANOVA was conducted on delay times. The results
showed a significant main effect of Type of Operation, F(1,
40362) = 6.723, p < .05, partial eta-squared < .001, a
significant main effect for Season, F(1, 40362) =
97.452, p < .05, partial eta-squared = .002, and a significant
Type of Operation x Season interaction, F(1, 40362) = 25.044, p
< .05, partial eta-squared = .001. The null hypothesis was
rejected.
The results of the simple main effects post hoc tests indicated
that the mean delays during Super Bowl for all flights (M =
5.251, SD = 25.894) was significantly higher than the mean for
all flights during Thanksgiving (M = 2.467, SD = 13.946). The
post hoc tests of the main effect of Type of Operation indicated
the mean for Non-scheduled flights (M = 4.571, SD = 33.531)
was significantly higher than the means for Scheduled flights
(M = 3.594, SD = 9.663). Figure 4 shows a significant
interaction between the Type of Operation and Special Event.
Figure 4. Interaction between Type of Operation and Special
Event.
Chapter V
Discussion, Conclusions, and Recommendations
The results from Chapter IV helped the researcher identify
certain trends and noteworthy patterns for scheduled flight
delays between the Super Bowls and Thanksgivings.

Discussions Comment by Jeremy Hodges: Here you discuss
the results from the previous chapter. All you did before his
present the data in the last chapter. Now provide your
discussion on the findings. What didn’t you expect to find that
you did and vice versa.
This is where the results are analyzed, interpreted and
thoroughly discussed. This chapter ties together
findings/results in relation to theory, review of the literature, or
rationale. Why did the results turn out the way they did?
The total number of flight for Super Bowl and Thanksgiving
were similar. However, Super Bowl had almost twice as many
non-scheduled flight as compared to Thanksgiving. About two-
thirds of the total flights were scheduled flights while the
remaining one-third made up the non-scheduled flights. The
lower number of non-scheduled flights during Thanksgiving
may have been a causal factor in the lower average delay when
compared to during the Super Bowl.
Across all the Super Bowls, non-scheduled flights increased
when compared to Thanksgiving. This increase was clear when
comparing all years, except when the event took place in New
York and was affected by a snowstorm. These years also
indicated slight decreases in the total number of scheduled
flights during the Super Bowl related to Thanksgiving.
Analyzing only the scheduled flight delays based on departure
delay, taxi out and taxi in times, and total delay, an increase in
all these times can be observed during Super Bowl over
Thanksgiving. Similarly, comparing the total delay between the
two special events, scheduled flight delays increased
significantly during Super Bowl.
Out of the five venues, only Indianapolis did not have a
significant difference between the Super Bowl and
Thanksgiving for scheduled traffic. Indianapolis had fewer
number of flights operating at the airport and may be the reason
it did not result in a significant difference. Similarly, for non-
scheduled traffic, only Phoenix had a significant difference
between Super Bowl and Thanksgiving. For the non-scheduled

flights at Phoenix, there was a large disparity between Super
Bowl and Thanksgiving. This drastic change in the number of
flights may have been the cause for significant difference
between the amounts of delay experienced.
There exists a significant interaction between the type of
operation and the special event. During Thanksgiving scheduled
flight had significantly lower delays than non-scheduled flights.
However, during Super Bowl, both types of operations yielded
no significant difference in delay statistics.
Conclusions Comment by Jeremy Hodges: The focus of this
section should be on the specific conclusion(s) that can be
drawn from the research. This chapter should contain the
hypothesis evaluation information, and/or answers to the
research question(s). Was the hypothesis supported, non-
supported, inconclusive? Was/were the research question(s)
answered? What is the significance, the impact?
Super Bowl had greater delays overall than Thanksgiving. The
delays for Super Bowl increased mostly when there was an
increase in non-scheduled flights. One reason may be because
non-scheduled flights do not regularly operate to the venues
where the Super Bowl was held; therefore, resulting in an
increase in traffic, which can cause bottlenecks. These
bottlenecks can occur for reasons ranging from saturated
airspace to limited parking spaces.
The increase in non-scheduled flights during Super Bowl is a
trend that can be observed across the five venues. This is most
likely due to fans travelling using private or personal aircraft to
the Super Bowl. These fans have the luxury of arriving before
the start of the event and departing after the game has ended.
The concentrated arrivals and departures may also cause delays
due to the demand meeting or even exceeding capacity for the
airspace and airport.
Thanksgiving provides slightly different characteristics for non-
scheduled travel compared to Super Bowl. The number of non-
scheduled flights arriving and departing during the
Thanksgiving holiday season is spread out over a greater time

period and therefore does not affect delays as drastically.
Furthermore, travel is not concentrated at a specific locale for
the holiday and does not contribute to delays.
Scheduled flights usually operate efficiently as they are
supported by a vast network from the airlines, yielding in fewer
delays. Non-scheduled flights, on the other hand, do not have
dedicated resources and rely on local facilities that result in
slightly greater delays. This trend can be clearly observed in
delays during Thanksgiving for both types of operations.
Conversely, scheduled flights experience greater delays during
the Super Bowl that have no significant difference from non-
scheduled flight even with all the supporting resources.
Therefore, increase in delays can likely be attributed to the
increase in non-scheduled flights operating in greater numbers
during the Super Bowl.
Recommendations Comment by Jeremy Hodges: What should
be done as a result of the research? Should further work be
conducted? Should the study be replicated at a later date with a
different or expanded population? Should new laws and/or
regulations be considered? Should a procedure be changed?
The recommended changes should be based clearly on the
results of the research.
The results of this study provide an awareness to the effects of
non-scheduled flights on scheduled flights during special
events. The delays that are a result of an increase in non-
scheduled flights can add millions of dollars to operating costs
for air carriers and non scheduled aircraft operations. This study
provides insight into an avenue for further research in
mitigating delays during special circumstances in the NAS.
Further research should be conducted to determine which phases
of flight cause bottlenecks and delays to occur during these
special events. Studies specific to airspace, airport, and specific
sectors of air traffic control may provide greater detail on how
non-scheduled flights affect delays. Other special events, such
as the NBAA convention, the Daytona 500, etc., can also be
studied to determine if the effects of non-scheduled flights are

