Evaluating the Behavior of General Aviation Aircraft and Design of General Aviation Runways Towards Mitigating Runway Excursions

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Evaluating the Behavior of General Aviation Aircraft and Design of General Aviation Runways towards Mitigating Runway Excursions

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Eunsun Ryu

Graduate Program in Civil Engineering

The Ohio State University

2017

Master's Examination Committee:

Seth Young, Advisor

Mark McCord

Philip Smith

Copyrighted by

Eunsun Ryu

2017

Abstract

A runway excursion is an event whereby an aircraft has strayed from a declared runway during takeoff or landing. Runway excursions are the most frequent of all runway related accidents. Aviation authorities such as the Federal Aviation Administration

(FAA) recommend runway design standards with additional safety areas to protect an aircraft in the case of a runway excursion. Despite their precautions, runway excursions remain a significant issue in the aviation industry. Previous research on the development of runway excursion risk models focused primarily on commercial aviation with relatively little attention paid to general aviation (GA).

In this research, to further understand the characteristics of general aviation runway excursions, various statistical analyses were conducted on several years of runway excursion accident and incident data from publicly available sources including

FAA, National Aeronautics and Space Administration (NASA), and the National

Transportation Safety Board (NTSB). The analysis included the determination of potential differences in runway excursion rates between commercial and general aviation operations, operations at towered vs. non-towered GA airports, varying weather conditions and runway dimensions. In some instances, where data such as national totals of general aviation operations and flight hours was not available, estimation models were developed based on the known data for the State of Ohio.

The results of the analysis performed found that: the risk of general aviation excursions is significantly higher than for commercial aviation. In addition, general aviation operations were revealed to have a higher risk of excursions in good visibility and ceiling “visual” meteorological conditions (VMC) rather than poor “instrument” meteorological conditions (IMC), which is a different result from that for commercial operations. That is, visibility and ceiling may be a less important factor for general aviation excursion than considered in previous models for commercial excursions since

GA pilots tend to less fly under the IMC condition and less trained for bad weather condition.

Furthermore, runway dimension and the presence of an air traffic control towers were also found to be factors. In general aviation, the non-towered GA excursion risk is higher than that for airports with air traffic control towers. Airports with smaller runways are also found to have a higher risk of runway excursions than airports with larger runways.

In order to gain further insights into what may cause general aviation runway excursions, this research also included an empirical study performed to investigate how much general aviation aircraft deviate from a runway centerline upon landing. This research was performed by analyzing aircraft trajectory data collected through LiDAR sensor systems on a runway at The Ohio State University Airport. Collected trajectories from 18 landing aircraft were analyzed based on their longitudinal and vertical distance change. Initial findings from this analysis revealed that aircraft tend to oscillate around the centerline in the early stage of the landing roll, immediately after touchdown. Much

iii of the lateral movement is thought to be the result of pilots attempting to correct their trajectory to get as close to, and stay on, centerline. As an aircraft moves toward the runway end, pilots had less correction in their lateral direction, as they either got close to centerline, or maintained a consistent distance away from the centerline. In addition, it was found that when aircraft land farther away laterally from a runway centerline, the aircraft had more deviation than aircraft that touch down closer to the centerline. The findings from this study may guide research into the relation between failing to properly correct deviation from centerline during touchdown and runway excursions.

Acknowledgments

One of the great fortune in my life is that I could have Dr. Seth Young as my academic advisor. I could not finish this thesis without his valuable guidance and comments. I would never forget two years of research experience with Dr. Young’s mentorship. Valuable support from Dr. Mark McCord and Dr. Philip Smith was another navigation for this study and conception for future research.

I should not leave behind huge support and encouragement from my parents and brother. This great chance to study in the United States was from my family’s trust and dedication. My friends in Columbus and Korea, who are another family, allowed me to overcome difficulties in a new country.

Lastly, the experience with the FAA PEGASAS program provided me invaluable opportunities to expand my perspective not only as a researcher but also a student who is involved in aviation industry. I am grateful that I could have great experience at the Ohio

State University.

Vita

February 2008 ...... Sawoo High

2014 ...... B.S. Air Transportation, Hanseo University

2014 to present ...... Graduate Research Assistant, Department of

Civil, Environmental and Geodetic

Engineering, The Ohio State University

Fields of Study

Major Field: Civil Engineering

Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vi

Chapter 1: Introduction ...... 1

Chapter 2: Analysis of Runway Excursion Incidents and Accidents ...... 38

Chapter 3. Empirical Study of Runway Centerline Deviation ...... 71

Chapter 4. Conclusion ...... 93

References ...... 100

Appendix A: Glossary...... 104

Appendix B: An Example of the Application of Runway Length Corrections ...... 107

Appendix C: Combined Accident and Incident Dataset ...... 109

Appendix D: List of Non-towered General Aviation Airports in Ohio [34] ...... 128

Appendix E: Non-towered Airport Operation Estimation based on 2014 Ohio Flown

Hour [35] ...... 133

Appendix F: The Longest Runways at Airports in Ohio [33], [34] ...... 136

Appendix G: Calculation of VMC and IMC operations based on flown hours ...... 142 vii

List of Tables

Table 1. Percentage Difference of Each Type of Runway Excursion [12] ...... 7

Table 2. Aircraft Approach Category (AAC) of FAA [18] ...... 11

Table 3. Aircraft Design Group (ADG) of FAA [18] ...... 11

Table 4. Aeroplane Reference Field Length of ICAO [19] ...... 12

Table 5. Standard for Greatest Main Gear Span and Wingspan of ICAO [19] ...... 12

Table 6. Comparison of Airport Design Group of FAA and ICAO ...... 13

Table 7. Airplane Weight Categorization for Runway Length Requirements [23] ...... 13

Table 8. A Runway Width Minimum based on the RDC by FAA [18] ...... 17

Table 9. Runway Width Recommendation based on the Aeroplane Code Letter and

Number by ICAO [25] ...... 18

Table 10. A Runway Shoulder Width Minimum based on the RDC by FAA [18] ...... 19

Table 11. Runway Safety Area Dimension Requirements by FAA [18] ...... 20

Table 12. Obstacle Free Zone Dimension Requirements by FAA [18] ...... 21

Table 13. Runway Obstacle Free Area Dimension Requirements by FAA [18] ...... 21

Table 14. Runway Protection Zone Dimension Requirements by FAA [18] ...... 23

Table 15. Required Runway Strip Dimension Not Allowing Fixed Objects [25] ...... 24

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Table 16. Required Runway Strip Dimension Not Allowing Mobile Objects during

Takeoff and Landing [25] ...... 25

Table 17. Prepared Area Dimension for Differences in Loading Bearing Capacity [25] . 25

Table 18. RESA Minimum Dimension based on the Aircraft Code Number [25] ...... 26

Table 19. RESA Recommended Length based on the Aircraft Code Number [25] ...... 26

Table 20. Example Aircraft Dimension Categories ...... 27

Table 21. Independent Variables used for Veer-off Model [29] ...... 32

Table 22. Collected Type of Information for a Single Runway Excursion Event ...... 49

Table 23. Operations and Excursions in the United States from 1990 to 2014 [32] ...... 54

Table 24. Operations and Excursions in the United States from 2010 to 2014 [32] ...... 55

Table 25. Commercial and General Aviation Excursion Rate Comparison in Towered

Airports in the United States (2010-2014) ...... 56

Table 26. Variables and Values for Analysis of General Aviation and Commercial

Aviation Runway Excursion Rate Difference...... 57

Table 27. General Aviation Excursion Probabilities Comparison in Towered and Non- towered airports in the United States [32] [34] [35] ...... 60

Table 28. Variables and Values for Analysis of Towered and Non-towered GA Airports

Runway Excursion Rate Difference ...... 60

Table 29. VMC and IMC Operation Estimation in 2014 [36] ...... 63

Table 30. Number of Operations and Runway Excursions in VMC and IMC ...... 64

Table 31. Runway Excursion Probabilities in GA VMC and IMC Operations ...... 64

Table 32. Variables and Values for Analysis of GA VMC and IMC Runway Excursion

Rate Difference ...... 64

Table 33. Runway Length and Width Categories ...... 66

Table 34. Runway Dimension and Runway Excursion Distribution in Ohio from 2010 to

2014 [34] [35] ...... 67

Table 35. Rate of Excursion per Million Operations for Each Group of Runway

Dimension ...... 67

Table 36. Variables and Values for Analysis of Runway Excursion Rate Difference in the

Smallest Runway Group and the General Excursion Rate ...... 68

Table 37. Aircraft Average, Standard Deviation and Slope of Deviations ...... 83

Table 38. Average, Standard Deviation and Average Slope of Absolute Deviation ...... 86

Table 39. Deviation by Horizontal Distance Sections ...... 87

List of Figures

Figure 1. Percentage of Runway Related Accidents and Percentage of Runway Related

Accident Fatalities to the Entire Accidents through 2015 [5]...... 4

Figure 2. 2009-2013 Breakdown per accident categories [8] ...... 5

Figure 3. Fatalities by type of runway accident [10] ...... 5

Figure 4. Fatal & non-fatal runway accidents by type [10] ...... 6

Figure 5. Exterior of C172S [14] ...... 9

Figure 6. Exterior of ERJ-145 [15] ...... 10

Figure 7. Exterior of Boeing 737-800 [17] ...... 10

Figure 8. Runway Length Requirement for Smaller Airplanes with Fewer than 10

Passenger Seats (Figure 2-1 of AC 150-5325-4B) [23] ...... 15

Figure 9. Runway Length Requirement for Airplanes with 12,500lbs < MTOW ≤

60,000lbs [23] ...... 16

Figure 10. Runway Protection Zone (RPZ), Runway Obstacle Free Area (ROFA) and

Runway Safety Area (RSA) [18] ...... 23

Figure 11. Required Runway Components by FAA (Not to scale) [18] ...... 26

Figure 12. An Example RESA of a Runway for Code 3 and 4 Aircraft [25] ...... 27

Figure 13. Use of runway edge to measure lateral distances [28] ...... 35

Figure 14. Subsections of the RSA – Example for normalization with runway distance available [28] ...... 35

Figure 15. Risk contour of adjusted probability to exceed a given lateral distance [28] .. 36

Figure 16. NTSB Aviation Accident Database & Synopses [29] (Source: http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx) ...... 40

Figure 17. An Example of Factual Report from NTSB Database [29] (Source: http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx) ...... 41

Figure 18. A Probable Cause Example Report from NTSB Database [29] (Source: http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx) ...... 42

Figure 19. Aviation Safety Reporting System (ASRS) by NASA [30] (Source: https://asrs.arc.nasa.gov/search/database.html) ...... 43

Figure 20. An example of ASRS result page [30] (Source: https://asrs.arc.nasa.gov/search/database.html) ...... 44

Figure 21. FAA Accident and Incident Data System (AIDS) [31] (Source: http://www.asias.faa.gov/pls/apex/f?p=100:12:0::NO:::) ...... 45

Figure 22. An example of FAA AIDS result [31] (Source: http://www.asias.faa.gov/pls/apex/f?p=100:12:0::NO:::) ...... 46

Figure 23. Percentage Distribution of Runway Excursion Events from Each Database .. 47

Figure 24. Example of collected data on Excel sheet with NTSB data ...... 48

Figure 25. Runway Excursion Rates per Million Operations ...... 55

Figure 26. Runway Excursion Causal Factor in Commercial Landing [37]...... 62

Figure 27. The Ohio State University Airport [39]...... 72

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Figure 28. A Single VLP-16 LiDAR Sensor Unit [40] ...... 73

Figure 29. LiDAR Sensors Mounted on Runway Edge Light Frames [41]...... 74

Figure 30. Scanned Range of Each Sensor (Not to scale) [41] ...... 75

Figure 31. Sensor System Configuration [42] ...... 75

Figure 32. Installed Sensor System Detecting a Landing Aircraft ...... 76

Figure 33. Data collection location [39] ...... 77

Figure 34. An example of raw point cloud from the sensor [43] ...... 78

Figure 35. Refined example aircraft – A small propeller aircraft [44] ...... 79

Figure 36. Refined example aircraft – A jet engine aircraft [44] ...... 79

Figure 37. Refined and combined aircraft image from four scanners [41] ...... 79

Figure 38. Detection Range by LiDAR Sensors ...... 80

Figure 39. Combined Aircraft Trajectories ...... 81

Figure 40. Trajectories of Aircraft 01, Aircraft 03 and Aircraft 09 ...... 84

Figure 41. Average and Standard Deviation of Absolute Deviation ...... 85

Figure 42. Distance Sections of LiDAR Detection Range ...... 87

Figure 43. Deviation Distribution along the Runway Horizontal Distance ...... 88

Figure 44. Average of Absolute Slope ...... 90

Figure 45. Velocity change of landing aircraft with respect to horizontal, lateral and vertical movement [45] ...... 91

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Chapter 1: Introduction

This thesis reports on research intended to investigate the causes of runway excursions on general aviation runways, and how airport design standards may be modified to accommodate the movement of general aviation (GA) aircraft effectively without risking safety.

As the demand for air transportation has increased, the number of air traffic operations at airports of all sizes has soared. Over the last 20 years, more, larger, and faster aircraft have begun to operate not only in large commercial airports, but also on smaller runways used primarily for GA. Despite these advances, the standard of airport design including runway design has never been changed or evaluated to adapt to these new operations. The purpose of this research is in part to evaluate the design safety of existing runway standards, in the anticipation of more, larger, and faster aircraft utilizing runways in the future.

In this chapter, the importance of runway design in mitigating runway excursions will be discussed. As the United States and the International Civil Aviation

Organization (ICAO) requires and recommends different standards for airport design, characteristic differences will be compared regarding essential runway design components, such as runway length, width, and other safety areas to consider runway design. Previous studies for runway excursion frequency estimation for commercial airports were reviewed to confirm whether they are proper for general aviation airports.

In Chapter 2, actual accident information found in publicly accessible data sources will be used to analyze common causal factors of runway excursions.

Collected hundreds of accident data were used to understand conditions that easily causes runway excursions.

The Chapter 3 reports about deviation of aircraft from runway centerline research. For this experimental research, four LiDAR sensors were installed along a runway edge at The Ohio State University Airport. Collected point clouds of aircraft helped to understand the exact location of landing gear on a runway during takeoff and landing. Based on the analysis result, it will be able to understand where along a runway excursion may occur and a common aircraft type or runway environment that easily cause deviations.

Finally, based on the causal factor analysis and deviation data, the fact that significant factors that cause runway excursions in general aviation are different from the common factors in commercial flight motivate future research.

1.1. Why runway design is important

According to the Aeronautical Information Manual (AIM) published by the

Federal Aviation Administration (FAA), an airport is “an area of land or water that is used or intended to be used for the takeoff and landing of aircraft, and includes its buildings and facilities, if any. [1]”. Similarly, the International Civil Aviation

Organization (ICAO), an international organization that provides guidance to nations worldwide on the design and management of their airports and aviation systems, defines a runway as “a rectangular area on a land aerodrome prepared for the landing and take-off of an aircraft. [2]” Even though the ICAO definitions specified a “land

2 area”, the two descriptions show that the most essential function of an airport is an offer of safe place for maneuvering an aircraft, especially for takeoff and landing.

Airplanes can fly by getting lift on the surface of wing and that lift comes from air flow. To generate enough air flow for lift, airplanes need to reach a certain speed on a runway. This requires a runway to be a long rectangular into one direction. Runway direction depends on the wind direction of the airport site and the length and width are determined by the critical aircraft. A properly designed runway is able to provide a sufficient space and also help protect against accidents and incidents during takeoff and landing.

