Continue on the Literature review word file. Also, i attached the instructions.

Chapter III Methodology – 6 pages

Chapter IV Results of your data collection, analysis, statistical test, charts, and conclusion on your hypothesis. (9 pages).

Chapter V Discussions, Conclusions and Recommendations of your project (5 pages).

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 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 Northrop Grumman MQ-8B Fire Scout when compared to manned vehicles to provision humanitarian aid in areas affected 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.

EXPLORING THE EFFECT

IV

ENESS OF THE MQ-8B FIRE SCOUT TO PRO

VI

SION 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

Master of Science in Aeronautics

6

EXPLORING THE EFFECTIVENESS OF THE MQ-8B FIRE SCOUT TO PROVISION HUMANITARIAN EFFORTS POST NATURAL DISASTERS
by

Graduate Capstone Project:

_________________________________________

March

2

020

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 II 7

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 1

Introduction

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 Northrop Grumman MQ-8B Fire Scout when compared to manned vehicles to provision humanitarian aid in areas affected 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 II

Review 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/5bf85cd386d2157a44fbbf2aa9532499c6f3

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

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

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/publication/327755534_The_use_of_unmanned_aerial_vehicles_and_drones_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

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

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

2

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

Master of Science in Aeronautics

Graduate Capstone Project:

________________

___________________________

XXXXXXXX. Comment by Jeremy Hodges: Jeremy Hodges, PhD

Graduate Capstone Project Chair

________________

xx

ii

Date

xxii

ii
xxii

xxii

ix

Acknowledgements 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

Figure

s 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 Event

s 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.

The null hypothesis was that there is no difference in total delay 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)

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 = 7.968, SD = 18.007) was longer than the mean total delay for Thanksgiving (M = 2.087, SD = 5.703). An independent samples t-test was significant, t(3944.278) = 18.766, p < .001. Therefore, the null hypothesis was rejected.

The null hypothesis was that there is no difference in total delay 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 = 5.559, SD = 29.981) 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)

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 = 5.402, SD = 11.052) was longer than the mean total delay for Thanksgiving (M = 0.785, SD = 2.794). An independent samples t-test was significant, t(888.535) = 11.228, p < .001. Therefore, the null hypothesis was rejected.

The null hypothesis was that there is no difference in total delay 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 not significant (p < .05) and no adjustments were made to degrees of freedom. The mean total delay for Super Bowl (M = 3.206, SD = 25.948) 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 hypothesis was retained.

Super Bowl Delays and Thanksgiving Delays Indianapolis (2011 – 2012)

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 not significant (p < .05) and no adjustment was made to degrees of freedom. The mean total delay for Super Bowl (M = 2.769, SD = 6.453) was longer than the mean total delay for Thanksgiving (M = 2.669, SD = 7.039). An independent samples t-test was not significant, t(1063) = 0.242, p > .05. Therefore, the null hypothesis was retained.

The null hypothesis was that there is no difference in total delay 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 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 hypothesis was retained.

Super Bowl Delays and Thanksgiving Delays Dallas (2010 – 2011)

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 = 3.392, SD = 7.493) was longer than the mean total delay for Thanksgiving (M = 1.888, SD = 5.002). An independent samples t-test was significant, t(8212.155) = 11.597, p < .001. Therefore, the null hypothesis was rejected.

The null hypothesis was that there is no difference in total delay 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 not significant (p < .05) and no adjustment was made to degrees of freedom. The mean total delay for Super Bowl (M = 3.407, SD = 24.677) 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

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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_price_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

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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/

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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

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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 12TG 13SB 13TG 14SB 14TG 15SB 1025 2375 241 1871 371 1668 1920 1151 941 2249

Year

Number of Flights

Average Scheduled Flight Delay

Departure Delay 10TG 11SB 11TG 12SB 12TG 13SB 13TG 14SB 14TG 15SB 8.4163509471584987 15.475520067241 7.1913696060037529 7.9530956848029861 6.9211309523809446 9.2956298200514187 10.077504393673131 29.74768713204374 12.149318018664751 31.79507848960543 Taxi Out 10TG 11SB 11TG 12SB 12TG 13SB 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 10TG 11SB 11TG 12SB 12TG 13SB 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 10TG 11SB 11TG 12SB 12TG 13SB 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 12TG 13SB 13TG 14SB 14TG 15SB 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 12TG 13SB 13TG 14SB 14TG 15SB 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

Scheduled SuperBowl Thanksgiving 5.5 2.0499999999999998 Non-Scheduled SuperBowl Thanksgiving 4.9400000000000004 3.81

Special Event

Average Delay