similar. The knowledge gained from these studies may aid
government organizations and air carriers to implement changes
that reduce delays, and in turn, reduce the cost of flying.
Government organizations, such as the FAA, can implement
new technologies that increase airspace capacity in the terminal
area of airports. Air carriers or charter companies could use this
study to optimize flight schedules to ensure aircraft do not
arrive at the airport at the same time and cause delays.
References Comment by Jeremy Hodges: Follow these
examples for references and use your APA manual.
Baik, H., Li, T., & Chintapudi, N. (2010). Estimation of flight
delay costs for U.S. domestic air passengers. Transportation
Research Record, 2177, 49-59. doi:10.3141/2177-07
Bureau of Transportation Statistics. (2009). On-time
performance. Retrieved from
http://www.transtats.bts.gov/HomeDrillChart.asp
Bureau of Transportation Statistics. (2015). Annual U.S.
Domestic Average Itinerary Fare in Current and Constant
Dollars. Retrieved from https://www.rita.dot.
gov/bts/airfares/programs/economics_and_finance/air_travel_pri
ce_index/html/AnnualFares.html
Clare, G., & Richards, A. (2013). Disturbance feedback for
handling uncertainty in air traffic flow management. In Control
Conference (ECC), 2013 European (pp. 3246-3251). IEEE.
Delgado, L., & Prats, X. (2014). Operating cost based cruise
speed reduction for ground delay programs: Effect of scope
length. Transportation Research Part C: Emerging Technologies,
48, 437-452. doi:10.1016/j.trc.2014.09.015
Evans, A., & Schäfer, A. (2011). The impact of airport capacity
constraints on future growth in the US air transportation system.

Journal of Air Transport Management, 17(5), 288-295.
doi:10.1016/j.jairtraman.2011.03.004
Federal Aviation Administration. (2009). FAA aerospace
forecast: Fiscal years 2009-2025. Retrieved from
http://www.faa.gov/data_research/aviation/aerospace_forecasts/
2009-2025
Federal Aviation Administration. (2015). On-time arrival
performance: Airline delay causes raw data. Retrieved from
https://aspm.faa.gov/
Federal Aviation Administration. (2015). The economic impact
of civil aviation on the U.S. economy. Daytona Beach, Florida:
Embry-Riddle Aeronautical University.
Gilbo, E. P. (1997). Optimizing airport capacity utilization in
air traffic flow management subject to constraints at arrival and
departure fixes. IEEE Transactions on Control Systems
Technology, 5(5), 490-503. doi:10.1109/87.623035
Glockner, G. (1996). Effects of air traffic congestion delays
under several flow-management policies. Transportation
Research Record: Journal of the Transportation Research
Board, 1517, 29-36. doi:10.3141/1517-04
Krozel, J., Hoffman, B., Penny, S., & Butler, T. (2003).
Aggregate statistics of the national airspace system. In AIAA
Guidance, Navigation, and Control Conference and Exhibit (p.
5710).
Lubbe, B., & Victor, C. (2012). Flight delays: Towards
measuring the cost to corporations. Journal of Air Transport
Management, 19, 9-12. doi:10.1016/j.jairtraman.2011.11.004

Peterson, E. B., Neels, K., Barczi, N., & Graham, T. (2013).
The economic cost of airline flight delay. Journal of Transport
Economics and Policy, 41, 107-121.
Schumer, C.E., 2008. Flight delays cost passengers, airlines and
the US economy billions, Joint Committee Majority Staff.
Sheth, K., & Gutierrez-Nolasco, S. "Development of miles-in-
trail passback restrictions for air traffic management," 2014
IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC),
Colorado Springs, CO, 2014, pp. 1D3-1-1D3-11.
doi: 10.1109/DASC.2014.6979414
Total Number of Flights
Scheudled Flights 10TG 11SB 11TG 12SB
12TG 13SB 13TG 14SB 14TG 15SB
5005 4746 532 533 669 776 5682 3507 2780 2320
Unscheduled Flights 10TG 11SB 11TG 12SB
1025 2375 241 1871 371 1668 1920 1151 941 2249
Year
Number of Flights
Average Scheduled Flight Delay
Departure Delay 10TG 11SB 11TG 12SB
8.4163509471584987 15.475520067241
7.1913696060037529 7.9530956848029861
6.9211309523809446 9.2956298200514187

10.077504393673131 29.74768713204374
12.149318018664751 31.79507848960543 Taxi Out
13TG 14SB 14TG 15SB
13.067796610169459 15.39314982139105
13.39774859287053 15.495309568480311
12.879464285714301 14.88431876606683
18.475303216734019 22.787042253521069
14.454055994257009 19.21392190152801 Taxi In
13TG 14SB 14TG 15SB
6.4726656025538496 7.0683922558922552
6.75422138836774 6.630393996247653
5.6651785714285596 6.9227799227799247
7.3875505538948456 9.0604519774011276
7.21966977745873 8.6011955593509697 Total Delay
13TG 14SB 14TG 15SB
1.887512487512484 3.3921196797302962
2.66917293233083 2.7692307692307709
0.78475336322870004 5.4020618556701017
2.087293206617395 7.9677787282577706
2.4733812949640361 6.7189655172413856
Year
Minutes of Delay
Average Total Delay
Unscheduled Delay 10TG 11SB 11TG 12SB
3.142682926829266 3.4067789473684211