In United States during 2015, there were 49,726,308 operations of aircraft [3], including civil, military and general aviation flights. Each operation is defined as a takeoff and landing on a runway. Although the takeoff and landing stages takes 4% of the entire flight time in average, as illustrated in Figure 1, the fact that 52% of accidents happen during those steps makes gives us an obvious reason to focus on runway design. [4]

From 2000 through 2015, the entire number of accidents on the National

Transportation Safety Board (NTSB) accident database was 30,176 cases. Among more than thirty thousand accidents, runway related accidents are 15,613 accidents, which is about 52% of the entire accidents during the same period. In these accidents, the number of fatal accidents is 6,395 and 2,640 cases (41.28% of the entire fatal accidents) were related to the runway. [5] This means 10% of accidents are runway related and causes loss of lives.

100% 80% Runway Non- Runway fatality runway related 60% 48% 52% Entire 40% accidents 20% 0%

Figure 1. Percentage of Runway Related Accidents and Percentage of Runway Related Accident Fatalities to the Entire Accidents through 2015 [5]

This is why a runway design should aim reducing the number of accidents. All aircraft must use a runway to be airborne and runway need to protect them from the danger of accidents.

1.2. Why should we focus on mitigating runway excursions?

According to the FAA Order 7050-1B, Runway Safety Program, a runway excursion is defined as “when a fuselage or wings are deviate from the runway, it is regarded as a runway excursion.” [6] ICAO takes similar definition of runway excursion; a veer off or overrun off the runway surface. [7] As illustrated in Figure 2, the International Air Transportation Association (IATA) states that runway excursions are the most prolific of all runway accidents. According to the Safety Data 2009-2013 published by IATA [8], there were 432 total commercial accidents between 2009 and

2013. 98 accidents were runway or taxiway excursions and 7 accidents caused 191 deaths of casualties. These 98 accidents take 23%, which is the largest portion of the accidents.

Accident categories

Runway/Taxiway Excursion Gear-up Landing/Gear 6% 23% Collapse Ground Damage 7% Loss of Control In-flight 8% Hard Landing 17% In-flight Damage 8% Controlled Flight Into 10% Terrain (CFIT) 13% Tailstrike Undershoot

Figure 2. 2009-2013 Breakdown per accident categories [8]

The Flight Safety Foundation also agrees to this opinion. The organization figured out that the runway excursions occur about 40 times more than runway incursions and

100 times more likely to happen than runway confusion accidents. [9]

A study referenced in the Runway Safety Manual published by the International

Federal of Air Line Pilots’ Associations (IFALPA) [10], also shows the obvious reason to reduce excursions with following Figure 3 and Figure 4.

Figure 3. Fatalities by type of runway accident [10]

Figure 4. Fatal & non-fatal runway accidents by type [10]

Compared to runway incursions and confusion accidents, runway excursions happen about 7 times more than the other two categories. The number of fatalities shows a huge difference; casualties of runway incursions and confusions are less than

10. On the other hand, runway excursions result about 350 deaths. Moreover, a steep soaring was observed since 2006 that causes a huge increase in runway excursion accidents. It supports the reason that we need to focus on excursions.

Not only the matter of human life, runway excursions draw significant loss of properties. The Honeywell Aerospace estimated that there are more than $900 million in economic cost as a result of runway excursions. [11] This tremendous loss includes the direct cost of damaged property (such as aircraft itself, airport facilities or other infrastructure around the airport and runway), loss of aircrew licensees, liabilities and compensation for injured passengers and crews. There are also other indirect fees to settle the situation after an accident. Tens or hundreds of flights should be delayed or cancelled and to replace the entangled schedules, another hundreds of aircraft need to reassigned. This aftereffect again causes other unseen expenditure.

1.3. Types of Runway Excursions

Runway excursions may be categorized as either “overruns” or “veer-offs”.

When an aircraft cannot stop before it gets to the end of the runway, it is called overrun. On the other hand, a veer-off occurs when a pilot loses directional control for whatever reason and veers off to the left or right side of the runway. [7] Overruns and veer-offs may occur either during landing or during aborted takeoffs. Thus, runway excursions can be categorized into four categories; takeoff overruns, takeoff veer-offs, landing overruns and landing veer-offs. A report from NLR Air Transport Safety

Institute compares the percentage differences among each different types of excursions, based on the region differences. [12] Following Table 1 shows the differences.

Region Excursion type World Europe Landing Veeroffs 39.8% 35.8% Landing Overruns 37.1% 41.8% Takeoff Veeroffs 12.4% 9.8% Takeoff Overruns 10.7% 12.6% Phase type Landing 76.9% 77.6% Takeoff 23.1% 22.4% Excursion type Overruns 47.8% 54.4% Veeroffs 52.2% 45.6% Table 1. Percentage Difference of Each Type of Runway Excursion [12]

As the Table 1 shows, about 80% of all runway excursions occurred during landing. The Flight Safety Foundation says that the trend of takeoff excursion is decreasing, however, landing excursions shows opposite tendency. Therefore, runway excursions during landing will be the focus of this research.

1.4 Comparing U.S. and International Runway Design Standards

ICAO takes a role of international administrator managing accord and conservation of safety, security and efficiency of 191 Member States (2016). [13]

ICAO publishes several annexes and documents to recommend the least required level of safety, including aerodrome design. Member States follow those standards or create their own regulation based on the ICAO’s specification. The United States is one of the counties that takes the later policy. The aviation industry in the United

States is governed by FAA and several standards are published. To be eligible for financial support from FAA, airports in the USA must follow the standards and requirements by FAA. As FAA developed their own design system regarding airport design, there are some discrepancy between FAA and ICAO standards. In this section, such design components similarities and differences in runway and related safety area design are compared.

The first step in design an airport runway is determining a design aircraft.

Airport operators need to choose a design aircraft that becomes reference for airport facility design. All of the components in an airfield are intended to protect an aircraft during flight operations but aircraft dimensions and specifications vary in thousand different cases. Therefore, airport planning should apply the characteristics of an aircraft that actually and most frequently uses the facility under the circumstance of the airport location. Sometimes an airport has various design or critical aircraft for different design components. For example, the largest aircraft should be the design aircraft for runway width planning since the width must be wider than the aircraft

8 wingspan. On the other hand, an aircraft with the smallest wingspan and slowest approach speed becomes a reference for runway orientation determination. To provide the best wind environment, airports must choose the runway direction that covers

95% of the wind direction and speed while the lightest aircraft is the criteria.

A runway, taxiway, gate and apron specifications are determined based on dimensions of this design aircraft, which is most frequently used airplane in the airport with critical dimensions (largest wingspan, fastest, most sensitive to winds, etc.). Therefore, it can be considered that categorization and standards of each aircraft category determine the dimension of airport components. However, the two largest organizations in world aviation industry, FAA and ICAO apply different standards for airport design, especially for runway and taxiway design. To show the differences and similarities between FAA and ICAO design, three aircraft became examples in this section. One of the most common training and leisure aircraft, the Cessna 172

Skyhawk a single engine aircraft was selected as a representative of small general aviation aircraft. Figure 5 is the exterior of the C172S.

Figure 5. Exterior of C172S [14]

The ERJ-145 by Brazilian aircraft manufacturer Embraer was used as a reference. As the two jet engine aircraft is able to carry up to 50 people, it is popular for regional airlines. Figure 6 shows the external appearance of ERJ-145.

Figure 6. Exterior of ERJ-145 [15]

Lastly, another prominent aircraft, Boeing 737 was included as an example.

The aircraft has been designed as short to medium range [16] flight with maximum capacity of 220 passengers and 1,835 ft3 of cargo capacity. Internationally, more than

13,000 aircraft have been purchased and the Boeing company is still developing the series to improve the performance. Figure 7 is the photo of B737-800.

Figure 7. Exterior of Boeing 737-800 [17]

In this research, since the study focuses on general aviation airports, no large commercial aircraft were considered as example.

FAA uses Runway Design Category (RDC), which is consisted of Aircraft

Approach Category (ACC) and Airplane Design Group (ADG). ACC is determined based on the approach speed, which is represented by reference speed (푉푅푒푓) or approach speed (푉퐴푝푝), and it is different from each aircraft model. The ADG is consisted of two elements, the first item is tail height measured from the ground to the highest tail top. The second element is a wingspan length measured from the left or right end of a wing to the end of the other wing. The ACC is shown in letter A, B, C,

D and E, on the other hand, the ADG is categorized as six Roman numbers, I, II, III,

IV, V and VI. Following Table 2 and Table 3 contain specification of each groups.

[18]

Code Approach Speed (푽푹풆풇/푽푨풑풑) A ~91kt B 91~121kt C 121~141kt D 141~166kt E 166kt~ Table 2. Aircraft Approach Category (AAC) of FAA [18]

Code Tail Height Wingspan I ~20ft (~6m) ~49ft (~15m) II 20~30ft (6~9m) 49~79ft (15~24m) III 30~45ft (9~13.5m) 79~118ft (24~36m) IV 45~60ft (13.5~18.5m) 118~171ft (36~52m) V 60~66ft (18.5~20m) 171~214ft (52~65m) VI 66~80ft (20m~) 214~262ft (65~80m) Table 3. Aircraft Design Group (ADG) of FAA [18]

For example, one of the most common instructional aircraft, the Cessna 172

Skyhawk has A-I category with approach speed of 62.4kt, 8ft 10inch of tail height and 35ft 10inch of wingspan. An ERJ-145 of Embraer having two turbo-fan engines is categorized into C-II based on the 101.4kt of approach speed, 22ft 2inch of tail height and 65ft 9inch of wing span. Another airplane Boeing 737-800, which is one of the most popular commercial airplanes around the world, is in D-IV with 150kt of reference speed, 41ft 2inch of tail height and 121ft 7inch of wingspan.

Differently, ICAO considers an aeroplane reference field length, the greatest main gear wheel span or the greatest wingspan length. The wingspan standards are exactly same as the FAA’s ADG guideline, however, the other two categories apply totally different elements. Following Table 4 and Table 5 are used for airport design category for ICAO criteria. [19]

Code # Reference Field Length 1 ~800m (~2625ft) 2 800~1200m (2625~3937ft) 3 1200~1800m (3937~5906ft) 4 1800m~ (5906ft~) Table 4. Aeroplane Reference Field Length of ICAO [19]

Code # Wingspan Main gear span A ~15m (~49ft) ~4.5m B 15~24m (49~79ft) 4.5~6m C 24~36m (79~118ft) 6~9m D 36~52m(118~171 9~14m E 52~65m(171 9~14m F 65m~(214 14~16m Table 5. Standard for Greatest Main Gear Span and Wingspan of ICAO [19]

Selected three examples, different from the FAA category, the Cessna 172S has 1A category with the same dimensions and speed. An ERJ-145 will be in group

4B and B737-800 is categorized in 4C. [20] [21] [22] Following Table 6 show the differences between FAA and ICAO for a same aircraft.

Aircraft FAA ICAO C172S A-I 1A ERJ-145 C-II 4B B737-800 D-IV 4C Table 6. Comparison of Airport Design Group of FAA and ICAO

Runway Length

When designing the length of a runway, FAA regulates that an airport elevation, local prevailing surface wind and temperature, runway surface conditions/slope and the performance/operating weight of critical aircraft should be essentially contemplated. [18] FAA categorizes aircraft into three groups and applies different methodologies. While FAA technically allows for the design of runways as short as 300 feet for very light and slow aircraft. Most runway lengths are designed for aircraft that approach runways for landing at a speed of 50 nautical miles per hour

(knots) and require runway lengths of at least 2,500 feet in length. Required runway lengths of aircraft with these approach speeds of at least 60 knots and/or having

Maximum Takeoff Weight (MTOW) greater than 12,500lbs are estimated using graphs in the FAA Advisory Circular AC 150/5325-4B. [23] If the design aircraft is equipped with jet engine or MTOW is greater than 60,000lbs, each manufacturer publishes airport planning manual that should be considered for airport design.

Design Airplane Weight Category MTOW RWY Length Approach 300ft (92m) at MSL Approach speeds less than 30kt Family (Above the MSL, increased 12,500lbs grouping of elevation 0.03 X Elev) (5,670kg) small 800ft (244m) at MSL or less Approach speeds of at least 30kt but airplanes (Above the MSL, 0.08 X less than 50kt Elev) Continued Table 7. Airplane Weight Categorization for Runway Length Requirements [23]

Table 7 continued With less than 5325-4B 7p. Approach speeds of 10 passengers 50kt or more With or more 10 5325-4B 8p. passengers Family Over 12,500lbs (5,670kg) grouping of 5325-4B 12~15p. but less than 60,000lbs (27,200kg) large airplanes Individual Airplane Manufacturer 60,000lbs (27,200kg) or more Regional Jets large Websites airplane

For aircraft of MTOW less than 60,000lbs, consideration of airport elevation and the mean daily maximum temperature of the hottest month of year are required for the determination and the right vertical axis gives a proper length with given condition.

For instance, at an airport that is 1,000ft above mean sea level, and the mean daily maximum temperature of the hottest month of year is 75°F, a runway design for a C172S need 3,200ft runway length using the Figure 8 (Refer to blue arrows). For the Beechcraft 1900D, which is a twin-turboprop aircraft, minimum 6,000ft of runway is required by FAA under the same airport location condition, 75% of fleet and 90% of useful load based on the figure of Figure 9 (Marked with blue arrows).

(Note: In this part, when an MTOW of an aircraft is greater than 60,000lbs or any weight of jet aircraft, individual charts for each aircraft should be used. Because the

AC 150/5325-4B offers runway length charts for airplanes of

12,500lbs≤MTOW<60,000lbs, the Beechcraft 1900D having MTOW of 17,120lbs

[24] has been chosen to show the length difference based on the aircraft size.)

Figure 8. Runway Length Requirement for Smaller Airplanes with Fewer than 10 Passenger Seats (Figure 2-1 of AC 150-5325-4B) [23]

Figure 9. Runway Length Requirement for Airplanes with 12,500lbs < MTOW ≤ 60,000lbs [23]

On the other hand, ICAO does not regulate specific length of runway and apply much more complicated process to decide a runway length. According to the

ICAO Annex 14 Aerodromes Chapter 3, [25] ICAO recommends that “The actual runway length to be provided for a primary runway should be adequate to meet the operational requirements of the aeroplanes for which the runway is intended and should not less than the longest length determined by applying the corrections for local conditions to the operations and performance characteristics of the relevant aeroplanes.” This instruction does not mean that the airport should consider the critical aircraft with its maximum weight. Also, the airport authority should deal with not only the take-off requirements, but also the landing requirements. ICAO guides to study an airport elevation, temperature, runway slope, humidity and runway surface

16 characteristics, which are almost the same conditions considered by FAA. The

Appendix B shows an example of runway length determination by ICAO (ICAO

Doc.9157 Part 1 Chapter 3 3.5.6) [19]

Runway Width

Different from the runway length, minimum runway widths are specified by both of FAA and ICAO based on the aircraft categories.

FAA considers a RDC of a critical aircraft and approach visibility minimums as criteria. Required runway width for each aircraft RDC is specified in the tables from Table A7-1 to Table A7-12 of FAA AC150/5300-13A [18].

RDC A/B- A/B-I A/B- A/B- A/B- A/B- C/D/E- C/D/E- C/D/E- Visibility II Small I II III IV I,II III,IV,V VI Small Not lower 60ft 75ft than ¾ mi 100ft 150ft 100ft 150ft 200ft Lower 75ft 100ft 100ft than ¾ mi Table 8. A Runway Width Minimum based on the RDC by FAA [18]

According to the Table 8, when the visibility minimum of an airport is lower than ¾ statue mile, a Cessna 172S (having A-I) will need 75ft of runway width. An

ERJ-145 (having C-II) and B737-800 (having D-IV) need 100ft and 150ft of runway width for each, regardless of the visibility minima.

As a recommendation, ICAO also applies specific runway minimum width based on an aeroplane reference field length and a wingspan or a greatest main gear span category. To determine more exact width for runway, deviation of an aeroplane from the centerline at touchdown, crosswind condition, runway surface contamination, rubber deposits, crab landing approaches used in crosswind

17 conditions, approach speeds, visibility and human factors should be considered.

(ICAO Annex 14 Chapter 5, 5.1.2) [25]

Code Letter Code # A B C D E F 1 18m 23m - 2 23m - - 30m 3 30m 45m 4 - 45m 45m 60m Table 9. Runway Width Recommendation based on the Aeroplane Code Letter and Number by ICAO [25]

As the Cessna 172S is categorized 1A, the runway width is recommended to be 18m (59ft). The ERJ-145 is in a group of 4B, however, there is no specific recommendation for the group. Therefore, it should be greater than 30m (98ft). For the B737-800, it will be in the group 4D, which requires 45m (148ft) of runway width.

Even though the overall ICAO standards are smaller than the FAA standards, most of the numbers are similar.

Runway Shoulder

Runway shoulders are provided to offer additional safety area to departing and arriving aircraft for prevention of blast erosion, support of maintenance/emergency equipment. It is an extra protection of passenger and aircraft itself in the case of runway excursion. To fulfill these missions, a runway shoulder should be paved area and meet specific standards.

As the runway width, FAA decides the runway shoulder width based on the RDC

(showed in the Table 10). ICAO considers the code letter of aircraft as a criteria and

a runway code D/E aircraft and runway width is less than 60m are recommended to

be provided a runway shoulder. For a code F aircraft runway, a runway shoulder

also recommended to be provided.

RDC

A/B-I,II A/B- C/D/E- A/B-IV C/D/E-I,II C/D/E-V C/D/E-VI (Small) III III,IV Shoulder 10ft 20ft 25ft 10ft 25ft 35ft 40ft Width Table 10. A Runway Shoulder Width Minimum based on the RDC by FAA [18]

In the perspective of FAA, the Cessna 172S and ERJ-145 will have 10ft of runway shoulder, even though they have different RDC. For the B737-800 of RDC D-

IV the proper shoulder width will be 25ft. On the other hand, ICAO does not specify any required runway shoulder for all of the aircraft above as none of they are D, E or

F category aircraft.

Other Segments for Runway Safety

There are some extra surfaces to enhance a safety of runway, however, FAA and ICAO have totally different systems. First, FAA categorize those areas into four,

Runway Safety Area (RSA), Runway Obstacle Free Zone (ROFZ), Runway Obstacle

Free Area (ROFA) and Runway Protection Zone (RPZ). Differently from FAA, ICAO consider a strip and Runway End Safety Area (RESA) for advanced safety.

FAA Safety Areas

 Runway Safety Area (RSA)

The RSA is a surface surrounding a runway to reduce a risk of damage when an overshoot, undershoot or excursion from runway is occurred. No object is allowed in the RSA including NAVAIDs. As the runway width and shoulder, RDC and

19 approach visibility minimum determines the dimension of RSA. (FAA AC150/5300-

13A Chapter 3 307.) [18]

*Dep: Departure

RDC

A/B-IV, Area A/B-I A/B-II A/B-III C/D/E- I~VI Not lower Lower Not lower Lower Not lower Lower VIS N/A than ¾ mi than ¾ mi than ¾ mi than ¾ mi than ¾ mi than ¾ mi Beyond Dep 800ft 1000ft end 240ft 600ft 300ft 600ft 600ft Prior to 600ft 600ft Threshold Width 120ft 300ft 150ft 300ft 300ft 400ft 500ft Table 11. Runway Safety Area Dimension Requirements by FAA [18]

Based on the Table 11, if there is an airport having lower visibility minimum than ¾ mile, for Cessna 172S, the RSA should be extended to 600ft beyond the runway and started 240ft prior to the threshold. The width should be 300ft. In the case of ERJ-145 and B737-800, the visibility minimum does not affect to the dimension of

RSA. The ERJ-145 needs a RSA of extended 1,000ft beyond the runway end, started

600ft prior to threshold and 500ft of width. An airport that wants to accommodate

B737-800 has to have same RSA dimension as the ERJ-145.

 Runway Obstacle Free Zone (ROFZ)

The OFZ is a three-dimensional airspace extended centered on a runway and a runway centerline. This space should be considered to offer proper safety margin for taking off, landing and executing missed approach aircraft. Also, no object can be placed within this space, except navigational aids (NAVAIDs) fulfilled the frangibility standards. (FAA AC150/5300-13A Chapter 3 308.) [18] The POFZ means

Precision Obstacle Free Zone, which is applicable for precision approach aircraft.

RDC

Dimension A/B-I A/B-II A/B-III A/B-IV C/D/E-I~VI

Length 200ft beyond each end of the runway

Lower than VIS ¾ mi approach visibility minimum: 300ft ROFZ Small Small aircraft with approach speeds of 50kt or more: 250ft Width Small aircraft with approach speeds of less than 50kt: 120ft

Large 400ft Visibility minimum not lower than ¾ mi: N/A POFZ Visibility minimum lower than ¾ mi: Length – 200ft, Width – 800ft (VIS: Visibility) Table 12. Obstacle Free Zone Dimension Requirements by FAA [18]

As stated in the Table 12, the length of Runway Obstacle Free Zone is 200ft for all airports. But for C172S, when it is an airport having visibility minimum lower than ¾ mile, the width should be 300ft. In any case, for the ERJ-145 and B737-800, the ROFZ width should be 400ft.

 Runway Obstacle Free Area (ROFA)

A ROFA is a rectangle area placed on the ground. Also, to enhance the safety, clearing the surface from any objects is needed except NAVAIDs. This is valid for any objects protruding above the nearest point of the RSA. (FAA AC150/5300-13A

Chapter 3 309.) [18]

RDC

A/B-IV, Dimension A/B-I A/B-II A/B-III C/D/E-I~VI Not lower Lower Not lower Lower Not lower Lower Visibility - than ¾ mi than ¾ mi than ¾ mi than ¾ mi than ¾ mi than ¾ mi Beyond Dep end 800ft 1,000ft 240ft 600ft 300ft 600ft 600ft Prior to 600ft 600ft Threshold Width 250ft 800ft 500ft 800ft 800ft 800ft 800ft (Dep: Departure) Table 13. Runway Obstacle Free Area Dimension Requirements by FAA [18]

Based on the FAA AC 150/5300-13A, the C172S will need an ROFA having length extended 600ft beyond the runway end and prior to the runway threshold for the airport having lower visibility minimum than ¾ mile. The width should be more than 800ft in this visibility condition. For the ERJ-145 and B737-800, the dimension of ROFA will be same as the RSA except width, which is 800ft instead of 500ft.

 Runway Protection Zone (RPZ)

RPZ is a trapezoidal area from prior to the threshold or beyond the runway

(FAA AC 150/5300-13A Chapter 3 310.). According to the advisory circular, the RPZ is set for enhanced safety of people and property on the ground. It is started from

200ft offset point of runway end or threshold prohibiting infrastructures that brings a number of people such as school, stadium or other social places. There are two kinds of RPZs, Approach RPZ that is placed prior to the runway threshold for arrival aircraft and Departure RPZ that is placed beyond the runway end for departure airplane. Each RPZ is consisted of two components, the central portion and the controlled activity area. The central portion extends from the beginning of the end of the RPZ and the width is same as the OFA width. The other area is regarded controlled activity area, which is either side of the central portion of the RPZ (FAA

AC150/5300-13A Chapter 3 310.). [18]

For example, all of the Cessna 172S, ERJ-145 and B737-800 will have an approach RPZ of 2,500ft length, 1,000ft of inner width and 1,750ft of outer width for an airport of lower visibility minimum than ¾ mile. For departure airplane, however, the length will be 1,000ft, 250ft inner width and 450ft of outer width will be needed for C172S. When an ERJ-145 or B737-800 takes off from the same airport, the airport

22 will need the departure RPZ having 1,700ft of length, 500ft of inner width and 1,010ft of outer width.

Figure 10. Runway Protection Zone (RPZ), Runway Obstacle Free Area (ROFA) and Runway Safety Area (RSA) [18]

RDC (Dimension in ft)

Dimension A/B-I Small A/B-I A/B-II Small A/B-II,III,IV C/D/E-I~IV

Not Not Not Not Not Not Not Not Not Not Lower Lower Lower Lower Lower VIS lower lower lower lower lower lower lower lower lower lower than ¾ than ¾ than ¾ than ¾ than ¾ than than ¾ than than ¾ than than ¾ than than ¾ than than ¾ (SM) mi mi mi mi mi 1mi mi 1mi mi 1mi mi 1mi mi 1mi mi Length 1000 1700 2500 1000 1700 2500 1000 1700 2500 1000 1700 2500 1700 1700 2500

Inner 250 1000 1000 500 1000 1000 250 1000 1000 500 1000 1000 500 1000 1000 App Width RPZ Outer 450 1510 1750 700 1510 1750 450 1510 1750 700 1510 1750 1010 1510 1750 Width

Acres 8.035 48.978 79.000 13.770 48.978 78.914 8.035 48.978 79.000 13.770 48.978 78.914 29.465 48.978 78.914

Length 1000 1000 1000 1000 1000 1700

Inner 250 500 250 500 500 500 Dep Width RPZ Outer 450 700 450 700 700 1010 Width

Acres 8.035 13.770 8.035 13.770 13.770 29.465 Table 14. Runway Protection Zone Dimension Requirements by FAA [18]

ICAO Safety Areas

ICAO requires much less extra safety surfaces than FAA. Two elements should be included for airports following ICAO standards, a runway strip and runway end safe area (RESA).

 Runway Strip

Runway strip has a similar function as the RSA of FAA, which offers a buffer area to protect passengers, aircraft wings and fuselage in the case of runway excursion. Objects on the runway strip should be regarded as a potential danger and removed as far as possible (ICAO Annex 14 Chapter 3.4.6). [25] Therefore, any fixed object is not allowed in this area except visual aids for navigation safety satisfying the frangibility requirements (ICAO Annex 14 Chapter 3.4.7). [25] Following Table 15 shows the required dimension of runway strip based on the aircraft category code number.

Dimension Length Width Code # Non- Non- Instrument Instrument instrument instrument 1 60m 30m 30m 75m 2 40m 60m 3,4 150m 75m Table 15. Required Runway Strip Dimension Not Allowing Fixed Objects [25]

Also any mobile object should be removed during takeoff and landing of aircraft within a distance based on Table 16 (ICAO Annex 14 Chapter 3 3.4.7). [25]

ILS Category Code # CAT I CAT II CAT III 1 45m - 2 3,4 60m F-4 77.5m (ILS: Instrument Landing System, CAT: Category) Table 16. Required Runway Strip Dimension Not Allowing Mobile Objects during Takeoff and Landing [25]

To protect a landing aircraft, ICAO recommends setting of a strengthened area against blast erosion at least 30m before a threshold (ICAO Annex 14 Chapter 3

3.4.11) [25]. Also, the strip should satisfy the loading bearing capacity to reduce a danger from the difference of it.

For example, in the case of instrument airport, a C172S should have a length

60m (197ft) extended from the threshold and end of the runway and width 75m extended from centerline. For the ERJ-145 and the B737-800, strip length will be 60m extended from the threshold and end of the runway and width will be 150m extended from centerline.

Approach Procedure Code # Instrument Non-instrument 1 30m 40m 2 40m 3,4 75m 75m Table 17. Prepared Area Dimension for Differences in Loading Bearing Capacity [25]

 Runway End Safety Area (RESA)

Runway end safety area (RESA) works for a similar purpose as the runway strip, but placed before the threshold and beyond the end of runway. It is required to be installed for a protection of passengers and aircraft in the case of overrun or undershoot, which is different from the runway strip served for runway excursion.

The required minimum dimension of the RESA is explained in Table 18. [25]

Dimension with respect to Approach Procedure Length Code # Width Instrument Non-instrument 1,2 At least twice width 90m Not required 3,4 of RWY 90m Table 18. RESA Minimum Dimension based on the Aircraft Code Number [25]

However, for an additional safety, ICAO recommends longer lengths of RESA than the required 90m.

Approach Procedure Code # Instrument Non-instrument 1,2 120m 30m 3,4 240m Table 19. RESA Recommended Length based on the Aircraft Code Number [25]

Following Figure 11 and 12 shows differences of FAA and ICAO runway design components.

Figure 11. Required Runway Components by FAA (Not to scale) [18]

Figure 12. An Example RESA of a Runway for Code 3 and 4 Aircraft [25]

For instance, an instrument airport serving C172S is recommended to have an

RESA of 120m (394ft) length and maximum width of 36m (118ft), which is twice of the runway width for C172S. Both of the ERJ-145 and the B737-800 are recommended to have 240m (787ft) length of RESA, but the width for ERJ-145 will be maximum 60m (197ft) and B737-800 will be maximum 90m (295ft) of width.

Following Table 20 is a summary of the required runway dimensions of three example aircraft based on the ICAO and FAA standards.

Example Aircraft Design Components C172S ERJ-145 B737-800 FAA ICAO FAA ICAO FAA ICAO FAA ICAO Design Code A-I 1A C-II 4B D-IV 4C TO:6,400ft TO:3,400ft Length 3,200ft N/A LDG: N/A N/A LDG:4,400ft 4,800ft 18m N/A 45m Width 75ft 100ft 150ft (59.06ft) (30m-98.43ft) (147.64ft) Shoulder 10ft N/A 10ft N/A 25ft N/A Beyond 600ft 1,000ft 1,000ft Dep 60m 60m 60m Length Prior to (196.85ft) (196.85ft) (196.85ft) RSA Strip 600ft 600ft 600ft THLD 75m 150m 150m Width Width 300ft 500ft 500ft (246.06ft) (492.13ft) (492.13ft) Beyond 600ft 1,000ft 1,000ft Dep 36m 60m 90m Length Prior to (118.11ft) (196.85ft) (295.28ft) ROFA RESA 600ft 600ft 600ft THLD 120m 240m 240m Width Width 800ft 800ft 800ft (393.70ft) (787.40ft) (787.40ft) (TO: Takeoff, LDG: Landing, Dep: Departure, THLD: Threshold) Continued Table 20. Example Aircraft Dimension Categories

Table 20 continued Length 200ft 200ft 200ft ROFZ Width 300ft 400ft 400ft Length 1,000ft 1,700ft 1,700ft Inner 250ft 500ft 500ft DepRPZ Width Outer 450ft 1,010ft 1,010ft Width Length 2,500ft 2,500ft 2,500ft Inner 1,000ft 1,000ft 1,000ft ArrRPZ Width Outer 1,750ft 1,750ft 1,750ft Width Area (1,000퐟퐭ퟐ) 6,942.5 977.27 10,376.0 3,653.38 8,776.0 2,824.13 Percentage difference Percentage difference 710% 284% 311% [(퐅퐀퐀 퐀퐫퐞퐚 ÷ 710% 284% [(퐅퐀퐀 퐀퐫퐞퐚 ÷ 퐈퐂퐀퐎 퐀퐫퐞퐚) × ퟏퟎퟎ] 퐈퐂퐀퐎 퐀퐫퐞퐚) × ퟏퟎퟎ]

As ICAO does not specify the runway length and shoulder width, when the same length is applied, an FAA airport for C172S will have about 7 times broader area than an airport considered ICAO standard. For the ERJ-145 and B737-800, when an airport follows FAA requirement rather than ICAO’s, about three times of expansive property is needed. This can be interpreted that airports in United States offers more buffer area in the case of an event. The international standard and the

U.S. standards were compared to investigate the runway design procedure helping aircraft to operate within the designated runway area. In this research, runway excursions that occurred in the United States were considered. Future research may consider comparing runway excursion probabilities of the U.S. with that of international operations to study any correlation between excursion likelihood and runway design differences.