2.8959751037344361 5.587536076964188
4.5746630727762811 3.2059532374100752
4.2712500000000002 5.5588444830582171
3.5419447396386801 6.9816851934192981
Scheduled Delay 10TG 11SB 11TG 12SB
1.887512487512484 3.3921196797302962
2.66917293233083 2.7692307692307709
0.78475336322870004 5.4020618556701017
2.087293206617395 7.9677787282577706
2.4733812949640361 6.7189655172413856
Years
Minutes of Delay
Type of Operation vs. Special Event
ScheduledSuperBowl Thanksgiving 5.5
2.0499999999999998 Non-Scheduled SuperBowl
Thanksgiving 4.9400000000000004 3.81
Special Event
Average Delay
EXPLORING THE EFFECTIVENESS OF THE MQ-8B FIRE
SCOUT TO PROVISION HUMANITARIAN EFFORTS POST

NATURAL DISASTERS
by
A Graduate Capstone Project Submitted to the College of
Aeronautics,
Department of Graduate Studies, in Partial Fulfillment
of the Requirements for the Degree of
6
EXPLORING THE EFFECTIVENESS OF THE MQ-8B FIRE
SCOUT TO PROVISION HUMANITARIAN EFFORTS POST
NATURAL DISASTERS

by
Graduate Capstone Project:
_________________________________________
March 2020
II
Acknowledgements
I would like to thank those who assisted and guided me
throughout my time in the master’s program.

III
Abstract
Scholar:
Title: Exploring the Effectiveness of the MQ-8B Fire Scout
to provision Humanitarian Efforts Post Natural Disasters
Institution:
Degree: Master of Science in Aeronautics
Year: 2020
This study will explore the increased use of Unmanned Aerial
Vehicles (UAVs) and specifically evaluate the effectiveness of
the Northrop Grumman MQ-8B Fire Scout in providing
humanitarian aid after natural disasters have occurred. The
ability to utilize the MQ-8B will be analyzed by determining its
ability to conduct humanitarian aid missions in areas affected
by natural disasters largely inaccessible using traditional
methods. The study will compare the use of UAVs in
humanitarian aid operations in terms of abilities and costs to the
use of response utility trucks. The viability of using UAVs will
be determined in responding to natural disasters while
simultaneously providing economic benefits. The use of UAVs
will be compared to existing approaches such as emergency
response utility vehicles and manned flight. The study will
develop a model to show the costs and benefits of utilizing MQ-
8B in responding to natural disasters. A quantitative approach
will be used to collect data from existing literature. Information
will be obtained from various sources including the Insurance
Information Institute, Federal Aviation Administration (FAA),
National Center for Biotechnology Information (NCBI),

Occupational Safety and Health Administration (OSHA), and
the Transportation Research Board on UAVs and manned
systems to help come up with a solution to these problems.
IV
Table of Contents
Page
Graduate Capstone Project
Committee……………………………………………………………
………ii
Acknowledgements…………………………………………………
…………………………………….iii
Abstract………………………………………………………………
……………………………………iv
Chapter I 1
Introduction 1
Significance of the Study 2
Statement of the Problem 2
Delimitations 5
Limitations and Assumptions 5
List of Acronyms 5
Chapter II7
Review of the Relevant Literature 7
Origins of UAV and its Applications 7
Cargo Delivery with UAVs 8
Impacts of Weather 9
Operational Flexibility of UAVs 10
UAV legislation and regulation Environment 11
Human Factors 12
Sensing and Processing 13
Mobile Wireless Access Networks 14
Safety of UAVs 14
Aviation Aerospace Safety systems and Unmanned Aerospace
systems 15
Summary 15

References 16
VI
Chapter 1Introduction
Preparation and response to natural disasters is a serious
logistical challenge. Significant resources are used by
intergovernmental, governmental, and non-governmental
organizations to prepare and respond to the effects of natural
disasters. When a natural disaster occurs, such organizations
mobilize their resources to respond. Recently, technological
advancements in autonomous, semiautonomous, and unmanned
vehicles have increased their utility while reducing costs. The
increased use of UAVs has created a new dimension to synthetic
Aperture Radar (SAR) operations. In real life, the use of UAVs
can be beneficial in cases where rapid decisions are required or
the use of manpower is limited (Boehm et al., 2017).
Natural disasters have significantly damaged transportation
infrastructure including railways and roads. In addition, barrier
lakes and landslides pose a serious threat to property and life in
areas affected. When infrastructure is interrupted with, heavy
rescue equipment, rescue vehicles, suppliers and rescue teams
face challenges to reach disaster-hit areas. As a result, efforts to
provide humanitarian aid is hampered (Tatsidou et al., 2019).
The traditional approaches of responding to natural disasters are
unable to meet the requirements to support the process of
disaster decision making. UAVs are well equipped to navigate
areas affected by natural disasters and provide humanitarian aid.
This study aims to explore the viability of using the MQ-
8B fire scout in providing humanitarian aid in areas affected by
natural disasters. The document will also provide a literature
review on the use of UAVs in providing humanitarian aid when
natural disasters have occurred. The study will also compare the
viability of using MQ-8B to MH-60 in conducting rescue
operations in areas affected by disasters. Significance of the
Study
The significance of this study is to discover the

effectiveness of using UAVs in providing humanitarian aid in
areas affected by natural disasters. The study will help in
developing new knowledge and bridge the existing gap in
providing humanitarian aid using UAVs. The findings of this
study will increase knowledge of the effectiveness of UAVs in
responding to natural disasters and provide more insights on
useful ways to respond to affected areas. Ultimately, these
insights could help develop more knowledge about the fate of
UAVs associated with rescue operations. The findings of this
study can be used as the basis for future studies by researchers
interested in this topic.Statement of the Problem
The problem to be addressed in this study is loss of human
life during natural disasters which could possibly be prevented
or reduced through enhanced delivery of humanitarian aid.
According to Luo et al. (2017), the earthquake that hit Haiti in
2010 claimed about 160,000 lives. The 2004 Indian Ocean
tsunami left about 360,000 people dead and more than
1,300,000 others displaced (Luo et al., 2017). While there were
efforts taken to deliver humanitarian aid in both instances, the
use of manned systems proved to be limited to areas that
presented less risk to the rescue teams.
After a natural disaster, governmental and non-
governmental organizations provide significant resources for
rescue and recovery missions. However, the nature of damaged
infrastructure makes it impossible for response vehicles to reach
the affected areas. This demonstrates the inefficiency associated
with traditional methods of providing humanitarian aid in such
situations. As a result, there exists a need for a more robust
approach to providing humanitarian aid after natural disasters to
mitigate the loss of life in the future. The use of UAVs can
augment response teams in providing humanitarian help to
affected areas in a cost-effective and timely manner.
Purpose Statement
The focus of this research will be the ability of the
Northrop Grumman MQ-8B Fire Scout to augment humanitarian
aid operations for mitigating loss of life after natural disasters.

The research will analyze the mishap rates of the MQ-8B
compared to the MH-60, and will look at how the Fire Scout can
be used mutually for military operations, as well its capacity for
provisioning humanitarian aid. Given their available speed and
ability to access high risk places, the MQ-8B Fire Scout can
offer a solution to the existing problem (Gomez & Purdie,
2017). Research Question and Hypothesis
This study aims to answer the following research questions
(RQ):
RQ1: How viable is the deployment of the MQ-8B Fire Scout
for a more expedient and cost-effective solution to delivering
humanitarian aid compared to using the MH-60 Sea Hawk?
RQ2: What are the advantages and disadvantages that could be
associated with the use of the MQ-8B Fire Scout for identifying
victims, water drops for wildfire hotspots, and first aid drops
for survivors post natural disaster?
The following hypothesis (H) has been formulated for the study:
H0: There is no statistical difference in safety when using the
Northrop Grumman MQ-8B Fire Scout when compared to
manned vehicles to provision humanitarian aid in areas affected
by a disaster.
H1: There is a statistical difference in safety when using the
by a disaster. Delimitations
This study will only focus on the potential use of UAVs in
rescue operations to provide humanitarian aid to individuals in
areas affected by natural disasters. As a result, the study will
not provide a description of how the UAVs can be used in
reconnaissance missions mostly conducted by military
personnel. The study will also not describe how UAVs can be
used to monitor riparian areas and pollution in marine areas.
Limitations and Assumptions
One of the major limitations of the research is that there
are significant costs associated with the use of UAVs and more
so with the MQ-8B fire scout. Various UAVs are needed to be

purchased to facilitate this study. However, due to their high
costs, the researchers settled to less efficient UAVs that could
not provide very accurate information. The physical demand of
the terrain, variation in weather conditions, and less optimal use
of machine tools are some of the other factors that affected the
study. These factors have a significant impact on situational
awareness and affect how data is interpreted from UAVs. The
UAVs used in the study had shorter ranges, and therefore, could
not generate a lot of information as expected. Another key
limitation of this study is observer bias that could have
compromised the results.List of Acronyms
FAA- Federal Aviation Administration
H- Hypothesis
RQ- research question
SAR-synthetic Aperture Radar
15
UAVs- Unmanned aerial vehicles
2
Chapter IIReview of the Relevant Literature
Providing humanitarian aid for people affected by natural
disasters has become an issue of major concern not only to
governments but also to other non-governmental organizations.
Destruction of existing infrastructure by natural disasters has
increased public interest in the development of effective tools to
provide humanitarian aid to disaster-hit areas. According to
(Macias, Angeloudis, and Ochieng 2018), unmanned aerial
vehicles are the logical choice for responding to natural
disasters. A review of relevant research will be conducted in
this chapter to determine the underlying knowledge of the
effectiveness of UAVs in providing humanitarian aid. This
review will also delineate factors that may affect the optimum
use of UAVs in areas affected by natural disasters. Origins of
UAV and its Applications

The first UAV was developed in World War I under the
concept of cruise missiles to attack enemies from short
distances. The first UAV was a wooden biplane with a range of
75 miles. This technology focused on attacking a specific
location with zero chance of return. However, by the 1950s, the
United States Air Force was able to develop a UAV capable of
returning after attacking a particular point. During World War
II, American soldiers were able to use UAVs to spy on their
enemies. In the late 1960s, United States Air Force engineers
embarked on developing UAVs with better electrical systems to
observe activities of their enemies with better precision
(Tatsidou et al., 2019).
The significant technological developments since that time
have led to improved UAVs that can take part in more delicate
and complex missions. The use of advanced electronic
controlling systems, better radio systems, high-resolution
digital cameras, sophisticated computers, and advanced global
positing systems (GPS) allow UAVs to conduct recovery
missions effectively during natural disasters. The quality of
UAVs significantly increased in the 2000s. UAVs are now used
by the military but by private firms, and by individual owner
operators. The performance of modern UAVs allows them to
provide humanitarian aid in areas affected by natural
disasters.Cargo Delivery with UAVs
Multiple studies show that UAVs are very effective in
delivering items to areas with poor transportation infrastructure.
From delivering important supplies to monitoring damage by the
use of cameras, UAVs can play a significant role in providing
humanitarian aid. When compared to traditional vehicles, UAVs
are more sophisticated due to their improved flexibility and ease
of use. It is more effective and safer to use a UAV to deliver
supplies in dangerous locations than sending a human being.
However, UAVs are unable to carry an excessively heavy load
because of their size and mostly drop cargo while in route
(D'Amato, Notaro & Mattei, 2018).
UAV designers choose to have them release cargo on air or