1.5. Previous Runway Excursion Modeling Studies

The above specifications were designed by FAA and ICAO for the primary purpose of providing runways and associated runway safety areas that are sufficient to minimize the risk of runway excursions. The question remains, how well do these designs contribute to minimizing these potential accidents. Some historical studies have begun to address this issue. Three of the more in-depth reports on modeling the risk of excursions have been published through the FAA’s ACRP (Airport

Cooperative Research Program); ACRP Report 3 – “Analysis of Aircraft Overruns and Undershoots for Runway Safety Areas” [26], ACRP Report 51 – “Risk

Assessment Method to Support Modification of Airfield Separation” [27], ACRP

Report 107 – “Development of a Runway Veer-Off Location Distribution Risk

Assessment Model and Reporting Template” [28]. The overruns and undershoots study tried to estimate the probability of runway overruns and undershoots during aircraft takeoff and landing.

The ACRP report 3 achieved to develop a model to estimate the risk of runway undershoots and overruns accidents. Based on the historical data, the research team tried to figure out relevant operation factors that causes high probability of undershoot or overruns. The model was expected to be used for runway safety area

(RSA) assessment rather than the runway paved area. A growing demand for larger aircraft motivated the airfield separation study, which is the report 51. Although there has been a growth in the number of larger aircraft, a number of airports had difficulties to modify their safety areas based on the conventional aerodrome planning standards. For airports that desires an approval of modification of standards (MoS), the report 51 focused on the separation between airfield components such as runway

29 and taxiway, taxiway and taxiway, taxilane and fixed obstacles. Their model helps to evaluate the existing distance based on the risk assessment method. The report 107 is the most recent research among three of the studies and adopted similar methodologies that were used for previous researches. The research also deals with the deviation from the centerline, however, the range of research was runway veer- offs. Researchers also used historical data to figure out contributing factors and could develop a software to estimate the probability and vulnerable location of the runway.

All of the three ACRP study researchers used similar approaches and developed several risk assessment models for each type of aviation accidents and incidents, however, they are more focused on commercial aviation, not general aviation.

Risk assessment models were developed exclusively for runway excursion accidents to assess runway safety area. While, these models were driven using commercial aircraft data, the findings can be used in general aviation airports as the models also considered a number of general aviation accident information. In this research, three models were reviewed; a risk assessment method about airfield separation standards modification, modeling for overruns and undershoots in the perspective of Runway Safety Area (RSA) and veer-off location distribution modeling.

Three studies used similar methodologies to analyze the risk of runway excursions. First, historical accident and incident data related each accident types were collected and filtered out inappropriate data from the collected dataset. The actual accident and incident data were collected from several U.S. and international databases. All of the airfield risk assessments, overruns/undershoot and veer-offs model researchers collected based on this condition, similar databases were

30 considered. Collected accident/incident data and identified event parameters were helpful to develop an approach for the development of risk assessment of runway safety area. An interesting point is that the safety area risk model and undershoot/overrun studies considered not only the event data, but also normal operation data (NOD) to figure out criticality of each causal factor. Collected materials were needed to be properly normalized to develop each model.

Common frame of the estimation model

For modeling, even though the three reports focus on different types of accidents/incidents, all three teams of researchers used the same basic models. Each report separated the model into three different elements; a frequency model that estimates the number of excursions under given condition, a location model that estimates the likelihood of excursion based on the location of aircraft on a runway and a consequence model that estimates the result of deviation from a centerline.

Frequency estimation model

Models for frequency of excursions were developed to estimate the likelihood of runway excursion under the given environmental and weather condition. Even though all of the three studies used same frame for the modeling, the estimated value in each study was different since the three reports are focusing on different aspects.

The basic model equation is shown in following equation (1).

1 푃{퐴푐푐𝑖푑푒푛푡 푂푐푐푢푟푟푒푛푐푒} = (1) 1+푒푏0+푏1푋1+푏2푋2+푏3푋3+⋯

When 푃{퐴푐푐𝑖푑푒푛푡 푂푐푐푢푟푟푒푛푐푒}: the probability of an accident type

occurring given certain operational

conditions;

푋푖: independent variables

푏푖: regression coefficients

In the safety risk assessment model, researchers tried to estimate the frequency of an aircraft excursion from a centerline while an aircraft is operated on a taxiway, taxilane and runway. The overrun/undershoot used the frame to estimate the probability of undershoot and overrun during takeoff and landing. The veer-off study used the equation to evaluate the likelihood of veer-offs on a runway.

Each study sets a reference situation for the event estimation and if the condition at the operation is different from the reference situation, the coefficient was changed for each variable. A reference condition for the estimation model is an aircraft of large jet with MTOW of 41,000lbs-255,000 lbs, domestic operation of commercial purpose, without icing or snow between two hub airports. For overruns and undershoots model, no electric storm, a temperature between 15-20 degrees in

Celsius, visibility between 8-10 mile, ceiling greater than 2,500ft and no significant terrain were added for reference condition. Veer-offs model additionally considered a turbojet engine without any gust/rain/frozen precipitation/fog and daylight.

Following Table 21 contains example independent variables used for veer-off frequency estimation model.

Independent Category 푿 Explanation 풏 Variables

Purpose of flight 푋1 User class G General aviation Heavy aircraft, with Aircraft weight (in 푋 User class A/B MTOW greater than MTOW) 2 255k lbs Continued Table 21. Independent Variables used for Veer-off Model [29]

Table 21 continued Commuter(MTOW 41k-255klbs) , Aircraft weight (in medium(MTOW 푋 User class D/E/F MTOW) 3 12.5k-41k), small aircraft (MTOW 12.5k or less lbs) Visibility less than 푋 4 2mi Visibility from 2 to Visibility 푋 (Just for TO) 5 4mi Visibility from 4 to 푋 (Just for TO) 6 8mi Crosswind from 5 푋 7 to 12kt Crosswind from 2 Crosswind 푋 (Just for LDG) 8 to 5kt Crosswind more 푋 9 than 12kt Tailwind from 푋 (Just for LDG) 10 5 to 12kt Tailwind Tailwind more than 푋 (Just for LDG) 11 12kt Temperature less 푋 12 than 5℃ Temperature from Temperature 푋 13 5 to 15℃ Temperature more 푋 14 than 25℃

푋15 Icing conditions (Just for LDG) 푋16 Rain 푋17 Snow Precipitation Frozen 푋 (Just for LDG) 18 precipitation

푋19 Gusts (Just for LDG) 푋20 Fog (Just for LDG) Engine type 푋21 Turboprop 푋22 Foreign O/D Airport type Hub/Non-hub 푋 (Just for TO) 23 airport Log criticality Calibration 푋 24 factor

Light condition 푋25 Night conditions (Just for LDG)

Among the independent variables in Table 21, general aviation flights, visibility less than 2 miles, crosswind more than 12 knot and a turboprop engine aircraft were revealed as critical variables with larger coefficients for landing veer- offs. On the other hand, in takeoff veer-offs, the general aviation flight, the visibility less than 2 miles and turboprop aircraft affected more in the probability. The visibility

33 condition from 4 to 8 miles and rain factor also have higher coefficient than other variables.

Location estimation model

The second element of the modeling was an accident location model that analyzed the correlation of location of an aircraft on a runway and the likelihood of an accident or incident. This is because the probability of the location of the excursion along a runway is not evenly distributed on a runway. When an aircraft is located closer to the runway threshold, the probability of an event is higher than when an aircraft located farther from the runway threshold. The basic model used for the location modeling is following function (2).

푛 푃{퐿표푐푎푡𝑖표푛 > 푥} = 푒−푎푥 (2)

When 푃{퐿표푐푎푡𝑖표푛 > 푥}: the probability of an accident when the distance

from the runway centerline to the aircraft is greater than a given

threshold;

푥: given location or distance from a reference point;

푎, 푛: regression coefficients

Interestingly, the veer-off study divided the runway distance available (RDA) into ten segments on each right and left side for a longitudinal direction to normalize each different length of runways. For the lateral deviation, the researchers measured the distance from each side edge of a runway toward outside because the focus of this report was veer-off modeling based on the runway, which is a paved area, not from the runway centerline. Because sometimes the RSA surface materials are different

34 from the runway pavement, the runway edge was used as reference for the lateral distances. Following Figure 13 and Figure 14 show the depicted distance references.

Figure 13. Use of runway edge to measure lateral distances [28]

Figure 14. Subsections of the RSA – Example for normalization with runway distance available [28]

According to the distribution of longitudinal probability, between the subarea

0.3 and the subarea 0.5 have high probability for landing and more aircraft were observed in the beginning sections of the runway, between the subarea 0.1 and the subarea 0.3 during takeoffs.

Figure 15. Risk contour of adjusted probability to exceed a given lateral distance [28]

The Figure 15 risk contour represents a similar result as the landing distribution. The adjusted risk contour was illustrated that the probability to exceed a certain level of deviation is higher in the sections between the subarea 0.3 and the subarea 0.5. This can be interpreted that the area closer to the runway threshold is relatively vulnerable.

Consequence model

Lastly, a consequence of an accident is helpful to estimate the influence of an event. The airfield separation standard study and the veer-off study considered the consequence models. Both articles tried to show the result of an accident by combining the frequency and location models in given circumstance information based on the cost for damage or the correlation between the location and the probability of a collision with an obstacle around RSA. However, as the probability of an event is dependent on not a single factor, but each condition of an airport, aircraft,

36 human factor and other environmental circumstances, it is hard to clearly present the model in single function.

Previous existing modeling studies have focused on larger aircraft. Even though three of the studies also collected the actual accident data from common database as this thesis, smaller general aviation aircraft, such as having light maximum gross takeoff weight smaller than 12,500lbs and single or piston engine aircraft, were removed from the dataset for each models. A number of commercial aircraft related data were also collected from foreign database and concentrated on commercial aspects, rather than general aviation.

In comparison to commercial flights, general aviation is not necessarily operated for commercial passenger transportation, but also for personal, instructional or agricultural purposes. General aviation has different characteristics from commercial aviation; smaller, lighter and slower aircraft using shorter and narrower runways. As great portion of the entire pilots stay within the general aviation environment, it is necessary to focus on general aviation.

In the following chapter, a general aviation centered study has been conducted. First, actual accident and incident reports were analyzed and correlated such accidents and incidents to runway design. Then, further investigation of aircraft operating behavior using empirically collected data of runway operations at the Ohio

State University Airport. Based on two data types of data analysis, runway design standards for general aviation were investigated.

Chapter 2: Analysis of Runway Excursion Incidents and Accidents

As mentioned in Chapter 1, runway excursion accidents make up a considerable percentage of overall aviation accidents. Towards the ultimate goal of reducing runway excursion accidents, it is necessary to investigate the conditions under which excursions may occur. As the literature reviewed for this research has revealed, historical runway excursion prediction models have generally focused on commercial aircraft operations, focusing on aircraft weight

(heavier commercial aircraft), purpose of flight (mainly commercial) and negative meteorological phenomena, such as poor visibility or low cloud ceilings. General aviation flights, however, may not necessarily be affected by these factors. This chapter will perform such an investigation.

2.1 Data Sources

To further understand the possible characteristics of runway excursions involving GA flights, this research investigated aviation accident and incident databases in the U.S. where hundreds of runway excursion events were analyzed.

The three databases considered for this research were: The National

Transportation Research Board (NTSB) [29], Aviation Accident Database and

Synopses, the Aviation Safety Reporting System (ASRS) operated by NASA [30], and the FAA Accidents and Incidents Data System (FAA AIDS) [31]. All of three databases have their own website with easy access to their records. Each server

38 offers conditional search functionality and users can easily narrow down accidents that a user exactly wants.

NTSB Aviation Accident Database and Synopses [29]

One of the purposes of the National Transportation Safety Board (NTSB) is to conduct investigations of transportation accidents, analyze accident causal trends, and provide suggestions for improvement of transportation safety based on the analysis result. To share information, the NTSB operates an accident database which includes analysis reports for each accident within the database. Basically, the NTSB aviation accident database offers two types of reports per case. The first version is a factual report that includes information about location/time, flight itinerary, aircraft specification, operator, weather, flight pilot and number of injuries. Narratives, a statement from related person to the accident, allows investigators and other people to get more specific and situational information regarding the accident. The other version, probable cause report, has all information of factual report but more specific aspects of meteorological information, pilot information and determined factors that caused the accident.

The probable cause report is usually posted after all of the investigation is finished. The first recorded runway excursion accident in the NTSB database occurred in May, 1991.

Figure 16. NTSB Aviation Accident Database & Synopses [29] (Source: http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx)

Figure 17. An Example of Factual Report from NTSB Database [29] (Source: http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx)

Figure 18. A Probable Cause Example Report from NTSB Database [29] (Source: http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx)

Aviation Safety Reporting System (ASRS) [30]

The Aviation Safety Reporting System (ASRS) is served by the National

Aeronautics and Space Administration (NASA). This database is filled with voluntary reports from those who may have been involved in an accident, incident, or other unsafe situation. Pilots, air traffic controllers and other field workers report what they experienced or witnessed. Most information is reported by pilots, especially general aviation pilots. Similar to the NTSB database, the

ASRS offers accident occurrence time, place, environment, pilot, narrative and

42 tentative assessment from the person who posted the accident. As the system collects voluntary information, many cases include scant data to retrieve the exact situation of an accident. Specifically, relatively few cases have the airport information at or near where the event occurred. Usually the month/year, aircraft model, FAR part, pilot information, result of the event and expected causal factor were described with narratives. The oldest runway excursion accident was reported in 2006 August. The database contains 184,425 cases and runway excursions were reported as 187 cases until 2015 September.

Figure 19. Aviation Safety Reporting System (ASRS) by NASA [30] (Source: https://asrs.arc.nasa.gov/search/database.html)

Figure 20. An example of ASRS result page [30] (Source: https://asrs.arc.nasa.gov/search/database.html)

FAA Accident and Incident Data System (AIDS) [31]

To cover safety issues that are not covered by the NTSB, FAA operates the FAA AIDS database focusing on voluntary reports. The FAA AIDS database contains the oldest runway excursion data, which was occurred in 1988 October.