land for a receiver to remove the cargo. However, for delivering
humanitarian aid in disaster-hit areas, UAVs are designed to
drop suppliers from the air. Based on the limited lifting
capacity of UAVs, items must be packaged in small containers
(Petrides et al., 2017). Cargo for humanitarian UAVs normally
consists of blood, bandages, syringes, water purifying tablets,
and medicine. Defibrillation attachments may also be included
in the deliverables. These items are light in nature and can be
packaged into small containers to be lifted by the UAVs. This
allows the UAV to travel for long distances without losing its
efficiency.Impacts of Weather
The impact of weather on a UAV depends on the power,
equipment, configuration, and size, as well as the exposure time
and the severity of the weather encountered. Most UAVs have
characteristics and configurations which make them more
vulnerable to extreme weather conditions compared to manned
aircraft. In general, today's UAVs are more fragile, lighter, and
slower, as well as more sensitive to weather conditions when
compared to manned aircraft. Small UAVs are very susceptible
to extreme weather conditions. Similar to manned aircraft,
certain weather conditions can also affect larger UAVs making
them difficult to control.
Extreme weather conditions such as snow, humidity,
temperature extremes, solar storms, rain, turbulence, and wind
may diminish the aerodynamic performance of UAVs, cause loss
of communication, and control. These same conditions can also
negatively affect the operator. Most flight regulations currently
in use do not address most of the weather hazards facing UAVs.
Some of the current restrictions pertaining to weather include
remaining 2000 feet away from ceiling and 500 feet below
clouds, operating under the unaided visual line, and maintaining
visibility for 4.83km (Macias, Angeloudis & Ochieng, 2018).
While this eliminates issues of poor visibility, it does not help
to reduce safety hazards associated with clear skies. Clear sky
hazards may include turbulence, glare, and wind.
Glare occurs in clear skies and may affect visibility in

various ways. First, it hinders the direct observation of the
UAV. On a sunny day, it may also be difficult to spot a UAV in
the sky. As a result, operators must use sunglasses on a sunny
day to be able to carry out their missions effectively. Second,
the operation of UAVs requires a user interface to be displayed
on a tablet, phone, monitor or any other screen to allow the
operator to track the UAV, change control derivatives, or send
commands while receiving telemetry updates. The sun can
overpower the LCD brightness of the screen, which makes it
difficult for the operator to send the correct information to
control the UAV.
Turbulence can also affect the stability of UAVs. Multiple
studies show that wind accounted for more than 50% of manned
aircrafts accidents. This percentage is higher for small aircraft.
This demonstrates the impact turbulence may have on small-
unmanned vehicles. The primary ways wind affects UAVs
includes reducing endurance, limiting control, and changing
flight trajectory. Strong winds affect the path of a UAV. Wind
speeds may also surpass the maximum speed of UAVs causing
them to struggle in such environments. The impact of turbulence
can make it difficult for the UAVs to deliver humanitarian aid
to affected areas in a timely manner.
Turbulence, wind gusts, and wind shear all have the
potential of affecting control of UAVs and will affect an
operator’s ability to complete the mission in the most effective
and expedient manner. UAV control is the ability to maneuver
the UAV by use of roll, pitch, and yaw. Pitch changes the attack
angle for the UAV, roll rotates the UAV, and yaw changes the
direction of the UAV. When the speed of the wind increases
suddenly, it affects the yaw of the UAV making it difficult for
the operator to control it effectively. A horizontal gust can also
roll the UAV and is most dangerous when flying in areas with
obstructions.
Operational Flexibility of UAVs
UAVs have increased persistence in air operations compared to
manned systems making them ideal for conducting humanitarian

aid operations. While there are theoretical and practical limits,
utilizing few vehicles allows for continuous surveillance for a
long period of time. Their flexibility allows them to carry out
operations when and where other manned aircraft are unable to
operate. The long-endurance capabilities of these vehicles allow
them to deliver humanitarian aid many hours into a flight,
which could otherwise be impossible with traditional
approaches. As a result, people in areas experiencing natural
disasters may receive supplies continuously.
While both unmanned and manned air operations can be
coordinated by multiple people, not having a physical operator
in the vehicle allows multiple operators to share direct controls.
The user with the immediate need or situational awareness may
assume full control of the UAV. This capability significantly
reduces the timelines of coordination between the UAV and
ground users. With the dire need associated with response
missions, UAVs are better suited to provide humanitarian aid
when compared to the traditional methods, which normally
takes a significant amount of time to reach those affected.UAV
legislation and regulation Environment
The ability to use UAVs for disaster response in the United
States is largely limited by the Federal Aviation Administration
(FAA). The current FAA policy for operating unmanned aerial
vehicles in the United States requires specific authority to
operate one. In general, any use of UAV requires an
airworthiness certification. However, potential users of UAVs
face significant regulatory challenges in the United States. The
law requires UAVs to include registration numbers in their
markings. Operation circular 91-57 describes the differences
between non-hobby use and hobby use of UAVs and operating
restrictions. The FAA has implemented various orders to restrict
the operation of UAVs.
Local governments have developed legislation that describes the
potential use of UAVs in emergency situations. Various
municipalities including Syracuse, New York, and
Charlottesville, Virginia, have implemented further restrictions

such as city purchases of UAVs. Serious concerns about data
collection and privacy have erupted in the United States. The
FAA developed a restriction for privacy in areas of UAVs
operations. It is clear that, until the private use regulation, and
legislation issues surrounding the adoption of UAVs are not
resolved, it will be difficult to use them in first response
situations. While these challenges exist, researchers need to
explore ways in which UAVs can be used to provide
humanitarian aid during natural disasters. Human Factors
In most cases, designers develop controls that work very well in
labs but fail in a real-world situation. The expectation is,
through training and familiarization, humans will be able to
learn and adapt to the controls and displays. However, this
approach is deemed to fail if used in the development of a
human-machine interface. As the capabilities of UAVs increase
every day, their complexity is also increased. The need to use
automation and advanced technology has also increased. While
these systems are unmanned, it is important to keep in mind that
humans are involved in the control and operation of UAVs
(Hildmann & Kovacs, 2019).
The lack of standardization across different UAV human-
machine interfaces results in an increased time of training for
one system and increased difficulty in transition to other
systems. Poor optimization of information results in the
difficulty of interpreting system information needed for
situational awareness that supports decision making in stressful
situations. Lack of adaptability and flexibility in UAVs often
lead to poor displays and ultimately to poor situational
awareness. Lack of basic sensory cues makes it even more
difficult to use UAVs in response missions. The cues which are
relevant in manned aircraft suddenly become irrelevant in UAVs
(Estrada & Ndoma, 2019). These cues are currently missing in
UAVs and need to be incorporated for increased efficiency.
The development of UAVs that consider the end-user could
increase their effectiveness in responding to natural disasters.
This implies designing human-machine interfaces that are

intuitive, functional, and user-friendly that allow easy
extraction of relevant information by operators. With the
current technological advancements, it is possible to come up
with intuitive and functional interfaces that utilize the available
cues to maintain high levels of situational awareness needed for
effective, efficient, and safe control of UAVs. This will allow
operators to understand various aspects of UAVs and be able to
deploy them in dangerous areas such as locations affected by
natural disasters.Sensing and Processing
The success of providing humanitarian aid to areas
affected by natural disasters requires the equipment to have the
appropriate sensors, and to be at the right place, and at the right
time. This is important particularly in response situations where
emergency signals, remoteness, weather, and terrain differ
significantly. Even if the UAV is at the right place at the right
time, it will be rendered ineffective without the right sensors.
The initial phase of a rescue mission is the most critical and
requires UAVs to have appropriate sensors. A single UAV may
use various sensors that allow it to come up with a general
picture of the situation (Grogan, Pellerin & Gamache, 2018).
Since the strength of signals is inversely proportional to the
square of the distance, unmanned aerial vehicles designed to
provide humanitarian aid in areas experiencing natural disasters
need to have stronger signals than ground station receivers and
satellites. The signal can be triangulated by multiple UAVs if
sent in a digital format. In cases where Emergency Locator
Transmitter (ELT) are not transmitting or activated, infrared
sensors can be used to search the location of the UAV.
Fortunately, sensors in the infrared and low light wavelength
have significantly decreased physical dimensions and costs.
Onboard automation will be very important for effective UAV
operations in extreme conditions.
Mobile Wireless Access Networks
Compared to traditional static sensors, UAVs are still more

costly. Considering that the infrastructure needed to respond to
such cases is currently being met by the existing infrastructure,
it is justified that most studies focus on the immediate aftermath
of a natural disaster. UAVs can be used to develop a
communication center to provide victims in an affected area
with wireless communication. UAVs can also allow people
trapped in areas affected by natural calamities to communicate
with the emergency control center for rescue (Grogan, Pellerin
& Gamache, 2018). One of the benefits of such a system is that
it serves those only in the affected location, and this can
maximize performance. Safety of UAVs
The use of UAVs in rescue operations depends on their
ability to safely operate in the shared aviation environment. As
a result, UAVs must demonstrate they can ensure safety both for
people on the ground and other aircraft. However, there are
various safety risks associated with UAVs which are different
from those presented by manned vehicles. The risk of pilots
losing their lives in flight is reduced because UAVs do not have
occupants. The use of manned vehicles, on the other hand,
implies that people will need to use vehicles to get to areas that
have been affected by natural disasters. As a result, the lives of
the rescue teams are at risk (Estrada & Ndoma, 2019).
UAV designers are aware of the safety concerns associated
with their systems, and more so concerning the poor reliability
of such systems in extreme conditions. They understand
political support and public trust would fade away in case of an
accident. For this reason, safety remains a top priority for the
UAV community. UAVs have the potential to provide
considerable safety benefits in disaster response operations.
Significant technological developments have the potential to
improve safety associated with UAVs. Advances in monitoring
systems, data exchange networks, communication, sensor
detection systems, and automation will have positive impacts on
UAVs and the UAV community. Automated takeoff eliminates
the possibility of accidents for operators (Escribano Macias,
Angeloudis & Ochieng, 2018).