Figure 21. FAA Accident and Incident Data System (AIDS) [31] (Source: http://www.asias.faa.gov/pls/apex/f?p=100:12:0::NO:::)

Figure 22. An example of FAA AIDS result [31] (Source: http://www.asias.faa.gov/pls/apex/f?p=100:12:0::NO:::)

2.2. Data Collection

As each database contains thousands of accident records, specific accidents and incidents that involved runway excursions are extracted for this research. “Runway excursion” was used as a search term for the NTSB and ASRS databases and “Excursion” was used for the AIDS database. A different search condition was applied for FAA AIDS because few of incidents were found with the term of “runway excursion.” Reports that existed in each database through

September 2015 were extracted. The earliest record was from the FAA AIDS database, which occurred in October 28th, 1988. The number of excursion events within the given period over the three databases was 628 cases. Among the entire

628 cases, 410 events (65.29%) were found in the NTSB database, 179 events

(28.5%) were from the ASRS and 39 events (6.21%) were collected from the FAA

AIDS database. Some reports found in the search that were not excursion accidents were removed, as the word “Excursion” in these records was used to explain pilot’s past accident experience. Those reports were removed from the dataset. Excursions at seaplane base and heliports were also excluded for this study.

28% NTSB ASRS 66% FAA AIDS

Figure 23. Percentage Distribution of Runway Excursion Events from Each Database

While it cannot be guaranteed that the searches performed on these databases captured every runway excursion, the more than 600 records found were thought to be sufficient to analyze the characteristics of these records with sufficient statistical significance.

Each accident report was coded on an Excel sheet, illustrated in part in

Figure 24. Each column categorizes each accident / incident record by characteristics surrounding the accident / incident. These characteristics are described in Table 22. As mentioned above, each accident report contains different level of detailed information. For example, NTSB reports record everything as detailed as possible, such as time, airport, operated runway, aircraft, specific weather information and pilot demography. However, ASRS and AIDS frequently contain reports with limited information about the accident. In such cases, either the categorized data was found deep within narratives of the reports and then categorized accordingly in the spreadsheet, or when there was no data for a certain category to be found, that characteristic was marked as “-” and considered as “No Info” for the given record. Example excursion cases in the collected dataset are attached in Appendix C.

Figure 24 . Example of collected data on Excel sheet with NTSB data

Category Item Explanation Category Identification # Combined # number in the combined dataset Accident data Source Database source Record number # in the dataset for each database Case number Accident Case # identification of the database

Year

Month Accident Time occurrence time Date

Time

Event type Accident/Incident Type of event Accident or Incident Airport Code Airport Airport (FAA) Model Aircraft model Operational Empty Weight OEW (OEW) of the aircraft Maximum Take- Off Weight Aircraft MTOW (MTOW) of the aircraft 0: MTOW≤ 12,500lbs 1: 12,500

Table 22 continued Aerial application, Aerial observation, Agricultural, Banner tow, Business, Cargo, Executive/Corporate, Purpose of the Ferry, Flight test, Mission flight Instructional, Medical, Other, Passenger, Personal, Positioning, Public use, Sightseeing, Skydiving, Test flight, -(No info or N/A) Flight Info Approach, Climbing, Enroute, Flare or Phase of the T/D(Touchdown), Landing, Phase causal factor was LDG(Landing) Roll, noticed Maneuvering, T/G(Touch and Go), Takeoff, Taxi, -(No info or N/A) Severity of Damage damage caused Substantial or minor by the accident Fire Yes or None VFR Meteorological Visibility Condition (VMC), IFR VIS Con condition Meteorological Condition (IMC) VIS Visibility in Statue Miles 0: VIS ≤ 1 1: 1

Wind Combined cell of “Wind Wind direction/speed Direc” and “Wind Speed”

In degree, based on Wind Direc Wind Direction magnetic north

Wind Speed Wind Speed In knots

0: Calm, light or wind ≤ 3kt Wind Speed 1: 3

Table 22 continued G: Gust Gust with speed **/--: Average wind speed Gust with speed (if available) of ** and the maximum wind speed of -- in knots Presence of gust Gust without speed Presence of X/T wind X: Crosswind Meteorological crosswind and (Cross/Tailwind) T: Tailwind Info tailwind Presence of any Any gust, windshear, Wind Event negative wind crosswind or tailwind event Temperature in Temperature Celsius Presence of any Precipitation precipitation Runway length Runway Info RWY Length in ft 0: Length≤1,000ft 1: 1,000

Table 22 continued Right of left based on the Deviation Excursion Excursion direction RWY centerline, overrun, tendency both, ambiguous Age of pilot Pilot age (CIP) 0: Age≤23yrs Pilot age 1: 23

terr/obj 0: Not occurred Evasive action 1: Occurred Rejected T/O Aborted after T/D Low approach, go around or L/A, G/A, M/A missed approach Collision during Result TO/LDG Weather encounter Nose over/nose

down Hard landing Abnormal RWY

contact LDG area

overshoot Continued

Table 22 continued LDG gear collapse Sys/Comp

Malf/Fail Stall Preflight/Dispatch Result RWY Incursion Abrupt maneuver Dragged wing/rotor/float or other

Using the collected dataset, rates of runway excursion under certain conditions were compared to figure out characteristics of general aviation runway excursions.

2.3. Data Analysis

Estimating Commercial Excursion Probability and General Aviation Excursion

Probability

Table 23 lists the total number of runway excursions from the collected dataset that occurred during each year, for both commercial and general aviation operations. The number of runway excursion of the commercial aviation includes the FAR part 121 (Air carrier certificate), Part 135 (Commuter and on demand operations), Non-U.S. and foreign operations. On the other hand, the flight hour for the general aviation contains operations by general aviation aircraft and considered number of runway excursions were under the FAR Part 91 (General operations), Part 103 (Ultralight aircraft), Part 137 (Agricultural), Part 141 (Pilot school), public and other purposes.

To compare the runway excursion rates between general aviation and commercial aviation, yearly operation data were collected from the FAA Air

Traffic Activity System (ATADS) that contains the official air traffic operations data. Table 23 represents the total number of annual operations of commercial airline operations and general aviation operations at towered airports from the

FAA ATADS.

Commercial General Aviation Year Operations Excursions Ratio Operations Excursion Ratio 1990 22,057,594 0 0 40,612,578 0 0 1991 21,399,332 0 0 38,405,611 0 0 1992 22,043,824 0 0 38,091,169 1 0.26 × 10−9 1993 22,669,909 0 0 36,591,937 1 0.27 × 10−9 1994 23,584,376 0 0 36,293,632 1 0.27 × 10−9 1995 23,911,274 1 0.42 × 10−9 35,579,641 1 0.29 × 10−9 1996 24,055,796 0 0 35,201,194 3 0.85 × 10−9 1997 24,401,949 0 0 37,468,566 0 0 1998 24,569,442 2 0.81 × 10−9 38,784,817 1 0.26 × 10−9 1999 25,388,368 1 0.39 × 10−9 40,332,986 0 0 2000 25,950,304 0 0 38,890,702 2 0.51 × 10−9 2001 25,014,431 0 0 37,495,028 0 0 2002 24,470,600 0 0 37,449,804 2 0.53 × 10−9 2003 24,269,079 0 0 35,304,510 1 0.28 × 10−9 2004 25,535,759 1 0.39 × 10−9 34,576,681 0 0 2005 25,904,327 2 0.77 × 10−9 33,925,208 0 0 2006 25,204,426 0 0 33,290,626 5 0.15 × 10−8 2007 25,268,859 3 0.11 × 10−8 32,895,626 3 0.91 × 10−9 2008 24,149,716 3 0.12 × 10−8 30,603,304 3 0.98 × 10−9 2009 22,145,338 2 0.90 × 10−9 27,513,640 3 0.11 × 10−8 2010 22,159,341 6 0.27 × 10−8 26,475,482 10 0.38 × 10−8 2011 22,039,745 12 0.54 × 10−8 25,957,089 13 0.50 × 10−8 2012 21,722,029 3 0.13 × 10−8 25,954,531 15 0.58 × 10−8 2013 21,642,083 4 0.18 × 10−8 25,855,562 16 0.62 × 10−8 2014 21,480,017 4 0.18 × 10−8 25,622,176 14 0.55 × 10−8 SUM 591,037,918 44 ퟎ. ퟕퟒ × ퟏퟎ−ퟗ 849,172,100 95 0.38 × 10−8 Table 23. Operations and Excursions in the United States from 1990 to 2014 [32]

As illustrated in Figure 25, runway excursion rates for both general aviation and commercial operations increased dramatically beginning in 2009.

0.7

0.6

0.5

0.4

0.3

0.2

0.1 Excursion Rates per Million Operations

0.0

1999 2011 1990 1991 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 2013 2014

Commercial Rate GA TWR Rate

Figure 25. Runway Excursion Rates per Million Operations

While the reason for this increase is unknown, one assumption may explain this increase; the influx of more, larger, or faster aircraft to smaller airports. Particularly for general aviation operations, the rate of runway excursions has been consistently greater than 0.5 excursions per million operations since

2010. As such, this study focused on the recent period from 2010 through 2014, illustrated in Table 24.

Commercial General Aviation Year RWY Excursion Operations RWY Excursion Operations (at towered airports) 2010 22,159,341 6 26,475,482 10 2011 22,039,745 12 25,957,089 13 2012 21,722,029 3 25,954,531 15 2013 21,642,083 4 25,855,562 16 2014 21,480,017 4 25,622,176 14 Total 109,043,215 29 129,864,840 68 Table 24. Operations and Excursions in the United States from 2010 to 2014 [32]

ANALYSIS: GA VS. COMMERCIAL EXCURSION RATES

The data presented in Table 24 illustrate that there may be a significant difference between the rate of excursions for general aviation and that of commercial aviation. As illustrated in Table 25, for example, the rate of a commercial aviation aircraft experiencing an excursion is 0.2659 per million operations, while the rate for GA, at towered airports, is 0.5362 excursions per million operations.

Operations Excursion Rate Type Excursions (millions) (per million operations) General Aviation 68 129.86 0.5362 (Towered airports) Commercial Aviation 29 109.04 0.2659

Overall 97 238.90 0.4060 Table 25. Commercial and General Aviation Excursion Rate Comparison in Towered Airports in the United States (2010-2014)

To investigate the statistical differences between the general aviation excursion probability and the commercial aviation excursion probability, an upper tailed Z test was conducted. The tested null hypothesis was that the general aviation and the commercial excursion probabilities are the same and therefore equal to the overall excursion probability (H0: 푝 = 푃퐺퐴 = 푃퐶푂푀). An alternative hypothesis was that the probability of GA excursion is higher than the overall probability of excursion per operation. If the Z score such that p-value is less than

0.05, the null hypothesis should be rejected at a 95% confidence interval.

H0: 푝 = 푃퐺퐴 = 푃퐶푂푀, H1: 푝 < 푃퐺퐴 (2)

Table 26 defines a series of variables to conduct this analysis.

Description Notation Value

GA excursions REGA 317

Commercial excursions RECOM 29

Total runway excursion REOverall = REGA + RECOM 346

GA operations NGA 503,474,695

Commercial operations NCOM 109,043,215

Total operations OPS = NGA + NCOM 612,517,910 Table 26. Variables and Values for Analysis of General Aviation and Commercial Aviation Runway Excursion Rate Difference

To test the null hypothesis, the probability to observe the actual number

(REGA = 317) or more excursions in general aviation (REGA ≥ 317) was calculated using a binomial distribution, the probability (푝) and the number of operation in GA (NGA). Under the null hypothesis that the probability of excursion in GA and the probability of excursion in commercial are equal, the probability of an excursion for an operation (푝) was calculated as a ratio of total runway excursions to the number of total operations (OPS) using the Equation (3).

RE 346 P = P = 푝 = Overall = = 5.6488 × 10−7 (3) GA COM OPS 612,517,910

Using a binomial distribution, the expected number of GA excursions in

GA (EGA) in NGA operation can be calculated as:

−7 EGA = NGA ∙ 푝 = 503,474,695 ∙ 5.6488 × 10 = 284.40 (4)

and the standard deviation (휎) can be calculated as:

휎 = √NGA ∙ 푝 ∙ (1 − 푝)

= √503,474,695 ∙ (5.6488 × 10−7) ∙ (1 − 5.6488 × 10−7) = 16.864 (5)

As the number of operations in general aviation is large enough, the binomial distribution was approximated by a normal distribution [33]. When the approximation is applied, by dividing the difference in the actual GA excursion and expected GA excursion (EGA) by the standard deviation (휎), the Z score is calculated as:

푥−푥̅ RE −E 317 −284.40 푍 = = GA GA = = 1.9329 (6) 휎 휎 16.864

Using the standardized normal distribution table, the probability of obtaining a Z value greater than or equal to 1.9329 is 0.9374. Therefore, under the null hypothesis, the probability to observe 317 or more excursions is 0.0266.

Since the value is less than 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Therefore, the probability of a GA excursion is significantly greater than the probability of commercial excursion.

ANALYSIS: EXCURSION RATES AT TOWERED VS. NON-TOWERED

AIRPORTS

A similar methodology was used to compare the statistical difference in the probability of GA excursion at towered airports and the probability of GA excursion at non-towered airports. As FAA does not have a combined database that contains all of the number of operations at each of the thousands of non- towered airports, an estimation of the total number of operations at non-towered airports was needed.

To determine the total operation number, an estimation methodology was developed by data of total non-towered operations in the State of Ohio (where

58 such data was found). Since FAA provides GA flown hour at towered airports in

2014 in each state, flown hours by state was used as an indicator of aviation activity. The assumption was that the number of operation proportionate to the flown hour and based on the FAA GA flown hour distribution, a ratio of flown hour in each state to the reference state flown hour could be calculated. Ratio by state was multiplied to the total non-towered GA operations. The sum of all state non-towered GA operations in each state was used as an estimation of non- towered GA operations in the United States for 2014. The five-years estimation was calculated by multiplying five to the 2014 estimation.

Ohio was used as a reference and the total number of operations at all non- towered GA airports in Ohio was counted and the list is attached as Appendix D.

[34] The total number of operations from 136 airports was 2,301,638 operations in

2014 at non-towered GA airports in Ohio. [34] [35] Based on the flown hour distribution in 2014 by states, each state flown hour ratio to Ohio flown hour could be calculated. [36] As the flown hour distribution can be an indicator that shows the level of general aviation activity in each state, calculated flown hour ratios were used to estimate the number of operation for each state. For example, the 2014 GA flown hour in California is about 3.03 times of the GA flown hour in

Ohio. Since the ratio is 3.03, it was multiplied to the number of operations at non- towered GA airports in Ohio, which is 2,301,638. Therefore, the operation estimation for the California is 6,964,622 per year. On the other hand, since the ratio of the GA flown hour in West Virginia is 0.10, the estimation for the West

Virginia was calculated as 233,965 (2,301,638 × 0.1017). A table represents all of the estimation for each state is attached in Appendix E. Based on the yearly

59 estimation, the total number of operations between 2010 and 2014 was calculated as 373,609,857 operations at non-towered airports in the United States.

Based on the estimated operations at non-towered airports and collected data of GA operations at towered airports from ATADS, runway excursion rates were calculated. Table 27 represents that the probability of excursion at non- towered general aviation is higher than the probability of excursion at towered airports.

Operations Excursion rate Type Excursions (millions) (per million operations) General Aviation 68 129.86 0.5362 (Towered airports) General Aviation 248 373.61 0.6638 (Non-towered airports) Table 27. General Aviation Excursion Probabilities Comparison in Towered and Non-towered airports in the United States [32] [34] [35]

The null hypothesis was that the towered GA and non-towered GA airports have the same excursion probability and the alternative hypothesis was that the non-towered GA airports have a higher probability of runway excursions than the towered GA airports.

H0: 푝 = PT = PNT, H1: 푝 < PNT (7)

Table 28 illustrates the runway excursion data at towered and non-towered

GA airports and used notation for the calculation.

Number of excursion or Description Notation operation

Non-towered GA excursions RENT 248

Towered GA excursions RET 68

Total GA excursions REGA = RENT + RET 317 Continued Table 28. Variables and Values for Analysis of Towered and Non-towered GA Airports Runway Excursion Rate Difference

Table 28 continued

Non-towered GA operations NNT 373,609,857

Towered GA operations NT 129,864,838

Total GA operations OPS = NNT + NT 503,474,695

Using a binomial distribution, the probability (푝) was calculated with the total number of excursions in general aviation (REGA) and the number of operation at all GA airports (OPS).

RE 317 P = P = 푝 = GA = = 6.2962 × 10−7 (8) NT T OPS 503,474,695

The expected number of GA excursions at non-towered airports (ENT) and the standard deviation (휎) in NNT operation can be calculated as:

−7 ENT = NNT ∙ 푝 = 373,609,857 ∙ 6.2962 × 10 = 235.23 (9)

휎 = √NGA ∙ 푝 ∙ (1 − 푝)

= √373,609,857 ∙ (6.2962 × 10−7) ∙ (1 − 6.2962 × 10−7) = 15.337 (10)

When the binomial distribution is approximated by a normal distribution, the Z score is calculated as:

RE −E 248 −235.23 푍 = NT NT = = 0.8324 (11) 휎 15.337

The probability of obtaining a Z value greater than or equal to 0.8324 is

0.7974. Since the probability to observe more excursions at non-towered GA airport is 0.2026, the null hypothesis is not rejected. Even though the null hypothesis is not rejected, it cannot be said that the presence of tower is irrelevant.

The rate of excursion at non-towered GA airport is higher than the towered airport rate. Other type of statistical test may be able to reveal the difference.

Excursion Probabilities under VMC and IMC

In commercial aviation, one of the most common causes of runway excursion is unfavorable weather conditions. According to a research conducted by Australian Transport Safety Bureau, weather factors were most frequently involved for runway excursion accidents. [37]

Figure 26. Runway Excursion Causal Factor in Commercial Landing [37]

Commercial flights are less restricted by the weather condition and frequently operated under instrument flight rules (IFR) that use various navigational aids during takeoff and landing under relatively poor weather conditions. On the other hand, visual flight rules (VFR) operations take a large portion of the general aviation that requires a clearer visibility and cloud condition. According to FAA, 84.6% (19,679,375 flight hours) of the general aviation flown hours were under VFR meteorological condition (VMC) condition

62 and just 15.6% (3,591,810 flight hours) were under IFR meteorological condition

(IMC). [37] VFR flights are conducted under clear conditions but still most of the excursions are in a general aviation environment. This may imply that a visibility and ceiling are not crucial for general aviation excursions. To confirm the higher probability of excursion in VMC condition, the number of operations and excursions under each meteorological conditions were considered.

For the reason that FAA does not provide the exact number of operation for each flight rules category, operation numbers needed to be estimated to compare the runway excursion probability per operation. The example estimation for the VMC operations and IMC operations in 2014 are presented in Table 29 based on the reported operation and flown hours in 2014. Used GA flown hour in

2014 data were searched from the 2014 FAA general aviation survey.

Total Operation 25,622,176 Operations in 2014 VMC 19,679,375 hrs Total flown hour IMC 3,591,810 hrs Total 23,271,185 hrs Flown hour per 23,271,185 ℎ푟푠 = 0.908 ℎ푟푠/표푝푠 = 54.49 푚𝑖푛/표푝푠 operation 25,622,176 표푝푠 19,679,375 ℎ푟푠 VMC = 21,667,500 표푝푠 0.908 ℎ푟푠/표푝푠 Operation estimation 3,591,810 ℎ푟푠 IMC = 3,954,676 표푝푠 0.908 ℎ푟푠/표푝푠 Table 29. VMC and IMC Operation Estimation in 2014 [36]

With the same methodology explained above, number of VMC and IMC operations for each year could be calculated. Used flown hour data and full calculation is attached in Appendix G. Table 30 represents estimated operation numbers and runway excursion accidents from the collected dataset.

VMC IMC Year Operations RWY Excursion Operations RWY Excursion 2010 23,120,736 9 3,354,746 1 2011 22,531,465 12 3,425,624 1 2012 22,390,462 14 3,564,069 1 2013 22,029,409 15 3,826,153 1 2014 21,667,500 13 3,954,676 1 Total 111,739,571 63 18,125,269 5 Table 30. Number of Operations and Runway Excursions in VMC and IMC

Based on the estimation, general probability of runway excursions under

VMC and IMC are calculated as in Table 31.

Operations Excursion rate Excursions (millions) (per million operations) VMC 63 111.74 0.5638 IMC 5 18.13 0.2759 Table 31. Runway Excursion Probabilities in GA VMC and IMC Operations

Runway excursion rates of VMC flights in Table 36 is double than IMC flights. To test the statistical difference between two excursion probabilities, the excursion probability comparison of VMC and IMC conditions was conducted using the same methodology as previous hypothesis test, but only using GA data at towered airports. Under the null hypothesis that the probability of excursion in

VMC is same as the excursion probability in IMC, the likelihood to observe more excursion than the actual VMC excursion was calculated.

H0: 푝 = PVMC = PIMC, H1: 푝 < PVMC (12)

The variables and data in Table 32 were used to test the null hypothesis.

Description Notation Value

GA VMC excursions REVMC 63 GA IMC excursions REIMC 5 Total GA excursions REGA = REVMC + REIMC 68 GA VMC operations NVMC 111,739,571 GA IMC operations NIMC 8,125,269 Total GA operations OPS = NVMC + NIMC 119,864,840 Table 32. Variables and Values for Analysis of GA VMC and IMC Runway Excursion Rate Difference

The probability (푝) was estimated as the ratio of the overall excursions

(REGA) to the estimated total operations (OPS).

RE 68 P = P = 푝 = GA = = 5.6731 × 10−7 (13) VMC IMC OPS 119,864,840

The expected VMC excursion (EVMC) is calculated using the number of

VMC operations and the probability (푝) in Equation (14). The standard deviation

(휎) can be calculated using the same variables as Equation (15).

−7 EVMC = NVMC ∙ 푝 = 119,864,840 ∙ 5.6731 × 10 = 63.39 (14)

휎 = √NVMC ∙ 푝 ∙ (1 − 푝)

= √119,864,840 ∙ (5.6731 × 10−7) ∙ (1 − 5.6731 × 10−7) = 7.962 (15)

The Z score is calculated as Equation (16) when the binomial distribution is approximated by a normal distribution.

RE −E 63 −63.39 푍 = VMC VMC = = −0.0490 (16) 휎 7.962

The probability of obtaining a Z value greater than or equal to -0.0490 is

0.4804. Since the probability to observe more excursions at non-towered GA airport is 0.5196, the null hypothesis is not rejected. As the null hypothesis is not rejected, two probabilities are it is not able to determine that the VMC excursion probability and the IMC excursion probability are not same. Even though the null hypothesis is not rejected, the rate of excursion in VMC is higher than the IMC with much higher number of operations under visual meteorological condition. As the general aviation has a much more flight under VMC, consideration of the

65 same weather variables in previous commercial excursion models may not be realistic for general aviation excursion.

Excursion Probabilities in the Perspective of Runway Dimension

To examine the effect of runway dimension, the runway length and width were categorized into several groups. For runway dimension grouping, the data of runways in Ohio were compiled and analyzed since the FAA does not offer combined database of the entire runways in the United States. Information of total

154 runways in Ohio was used to examine the effect of runway dimensions. When an airport has multiple runways, the longest runway information was chosen because airports do not report operation numbers by runway. To effectively analyze the runway dimension effect, both runway lengths and widths were divided into four groups. Following Table 33 shows the categories for each component.

Length Group Width Group Length ≤ 2500ft Width ≤ 75ft 2500ft < Length ≤ 5000ft 75ft < Width ≤ 100ft 5000ft < Length ≤ 10000ft 100ft < Width ≤ 150ft 10000ft < Length 150ft < Width ≤ 200ft Table 33. Runway Length and Width Categories

The number of runways, the sum of yearly operations of corresponding airports, the estimation of five years of operations (from 2010 through 2014) and the number of excursions on corresponding dimension runway are stated in following Table 34. All the considered airports and runways are represented in

Appendix F.

RWY Length (ft) RWY Width (ft) ퟐퟓퟎퟎ < 퐋 ퟓퟎퟎퟎ < 퐋 Total 퐋 ≤ ퟐퟓퟎퟎ ퟏퟎퟎퟎퟎ < 퐋 ≤ ퟓퟎퟎퟎ ≤ ퟏퟎퟎퟎퟎ RWYs 9 75 4 0 88 퐖 Total Ops 151,994 1,355,587 98,074 0 1,605,655 ≤ ퟕퟓ 5 yrs Ops 757,470 6,777,935 490,370 0 8025775 Excursions 1 4 0 0 5 RWYs 5 18 19 0 42 ퟕퟓ Total Ops 15,957 318,780 810,769 0 1,281,506 < 퐖 ≤ ퟏퟎퟎ 5 yrs Ops 79,785 1,593,900 4,053,845 0 5,727,530 Excursions 0 0 1 0 1 RWYs 4 6 9 4 23 ퟏퟎퟎ Total Ops 16,378 76,154 418,093 216,251 726,876 < 퐖 ≤ ퟏퟓퟎ 5 yrs Ops 81,890 380,770 2,090,465 1,081,255 3,634,380 Excursions 0 0 0 1 1 RWYs 0 0 0 3 3 ퟏퟓퟎ Total Ops 0 0 0 37,035 37,035 < 퐖 5 yrs Ops 0 0 0 185,175 185,175 Excursions 0 0 0 0 0 RWYs 18 99 32 7 156 Total Ops 320,329 1,750,521 1,326,936 235,286 3,651,072 Total 5 yrs Ops 919,145 8,752,605 6,634,680 1,266,430 17,572,860 Excursions 1 4 1 1 7 (Ops: Operations, L: Length, W: Width) Table 34. Runway Dimension and Runway Excursion Distribution in Ohio from 2010 to 2014 [34] [35]

RWY Length (ft) RWY Width ퟐퟓퟎퟎ < 퐋 ퟓퟎퟎퟎ < 퐋 Total (ft) 퐋 ≤ ퟐퟓퟎퟎ ퟏퟎퟎퟎퟎ < 퐋 ≤ ퟓퟎퟎퟎ ≤ ퟏퟎퟎퟎퟎ 퐖 ≤ ퟕퟓ 1.3202 0.5902 0 0 0.6230 ퟕퟓ < 퐖 ≤ ퟏퟎퟎ 0 0 0.2467 0 0.1746 ퟏퟎퟎ < 퐖 0 0 0 0.9249 0.2752 ≤ ퟏퟓퟎ ퟏퟓퟎ < 퐖 0 0 0 0 0 Total 1.0880 0.4570 0.1507 0.7896 0.3983

Total Operations 17,572,860

Total Excursions in Ohio 7 Probability of Excursion 0.3983 (per million operations) Table 35. Rate of Excursion per Million Operations for Each Group of Runway Dimension

According to the summary Table 35, the dimension group that has the largest number of runways is the runway length 2,500ft < Length ≤ 5,000ft and

67 the runway width Width ≤ 75ft. The category also has the largest number of operations with an appropriate dimension for general aviation flight. However,

Table 31 represents that the runway dimension of the length greater than

Length ≤ 2,500ft and the width group Width ≤ 75ft has the highest probability of excursion per million operation. It is about 3 times higher than the general excursion rate in Ohio, which is calculated by the total number of operations and the number of runway excursions during five years. Therefore, narrower runways with less than 75ft of width are more likely to have runway excursions than wider runways.

A Z test was conducted to investigate the difference in the smallest runway group probability and the likelihood of runway excursion in Ohio. A same methodology was applied using the null hypothesis that the probability of excursion in the smallest runway group is same as the excursion probability in the

State of Ohio.

H0: 푝 = PSmall = POH, H1: 푝 < PSmall (17)

The variables and data in Table * were used to test the null hypothesis.

Description Notation Value

Smallest RWY excursions RESmall 1 Total excursions in OH REOH 7 Smallest RWY operations NSmall 757,470 Total operations in OH NOH 17,572,860 Table 36. Variables and Values for Analysis of Runway Excursion Rate Difference in the Smallest Runway Group and the General Excursion Rate

The probability (푝) was estimated as the ratio of the overall excursions

(REOH) to the estimated total operations (NOH).

RESmall 1 −7 PSmall = POH = 푝 = = = 3.9834 × 10 (18) NOH 17,572,860

The expected excursions in the smallest runway group (ESmall) is calculated based on the probability (푝) and the number of operations at the smallest runway group as:

−7 ESmall = NSmall ∙ 푝 = 757,470 ∙ 3.9834 × 10 = 63.39 (19)

And the standard deviation (휎) is calculated as:

휎 = √NSmall ∙ 푝 ∙ (1 − 푝)

= √757,470 ∙ (3.9834 × 10−7) ∙ (1 − 3.9834 × 10−7) = 0.549 (20)

The Z score is determined using the difference between the actual and the estimate number of excursions at the smallest runway group and the standard deviation as:

RE −E 1−0.30 푍 = Small Small = = 1.271 (21) 휎 0.549

Since the number of operations at non-towered airports is large, the binomial distribution can be approximated by a normal distribution. According to the standardized normal distribution table, the probability of obtaining a Z value greater than or equal to 1.271 is 0.8982. Since the probability to observe more excursions at smaller runway GA airport is 0.1018, the null hypothesis is not rejected at a 95% confidence level, but may be rejected at a 90% confidence level.

As summary, the runway excursion rate is higher in general aviation environment specifically when they are operated in airports that are not equipped with an air traffic control tower. This may imply that the importance of ATC

69 tower in general aviation since air traffic controllers are able to provide appropriate traffic information or weather advices in timely manner. In general,

GA pilots rely less on instrument and the effect of ATC advisory is greater than in commercial operations. In the case of an aircraft operates on a narrower runway, a pilot may have less opportunity to correct the heading of an aircraft when it deviates from the runway centerline. An insufficient visibility and cloud condition may not be a causal factor for GA excursion as much as in the commercial excursions since large number of flights is operated under VFR meteorological condition on a relatively smaller runway than commercial runways.

Chapter 3. Empirical Study of Runway Centerline Deviation

Based on the findings in Chapter 2, a general aviation focused runway excursion frequency model is needed since the characteristics that determine the risk of runway excursions for general aviation operations seem to differ from that of commercial operations. This chapter describes research performed to further understand the operational behavior of general aviation aircraft as it relates to their associated risk of runway excursions.

Runway excursions tend to begin with an aircraft deviating from the centerline of a runway during takeoff or landing. If the pilot does not sufficiently correct this deviation, a runway excursion is likely to occur. Thus, it is important to understand the behavior of aircraft during takeoff and landing. If observations of slight deviations from runway centerline can be observed, a greater understanding of the potential for runway excursions may be gained.