UAVs use the same airspace as other aircraft. As a result, there
are high chances of collision in the airspace. Numerous studies
by research institutions, universities, industry, and governments
across the world have focused on how collisions can be avoided
in the airspace. While avoiding collisions is a difficult task, the
UAV community has developed see and avoid capabilities that
allows them to avoid obstructions. The distance of 25ft for
detecting obstructions has been clearly provided by the FAA
regulations. The FAA calls for operators to maintain vigilance
to detect and avoid collisions with obstructions while flying
UAVs.Aviation Aerospace Safety systems and Unmanned
Aerospace systems
The use of unmanned aerospace systems (UAS) has increased
significantly over the past few years. This has raised significant
safety issues concerning UAS. Different countries have
developed policies to govern the operation of UAS in the
aviation aerospace to enhance safety and security. Various
safety initiatives have been developed, most notably the
commercial Aviation Safety Team (CAST) and European
Strategic Safety Initiative (ESSI). The purpose of CAST is to
reduce the fatality rate associated by commercial aviation by
80%. The ESSI aims to enhance safety for European citizens
through safety analysis and coordination with other global
safety initiatives. Summary
Most of the studies explore the effectiveness of using
UAVs in conducting reconnaissance missions. However, there is
a gap in research focused on the effectiveness of using UAVs to
provide humanitarian aid during and after natural disasters.
There is limited research comparing the effectiveness of using
UAVs to conduct rescue and recovery missions compared to the
use of manned vehicles. There is also limited research focused
on determining the costs and benefits of utilizing emergency
response vehicles and UAVs in responding to natural disasters.
This study will determine the resourcefulness of using
UAVs in responding to natural disasters while simultaneously
providing economic benefits. The study will help develop new

knowledge and bridge the existing gap in providing
humanitarian aid using UAVs. The findings of this study will
increase knowledge on the effectiveness of UAVs in responding
to natural disasters and provide more insights on ways that they
can be used to respond to affected areas. Ultimately, these
insights could help develop more knowledge about the fate
associated with rescue operations. The findings of this study
can be used as the basis for future studies by researchers
interested in this topic.
References
Boehm, D., Chen, A., Chung, N., Malik, R., Model, B., &
Kantesaria, P. (2017). Designing an Unmanned Aerial Vehicle
(UAV) for Humanitarian Aid. Retrieved from
https://pdfs.semanticscholar.org/7c1c/5bf85cd386d2157a44fbbf
2aa9532499c6f3.pdf
D'Amato, E., Notaro, I., & Mattei, M. (2018, June). Distributed
collision avoidance for unmanned aerial vehicles integration in
the civil airspace. In 2018 International Conference on
Unmanned Aircraft Systems (ICUAS) (pp. 94-102). IEEE.
Retrieved from
https://www.mitre.org/sites/default/files/pdf/04_1232.pdf
Escribano Macias, J. J., Angeloudis, P., & Ochieng, W. (2018).
Integrated Trajectory-Location-Routing for Rapid Humanitarian
Deliveries using Unmanned Aerial Vehicles. In 2018 Aviation
Technology, Integration, and Operations Conference (p. 3045).
Retrieved from https://arc.aiaa.org/doi/abs/10.2514/6.2018-3045
Estrada, M. A. R., & Ndoma, A. (2019). The uses of unmanned
aerial vehicles–UAVs- (or drones) in social logistic: Natural

disasters response and humanitarian relief aid. Procedia
Computer Science, 149, 375-383. Retrieved from
https://www.mitre.org/sites/default/files/pdf/04_1232.pdf
Gomez, C., & Purdie, H. (2016). UAV-based photogrammetry
and geo-computing for hazards and disaster risk monitoring–a
review. Geoenvironmental Disasters, 3(1), 23. Retrieved from
https://link.springer.com/article/10.1186/s40677-016-0060-y
Grogan, S., Pellerin, R., & Gamache, M. (2018). The use of
unmanned aerial vehicles and drones in search and rescue
operations–A survey. Proceedings of the PROLOG. Retrieved
from
https://www.researchgate.net/profile/Michel_Gamache/publicati
on/327755534_The_use_of_unmanned_aerial_vehicles_and_dro
nes_in_search_and_rescue_operations_-
Grumman, N. (2015). MQ-8B Fire Scout: Unmanned Air
System. Retrieved from
https://www.northropgrumman.com/air/fire-scout/
Hildmann, H., & Kovacs, E. (2019). Using Unmanned Aerial
Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for
Disaster Response, Civil Security and Public
Safety. Drones, 3(3), 59. Retrieved from
file:///C:/Users/ADMIN/Downloads/drones-03-00059.pdf
Kimchi, G., Buchmueller, D., Green, S. A., Beckman, B. C.,
Isaacs, S., Navot, A., ... & Rault, S. S. J. M. (2017). U.S. Patent
No. 9,573,684. Washington, DC: U.S. Patent and Trademark
Office. Retrieved from
https://patents.google.com/patent/US9573684B2/en
Luo, C., Miao, W., Ullah, H., McClean, S., Parr, G., & Min, G.
(2019). Unmanned aerial vehicles for disaster management.
In Geological Disaster Monitoring Based on Sensor
Networks (pp. 83-107). Springer, Singapore. Retrieved from
https://link.springer.com/chapter/10.1007/978-981-13-0992-2_7
Macias, J. J. E., Angeloudis, P., & Ochieng, W. (2018).
Integrated Trajectory-Location-Routing for Rapid Humanitarian
Deliveries using Unmanned Aerial Vehicles. Retrieved from
http://www.optimization-online.org/DB_FILE/2018/12/6980.pdf