This chapter describes an empirical analysis consisting of monitoring trajectories along the runway of general aviation operations during takeoff and landing under normal conditions, whose results may provide further understanding of runway centerline deviation and subsequent correction.

3.1. Data Collection Location [34]

To data, the Ohio State University Don Scott Airport was selected as a data collection location. The airport is a publically owned by the Ohio State

University, located 10mi northwest of Columbus downtown in the state of Ohio. It is designated as a reliever airport in the National Plan of Integrated Airport

Systems (NPIAS) and serves mostly light general aviation aircraft and corporate aviation operations. As of in May 2016, the number of based aircraft is 158 and more than 71,000 operations were reported in 2014.

The OSU airport operates three runways and one helipad. All of the runways have 100ft of width and the longest runway is runway 9R/27L with length of 5,004ft. Parallel runway 9L/27R is 2,994ft of length. The runway 5/23 crosses the runway 9R/27L with the length of 3,562ft. Data was collected by observing the trajectories of aircraft taking off and landing on runway 9L/27R.

Figure 27. The Ohio State University Airport [39]

3.2. Sensing Method and System

A LiDAR (Light Detection And Ranging) system of sensors was chosen to detect movements on the runway. LiDARs use laser light instead of radio frequency and measure the time, direction and distance to an object by sensing reflection signal. Various types of LiDARs are available in the market and three different LiDARs from three companies were evaluated. A group of geodetic science researchers at the Ohio State University conducted the data acquisition in various aspects; number of points per aircraft speed, accuracy of point cloud and data acquisition by scan angle change. As a result, the VLP-16 manufactured by

Velodyne was selected as data acquisitions sensor.

A VLP-16 sensor is also called LiDAR “puck” because it is as small as two stacked hockey pucks. Figure 28 illustrates a single unit of VLP-16.

Figure 28. A Single VLP-16 LiDAR Sensor Unit [40]

In the middle of the sensor, 16 layers of lights sweeps 360 degrees horizontally and 30 degrees vertically. With the high accuracy, about 3cm, signal from the sensor can reach up to 100m, which is sufficient to study deviation from

73 runway centerline. To expand the field of view, sensors were mounted on frame, which is used for runway edge light, and scan surrounding about 50cm above the ground surface. [40] Since a sensor is small and light enough, it was able to be installed on fragile frame used as runway edge light. Figure 29 shows the installed

LiDARs on two runway edge light frame.

Figure 29. LiDAR Sensors Mounted on Runway Edge Light Frames [41]

Four scanners were separated into two groups and located in two different locations with 40m of separation between them. In the Figure 29 two sensors on left side have horizontal axes facing different direction with 30 degrees between each sensor. The right side group has different axes between each scanner; one scanner is located on the top and the other scanner is attached on side. The top sensor has vertical axis and scans the largest area of the runway among four sensors. The other LiDAR has horizontal axis as the other group. There was no fixed order of capturing aircraft between scanners. In some data collections, the scanner 1 recorded the earliest point clouds and the scanner 3 and 4 captured the

74 latest movement of an aircraft. In other collections, the frame with vertical axis was located closer to the runway threshold and the other frame was placed 40m away from the vertical sensor frame.

Figure 30. Scanned Range of Each Sensor (Not to scale) [41]

Four sensors were located on the side of the runway and properly mounted on temporarily installed frames, additional equipment are required to supplement collected data; GPS antenna, GPS receiver, sensor interface box, battery to provide enough power and logging laptop to control the sensors and save collected data. Figure 31 shows the sensor system setting.

Figure 31. Sensor System Configuration [42]

A GPS antenna and receiver was needed to record the accurate time of the sensor’s reflected signal and coordinates of the data collection station. The sensor interface box is a connection between the sensor and the laptop. Figure 32 shows the entire view of installed sensor system detecting landing aircraft on the side of the runway.

Figure 32. Installed Sensor System Detecting a Landing Aircraft

3.3. Data collection

By using the sensors, the data of scanned aircraft movements were collected from two different locations; vicinity of the runway 9L threshold for the first time and beginning section of the runway 27R. Because a runway is used both sides, the runway 9L and runway 27R are basically same runway but using different directions. The red marked runway is the runway 9L/27R and data collection stations are marked on Figure 33.

Figure 33. Data collection location [39]

The first data collection location is marked in red colors and the blue colored markings shows the second data collection location. Approximately 25 aircraft were collected from the first time and 36 aircraft were recorded at the second location.

From the second data collection, blue colored locations in Figure 33 was chosen as sensor location. Since the purpose of second data collection was estimation of aircraft altitude during landing using collected point cloud, sensor location was closer to runway threshold than the first data collection. As a result, detected aircraft were approaching and still in the air, not on the runway.

However, as each sensor is able to record three dimensional information, based on

X, Y and Z axis, relative projected aircraft trajectory on the runway could be calculated.

3.4. Data Analysis

Aircraft Reconstruction

The theoretical accuracy of the sensor was 3cm, however, less than 10 cm level of accuracy was obtained because of the distance between the sensor and aircraft. An example of raw data point cloud is attached as Figure 34.

Figure 34. An example of raw point cloud from the sensor [43]

Different colors illustrate the time difference among each point cloud. The aircraft is proceeding from the upper right corner to the left down side. The earliest signal captured by a scanner is presented in red and the movement of aircraft is demonstrated in order of purple, cyan, green, black and yellow colors.

Raw dataset from the sensor system was processed to create point cloud images of moving aircraft by the research group of geodetic science at the Ohio State

University. The data reconstruction process will not be included in this thesis research and the refined example of an aircraft is presented in Figure 35 and

Figure 36.

Figure 35. Refined example aircraft – A small propeller aircraft [44]

Figure 36. Refined example aircraft – A jet engine aircraft [44]

Figure 37. Refined and combined aircraft image from four scanners [41]

The Figure 37 is an example of refined image from four different scanners for one landing aircraft. Because scanning range of the scanner 1 was closest to the runway threshold, the aircraft’s landing gear is not yet landed. But points from the scanner 2 through 4 show that landing gears touched down the runway.

Figure 38. Detection Range by LiDAR Sensors

Figure 38 illustrates the detection range. Since the raw data has its own local coordinate system, detected aircraft coordinates were normalized. LiDARs recorded the longitudinal distance in X coordinates and the vertical distance between the runway centerline to an aircraft in Y coordinates. The reference point

(0, 0) is the intersection of 290 feet from the runway threshold and the runway centerline. The reference point was determined by the closest observation point to the runway threshold. The field of view of the horizontal distance was determined by the detected point that has the farthest point from the runway threshold. The difference between the closest X coordinate and the farthest X coordinate was 362 feet, the detected range of the LiDAR system.

Aircraft Trajectories

0 0 30 60 90 120 150 180 210 240 270 300 330 360

-1 centerline centerline (ft)

-2 Lateral distance from Lateral distance from the -3

-4 Longitudinal distance (ft)

Figure 39. Combined Aircraft Trajectories

Figure 39 represents combined trajectories of 18 landing aircraft.

According to the figure, most of the aircraft maintained their track close to the centerline and all of the aircraft did not exceed more than 4 feet from the reference line.

The first hypothesis for the aircraft deviation was that as an aircraft that touches down farther point laterally from the centerline, the aircraft makes a greater correction than the aircraft that lands closer to centerline. To investigate the assumption, each aircraft movement was analyzed based on their touchdown point, average level of deviation, standard deviation and the slope of the correction trend. Table 37 explains observed parameters from 18 aircraft. The touchdown point and the end point includes the normalized coordinates of the first touchdown point and the last detected observation point. The “Avg Y” means the

81 average of lateral distance; between the runway centerline and the aircraft location. When the value is negative, the aircraft was located on the right side of the centerline and the aircraft general moved left towards the centerline if the average is positive. For an intuitive comparison, the vertical deviation was also analyzed in absolute value, which is represented in column “Avg |Y|”. Different from using the raw distance, in absolute value, larger value means more divergence from the runway center. The “Avg Slope” means the average of ratio between the change in vertical deviation to the change in horizontal distance. The slope explains the ratio of the change in the vertical distance to the change in the horizontal distance. The sign of the slope can be used to predict the aircraft movement with the initial touchdown point. When the absolute value of the slope is large, it implies the pilot more adjusted the aircraft heading as the aircraft moves toward the end of the runway.

TD Point (ft) End Point (ft) Observated Observation Avg Y Avg |Y| Aircraft ID Std.Dev Avg Slope Avg |Slope| Trajectory Movement X Y X Y Points Distance (ft) (ft) (ft) Aircraft 01 34.248 -0.849 350.333 -0.245 21 316.085 -0.860 0.860 0.273 0.002 0.004 One side Closer Aircraft 02 80.171 -0.276 342.669 -0.786 19 262.498 -0.638 0.638 0.158 -0.002 0.003 One side Farther Aircraft 03 23.677 2.849 355.425 -0.701 29 331.748 0.569 0.863 1.061 -0.011 0.011 Cross CTL Closer Aircraft 04 54.735 1.418 352.957 1.022 20 298.222 0.955 0.955 0.311 -0.001 0.005 One side Closer Aircraft 05 97.578 -0.060 332.992 -0.198 11 235.414 0.283 0.337 0.312 -0.004 0.006 Cross CTL Farther Aircraft 06 0.000 -0.375 353.412 0.047 35 353.412 -0.394 0.397 0.194 0.001 0.003 Cross CTL Closer Aircraft 07 45.722 -0.199 354.329 -0.402 21 308.607 -0.558 0.558 0.185 0.003 0.003 One side Farther Aircraft 08 77.800 -1.030 349.334 -0.186 19 271.533 -0.650 0.650 0.284 0.003 0.003 One side Closer Aircraft 09 74.729 -1.427 348.969 -2.440 19 274.240 -1.760 1.760 0.353 -0.004 0.004 One side Farther Aircraft 10 36.552 1.799 360.270 -0.042 21 323.718 0.641 0.649 0.585 -0.006 0.006 Cross CTL Closer Aircraft 11 70.077 -0.345 354.336 1.100 17 284.259 0.680 0.752 0.506 0.005 0.005 Cross CTL Farther

83 Aircraft 12 40.682 3.236 356.804 2.531 22 316.122 2.593 2.593 0.276 -0.002 0.004 One side Closer

Aircraft 13 23.863 3.046 357.959 1.422 29 334.096 2.492 2.492 0.608 -0.005 0.006 One side Closer Aircraft 14 59.975 1.486 359.048 1.710 18 299.072 1.388 1.388 0.136 0.001 0.003 One side Farther Aircraft 15 68.571 -1.647 351.295 0.297 18 282.723 -0.785 0.841 0.667 0.007 0.007 Cross CTL Closer Aircraft 16 72.008 -0.062 357.661 0.136 18 285.653 0.176 0.187 0.132 0.001 0.003 Cross CTL Farther Aircraft 17 50.284 -0.177 361.269 0.004 21 310.985 -0.508 0.508 0.278 0.001 0.005 Cross CTL Closer Aircraft 18 47.697 -1.778 354.083 -2.227 19 306.386 -2.434 2.434 0.326 -0.001 0.006 One side Farther Closer: 10 Average 53.243 0.312 352.952 0.058 21 299.710 0.066 1.048 0.369 -0.001 0.005 N/A Farther: 8

(TD Point: Touchdown point, Std.Dev: Standard Deviation) Table 37. Aircraft Average, Standard Deviation and Slope of Deviations

If the sign of the average slope is same as the Y coordinate sign of the touchdown location, it may imply that the aircraft moved farther from the runway centerline. On the other hand, if the sign of the average slope and the touchdown location Y coordinate do not match each other, the aircraft might change its heading closer to the center. However, if an aircraft precisely landed on the runway but has a slope, it can lead that the aircraft moving farther from the centerline.

0 0 30 60 90 120 150 180 210 240 270 300 330 360 -1

-2

-3

-4

Lateral deviation from Lateral centerline deviation from the (ft) Longitudinal distance (ft)

Aircraft 01 Aircraft 03 Aircraft 09

Figure 40. Trajectories of Aircraft 01, Aircraft 03 and Aircraft 09

For example, Figure 40 shows three trajectories of aircraft 01, aircraft 03 and aircraft 09. Since the aircraft 01 landed on the right side of the centerline

(negative Y coordinate at the touchdown point) and a positive slope, the aircraft traveled closer toward the runway centerline. Aircraft 03 has an initial positive Y coordinate at touchdown and a negative slope, it also moved closer to the runway centerline. However, for the aircraft 09, since the Y coordinate sign and the slope sign matches each other, the aircraft ended up at a farther point from the runway centerline.

Figure 41 illustrates the average absolute distance from centerline and its associated standard deviation. It also represents the difference of average absolute slope.

Avg|Y| (ft) |Y| Std.Dev Avg |Slope|

3.0 0.030

2.5 0.025

2.0 0.020

1.5 0.015

1.0 0.010

Absolute Absolute lateral deviation (ft) 0.5 0.005

0.0 0.000

Figure 41. Average and Standard Deviation of Absolute Deviation

According to Figure 41, aircraft 09, aircraft 12, aircraft 13 and aircraft 18 showed more deviation than other airplanes. Among four aircraft, aircraft 13 and aircraft 18 also has relatively high standard deviation and average of absolute slope. This means they were more strayed from the runway centerline and also had greater correction than other aircraft. On the other hand, aircraft 03 and aircraft 15 landed and remained closer to the runway centerline, however, standard deviations and averages of absolute slope are higher than other aircraft. It can be interpreted that they did not significantly deviated from the centerline but trajectories may not be straight line.

As illustrated in Table 38, aircraft that landed within 2ft point from the centerline have not only smaller vertical deviation distance, but also small average of standard deviation. Including the higher average slope, it is clear that the aircraft that touches closer point to the runway centerline do not make change as much as other aircraft that touches down farther points.

Avg. Abs Avg. Avg. Touchdown Point Avg. Y Avg. |Y| T/D Std.dev |Slope| Touchdown within 1ft 0.293 -0.227 0.530 0.228 0.004 Touchdown within 2ft 1.512 -0.470 1.240 0.368 0.005 Touchdown more than 2ft 3.044 1.885 1.983 0.571 0.007 Table 38. Average, Standard Deviation and Average Slope of Absolute Deviation

Horizontal Distance Sections

The other perspective to analyze the aircraft deviation is to focus on each segment of runway horizontal distance. The distribution on each distance segment was investigated to examine the hypothesis that closer distances to the touchdown have greater deviations and deviations are decreased as the aircraft moves toward the end of the runway. To categorize horizontal distance sections, original X coordinates ranges were assigned for each distance section and arbitrary distances were determined to illustrate intuitively. As each aircraft did not record common

X coordinates, arbitrary distances were assigned based on each X coordinate range of the raw data. Figure 42 illustrates the distance sections.

Figure 42. Distance Sections of LiDAR Detection Range

Table 39 presents the X coordinate ranges, arbitrary distances, average deviations, average absolute deviations, standard deviations of deviations and average of slopes for each distance sections. Figure 43 illustrates distribution of vertical deviation and average deviations along the horizontal distances. Since each aircraft does not have common X coordinates, the entire horizontal distance was separated into 25 sections and normalized X values were assigned for each section.