Petrides, P., Kolios, P., Kyrkou, C., Theocharides, T., &
Panayiotou, C. (2017). Disaster prevention and emergency
response using unmanned aerial systems. In Smart Cities in the
Mediterranean (pp. 379-403). Springer, Cham. Retrieved from
https://link.springer.com/chapter/10.1007/978-3-319-54558-
5_18
Tatsidou, E., Tsiamis, C., Karamagioli, E., Boudouris, G.,
Pikoulis, A., Kakalou, E., & Pikoulis, E. (2019). Reflecting
upon the humanitarian use of unmanned aerial vehicles
(drones). Swiss Medical Weekly, 149(1314). Retrieved from
https://smw.ch/article/doi/smw.2019.20065/
Valavanis, K. P., & Vachtsevanos, G. J. (Eds.).
(2015). Handbook of unmanned aerial vehicles (Vol. 1).
Dordrecht: Springer Netherlands. Retrieved from
https://link.springer.com/978-90-481-9707-1
Present Your Data and Analysis
WRITER: DO NOT ADD ANYTHING THE TO “LITERATURE
REVIEW MQ8B1” PAPER!!!!!!!
In week 4's submission, you are going to cover your Chapter III
Methodology (6 pages).
Also, you need to demonstrate in Chapter IV Results of your
data collection, analysis, statistical test, charts, and conclusion
on your hypothesis. (9 pages).
Finally, you need to cover your Chapter V Discussions,
Conclusions and Recommendations of your project (5 pages).
A template will be uploaded for reference, you need to follow
the template how its formatted. Continue on Chapter III of the

template!
Purpose Statement
The focus of this research will be the ability of the
Northrop Grumman MQ-8B Fire Scout to augment humanitarian
aid operations for mitigating loss of life after natural disasters.
The research will analyze the mishap rates of the MQ-8B
compared to the MH-60, and will look at how the Fire Scout can
be used mutually for military operations, as well its capacity for
provisioning humanitarian aid. As such, the analysis will
evaluate which between manned aircraft and UAVs are the most
effective in timely provision of humanitarian aid during natural
disasters as a means of preventing loss of human lives. Given
their available speed and ability to access high risk places, the
MQ-8B Fire Scout can offer a solution to the existing problem
(Gomez & Purdie, 2017).
Research Question and Hypothesis
This study aims to answer the following research questions
(RQ):
RQ1: How viable is the deployment of the MQ-8B Fire Scout
for a more expedient and cost-effective solution to delivering
humanitarian aid compared to using the MH-60 Sea Hawk?
RQ2: What are the advantages and disadvantages that could be
associated with the use of the MQ-8B Fire Scout for identifying
victims, water drops for wildfire hotspots, and first aid drops
for survivors post natural disaster?
The following hypothesis (H) has been formulated for the study:
H0: There is no statistical difference in safety when using the
by a disaster.
H1: There is a statistical difference in safety when using the
by a disaster.

Methodology from your proposal
In the proposed study, quantitative research methods will be
used to analyze existing data on the usability of UAVs in
disaster-stricken areas. A mathematical model will be developed
to replicate mishaps which may occur during the humanitarian
aid using MQ-8B fire scout and the MH-60 Sea hawk. This
method will provide objective, data-based evidence regarding
the prospects of employing UAVs in disaster-stricken areas.
(Gomez & Purdie, 2017). A qualitative study would be
insufficient to satisfy the hypothesis given its subjectivity and
inability to sufficiently answer the research questions outlined
above.
Developing the model
Data provided by a model created by Choudhury et al. (2017)
will be used in this study. The model replicates the mishap rates
to evaluate the effectiveness of the use of the MQ-8B compared
to the MH-60 in disaster-stricken areas, several guidelines will
be followed. The logistics network is expressed in the form of
smooth continuous functions. The logistics network is
represented in a two-dimensional space with demand points
represented by discrete points within the service area in the
two-dimensional space (Gomez & Purdie, 2017). The demand
for humanitarian aid in the demand points will be modelled as
Poisson processes. Using the model created by Gomez and
Purdie, different scenarios will be simulated to obtain the said
data.
The model will incorporate the rates of mishaps for both the
MQ-8B and the MH-60 in different types of disasters and
landscapes. More specifically, the two modes of delivery will be
compared in terms of mishap rates in mountainous landscapes,
shrub lands, coasts and wetlands. The rates are encountered in
such disasters as hurricanes, tsunamis, fires and earthquakes
will also be integrated in the model to ensure it depicts the real-
world mishaps rates.

Such factors as speed and ability to access the disaster-stricken
areas will be incorporated into the model. UAVs are faster and
have the ability to be deployed in areas that may be inaccessible
to disaster response utility trucks.
The model, therefore, will be used to compare the rates of
mishaps in a simulated scenario. The viability of the use of
UAVs in provision will be evaluated by using this method. The
rates of mishaps for MQ-8B Fire Scout will be compared with
those of the MH-60. A t-test will be carried out on the
independent means of mishap rates for both aircraft systems. If
the P-value that will be obtained during the hypothesis will be
less than the chosen alpha value, the null hypothesis (H0) will
be rejected. If the P-value is greater than the chosen alpha
value, then the null hypothesis will be upheld.
Additional Notes:
Collecting and analyzing your data is critical to the successful
completion of your capstone project.
Your approved proposal defined "how" you would collect and
analyze your data, but often times, the process of collecting and
analyzing data comes with challenges.
These challenges are generally not insurmountable, as long as
they are identified early in the process.
Collecting and analyzing your data early in the capstone process
ensures the data is available, that it is valid, and that it is
reliable.
Although a formal and complete analysis is not critical at this
point, you need to make sure you have the data needed to make
informed analysis and decisions.
Submit the data you have identified to your instructor. If you
have already performed a statistical analysis, submit your
analysis as well. Your instructor will respond using the
Document Viewer in the Grades area with comments and any
necessary guidance.

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