Normalized Avg |Y| Std. Avg Avg Aircraft ID Avg Y (ft) X (ft) (ft) Dev Slope |Slope| Section 1 7 -0.388 0.388 0.019 -0.0024 0.0024 Section 2 22 1.820 2.110 1.955 -0.0029 0.0029 Section 3 36 1.560 2.000 1.792 -0.0077 0.0077 Section 4 50 0.676 1.445 1.717 -0.0068 0.0077 Section 5 65 0.470 1.357 1.654 -0.0052 0.0073 Section 6 79 0.177 1.469 1.778 -0.0060 0.0065 Section 7 94 0.160 1.144 1.480 -0.0038 0.0051 Section 8 108 0.216 1.171 1.476 -0.0031 0.0052 Section 9 122 -0.062 1.161 1.476 -0.0021 0.0060 Section 10 137 0.068 1.157 1.436 -0.0012 0.0059 Section 11 151 0.212 1.336 1.642 -0.0032 0.0054 Section 12 166 -0.064 1.098 1.369 -0.0023 0.0056 Section 13 180 -0.030 1.134 1.393 -0.0015 0.0052 (Avg.Dev: Average Deviation, Avg Abs Dev: Average Absolute Deviation, Std.Dev: Standard Deviation) Continued Table 39. Deviation by Horizontal Distance Sections

Table 39 continued Section 14 194 0.009 0.989 1.287 -0.0003 0.0036 Section 15 209 0.097 1.113 1.429 0.0008 0.0038 Section 16 223 0.150 0.894 1.134 -0.0005 0.0034 Section 17 238 -0.104 1.021 1.330 -0.0003 0.0046 Section 18 252 -0.026 0.923 1.235 -0.0002 0.0048 Section 19 266 -0.028 0.914 1.235 -0.0003 0.0044 Section 20 281 0.057 1.028 1.366 0.0004 0.0040 Section 21 295 -0.078 0.800 1.166 0.0000 0.0042 Section 22 310 0.159 0.758 1.093 0.0002 0.0048 Section 23 324 -0.069 0.810 1.182 0.0011 0.0047 Section 24 338 0.181 1.001 1.338 0.0003 0.0042 Section 25 346 0.298 0.805 1.137 0.0013 0.0039

0 0 30 60 90 120 150 180 210 240 270 300 330 360 -1

-2 Lateral Lateral deviation distance (ft)

-3 Avg Y (ft)

-4 Longitudinal distance (ft)

Figure 43. Deviation Distribution along the Runway Horizontal Distance

In Figure 43, the average deviation point converges to the centerline after the fifth section (65 feet point from the reference). Since the reference point is

290ft from the runway threshold, not all of the aircraft necessarily land on the exact 290ft point as most of the general aviation aircraft try to touchdown between

300ft and 500ft section. As the number of observation increases, the average vertical deviation moves closer to the runway centerline.

Unfortunately, different from the hypothesis, the deviation tendency does not converge to the centerline as aircraft move down. Generally required distance for general aviation aircraft is 1,500ft and the detection range was 360ft segment from the touchdown. The studied field covers only 20% of the regular landing distance, especially the beginning phase of the landing. The insufficient field does not explain the entire behavior of aircraft. Therefore, the inconsistent pattern of deviation may imply that pilots start to wiggle for heading adjustment.

Even though the collected dataset does not represent converging movement, most of the averages of strayed distance are within three feet deviations from the centerline except for two sections, the 108ft and 115ft sections. It indicates aircraft are able to touch down adjacent to the runway centerline.

The slope can be used to explain how much a deviation was changed compare to the change in horizontal distance. Figure 44 represents averages of absolute slope. Since the figure considers absolute value, a greater slope means there was a greater change in vertical deviation for given runway distance.

0.009 0.008 0.007 0.006 0.005 0.004

0.003 Absolute Absolute Slope 0.002 0.001 0.000 0 30 60 90 120 150 180 210 240 270 300 330 360 Longitudinal Distance (ft)

Figure 44. Average of Absolute Slope

As the change of absolute slope has clearly decreasing tendency compare to the beginning segment of the runway, aircraft do not tend to adjust their heading as they move toward the end of runway.

When the slope figure is considered with the deviation distribution, aircraft deviations do not converge to the centerline in the beginning phase of landing roll.

On the other hand, in the perspective of the changing rate, pilots less adjust their heading as they move down to the runway end. The decreasing slope may imply

Trajectory Estimation based on Velocity Estimation

From the second data collection data, as previously mentioned, the sensor system was located closer to the runway threshold and collected point clouds did not contact on runway surfaces. Based on the three-axis analysis, trajectories of landing gear, especially nose gear, were able to project. As the velocity and altitude were focused, the Figure 45 presents the change of velocity. The red marked curve means the velocity change of horizontal location change, which

90 means if there is no change in aircraft direction, the velocity becomes 0 m/s. The green curve means the altitude of aircraft and the blue data is lateral speed of aircraft during landing.

Figure 45. Velocity change of landing aircraft with respect to horizontal, lateral and vertical movement [45]

Since the data collection location was close to the runway threshold, the collected point cloud presents that the aircraft is in the air. The green curve in

Figure 45 shows the change in height and the value is greater than 0 until 2.5 second point. As the aircraft is still approaching in the air, the horizontal direction also shows some change in speed that implies pilots try to adjust aircraft heading.

After aircraft touch down on runway surface, overall velocity in directional change decreases and become 0 m/s. This can be interpreted that during and right after the touchdown aircraft make oscillations to adjust the direction, however, after few seconds from the initial phase of landing roll, the directional velocity is close to 0 m/s. This result is same as the previous result from first data collection.

As a summary, most of the aircraft are able to stay on closer to the centerline. Pilots tend to stray from the centerline after touchdown, however, the

91 deviation distance is not significant more than 3ft. If an aircraft touched down farther point from the centerline, the aircraft tend modify their heading more than aircraft that landed on closer points. As aircraft move down toward the end of runway, pilots less adjust their heading and try to maintain their track at least to be straight. Therefore, when an aircraft fails to properly land on the closer point to the center, it is likely to cause runway excursion. If the degree of correction is dependent on the first location of touchdown, the first grounding point will be much important to aircraft that have wide wingspan.

By collecting plenty of dataset and investigating aircraft trajectories will help to figure out the critical level of deviation from the centerline that may cause excursion. Also, aircraft movement patterns can be used to modify airport design standards towards reducing the risk of huge deviation.

Chapter 4. Conclusion

As shown in previous chapters, although airport operators and aviation authorities attempt to offer safe runway environments, large number of accidents occur on a runway and runway excursions are the most common type of accident.

Collected excursion data reveals that general aviation excursions occur under different environments from commercial aviation excursions. Previous research developed runway excursion risk estimation models, however, these models were focused on excursions in commercial aviation environment. It was investigated that general aviation excursions risk is statistically higher than commercial excursion risk. This supports that it is necessary to conduct a research for GA excursion.

Another finding of this study is that the visibility and ceiling may not contribute to GA runway excursions as much as they do for commercial excursions. Previous research considered various weather factors as their important estimation variable such as visibility and precipitation. However, the ratio of risk of excursion in VMC is higher than the ratio of excursion in IMC for general aviation. The finding that large numbers of GA excursion occur under good weather conditions represents that weather factors may not be critical in general aviation excursion as crucial as for commercial excursions.

This may be because GA aircraft are mainly conducted for personal purpose and pilots tend to avoid poor weather condition. In general, much more flights operate with good visibility, ceiling and precipitation condition.

Since previous models considered weather factors significant, it may not be realistic using the previous models for general aviation without modification.

Another model is needed for general aviation that contains more related factor for

GA excursions.

One of the largest differences between general aviation and commercial aviation is the presence of air traffic control tower at airports. Large numbers of

GA airports do not have a tower. By comparing the risk of excursion at GA towered airports and the excursion risk at GA non-towered airports, non-towered airports are more likely to experience a runway excursion. It is assumed because an air traffic control tower can provide an exact information of traffic situation and wind information in the airspace. The latest data of the airspace may significantly affect pilots to make a better decision and helps to prepare more stabilized landing. Moreover, non-towered GA airports usually operate single runway that provides a limited option of runway direction for unfavorable wind condition such as crosswind or tailwind. Therefore, a presence of ATC tower can be a critical factor for GA excursion that was not considered in the previous models for commercial excursions.

The runway geometry can be another significant factor for general aviation excursions. The runway excursion probability analysis revealed that narrower runways may prone to experience excursions than wider width runways. Since

94 veer-offs are defined as a deviation toward side of the runway, a wider width may provide more chance to correct the aircraft heading within the runway boundary.

Therefore, runway dimension, especially a width of runway can be another estimator for GA excursion.

Analysis of landing aircraft trajectories investigated how much an aircraft deviate from a runway centerline. Most of detected aircraft did not deviate significantly from the runway centerline while staying within the 4ft distance from the center. Instead of a detection of an actual runway excursion, some patterns were observed from collected trajectories. Deviations from runway centerline are likely to occur in the early phase of landing, which is right after a touchdown.

Pilots tried to adjust the aircraft heading after a touchdown. However, sometimes the deviation was overly corrected and made more deviation while the heading correction. After oscillations, aircraft made less correction in the later stage of landing. Also, aircraft that had touched down farther location from the runway centerline had larger average deviation distances. When an aircraft touched down farther, pilots apply more corrections that may result more deviations. Therefore, the first touchdown point may be another important factor for excursion estimation. The first touchdown point can be the most important segment that differentiates normal operation and runway excursion.

Not only for the aspects listed above, weight of an aircraft or the skill of pilot also can be significant factors for general aviation excursions. Although the aircraft weight was not considered as a variable in this study, most general aviation aircraft are much lighter than commercial aircraft. If an aircraft weight is

95 light, it is easily affected by the wind change. Also, there are more pilots who are less trained than commercial aviation. One of the largest portion of general aviation is training flights. As it was investigated as the heading correction may be a contributor, it is more likely to have a mistake such as missed touchdown or over correction by less-skilled pilots. This study focuses on the investigation of the difference between the GA excursion and commercial excursion probabilities and part of the possible variables based on different environments to notify the need of research for GA excursions. Consideration of more various factors that are different aspects from the commercial aviation would be helpful to develop a proper model for general aviation excursions.

Not only focusing on the environments of excursions, it may imply the need for evaluation of runway design standards that were developed decades ago.

As previously mentioned, there was no significant update in runway design standard except for the new large aircraft that means no change in general aviation sector. Nowadays, faster and larger aircraft operates at existing runways that were designed long times ago. The recent acute increase of runway excursion may imply that this is time to evaluate the design standard for the changing trend in the general aviation.

4.1 Limitation of the research

This study is among the first to consider runway design standards related to runway excursions in the perspective of general aviation. However, there were some limitations in the first study. During the investigation of published accidents

96 and incidents, it was found that many of incident data do not contain detailed information of the event. To compile the searched excursion reports, more intense study of certain incident records was required such as reading all the narratives and extracting certain data from the narratives. While the NTSB aviation accident database is consisted of well-organized reports with specific aspects related to an accident or incident, the ASRS and the FAA AIDS were operated based on spontaneous reports from aviation personnel and sometimes limited information was left on the database. To investigate more clear factors for excursion, it was necessary to have detailed description of incidents, not only for the general airport information, but also the exact runway data in the case of the airports operates multiple runways. Also, by comparing accident and incident descriptions, it can help to see a critical factor that makes an oscillation to a significant deviation excursion accident or minor incident.

The runway centerline deviation study could help to see movements of aircraft but all the collected trajectories were normal operations without significant deviation. An observation of severe oscillation or excursion would be helpful to understand the runway excursion, however, as the probability of runway excursion is significantly low, it may be necessary to observe millions of aircraft to have a single sample of severe deviation or runway excursion.

Because of the great number of general aviation airports in the United

States, an accurate and easily accessible database does not exist for exact distribution of runway dimension and number of flight operation in general aviation airports especially for non-towered airports. Therefore, an exact comparison using the exact information of general aviation airports was

97 impossible and estimations needed to be applied. To enhance a reliability of the descriptive analysis in this research and future excursion model development, comprehensive database of GA airports should be established.

4.2. Future work

Even though this study shed a light on a need of runway excursion estimation model exclusively developed for general aviation, it was unable to develop a completed model with the stated limitations above. It is required to collect large number of empirical data on excursion causal factors and centerline deviation data. Following analysis can be part of the new model improvement;

 More data collection for accident/incident data and runway centerline

deviation

 Analysis of significance of each independent variable from the

accident dataset

 Development of runway excursion frequency model for general

aviation based on the collected dataset

 Data collection from international server to compare safety difference

between FAA and ICAO airport design standards, especially in the

perspective of runway

Even though this research does not propose clear solution for mentioned problems, it is beneficial that the study is the first analysis focusing on runway excursions exclusively for general aviation airports and suggested foundation for future research. This study can be used for future excursion modeling, understanding a difference between a normal operation and an excursion based on

98 a certain level of deviation. It is hoped that the findings from this research will motivate future study towards further understanding the behavior of general aviation aircraft and the design of general aviation runways, with the goal of providing airport environments that reduce the risk of runway excursion.

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103

Appendix A: Glossary

104

Abbreviation Full Notation AAC Aircraft Approach Category AC Advisory Circular ADG Airplane Design Group AIDS Accident and Incident Data System AIM Aeronautical Information Manual ASDA Accelerate Stop Distance Available ASRS Aviation Safety Reporting System CAT Category Dep Departure FAA Federal Aviation Administration FAR Federal Aviation Regulation GA General Aviation IATA International Air Transport Association ICAO International Civil Aviation Organization IFALPA International Federal of Air Line Pilots’ Associations IFR Instrument Flight Rules ILS Instrument Landing System IMC IFR Meteorological Condition LDG Landing LiDAR Light Detection and Ranging MoS Modification of Standard MSL Mean Sea Level MTOW Maximum Take Off Weight NASA National Aeronautics and Space Administration NAVAID Navigational Aid NLA New Large Aircraft NOD Normal Operation Data NTSB National Transportation Safety Board OR Overrun PIC Pilot-In-Command RDA Runway Distance Available RDC Runway Design Category RESA Runway End Safety Area ROFA Runway Obstacle Free Area ROFZ Runway Obstacle Free Zone RPZ Runway Protection Zone RSA Runway Safety Area SEPAR Separation Continued

Table A- 1. Glossary

105

Table A-1 Continued SM Statue Mile THLD Threshold TO Takeoff TODA Take Off Distance Available US Undershoot VFR Visual Flight Rules VIS Visibility VMC VFR Meteorological Condition VO Veer-offs

푉푎푝푝 Approach Speed 푉푟푒푓 Reference Speed

106

Appendix B: An Example of the Application of Runway Length Corrections (Source: ICAO Doc.9157, Part I, Chapter 3, 3.5.6)

107

108

Appendix C: Combined Accident and Incident Dataset

109

Event # SOURCE TIME AIRPORT AIRCRAFT type

RWY Case # Database Case ID Year Month Date Time Acc/Inc Airport State ATCT Type Ops/yr Model OEW MTOW Number

1 NTSB ERA15LA300 2015 8 8 - Accident HSP VA No 1 GA 15,407 Cessna 182Q 1621 2550

2 NTSB ERA15LA289 2015 7 23 EDT 16:05 Accident FXE FL Yes 2 Reliever 149,710 Piper PA-28R-180 1380 2550

3 NTSB GAA15CA189 2015 7 28 MDT 13:00 Accident ------Cessna 182Q 1621 2550

4 NTSB GAA15CA184 2015 7 14 MDT 09:15 Accident U03 ID No 1 GA 15,010 Piper PA-12 950 1750

5 NTSB SPR15WA225 2015 7 5 LOC 02:10 Accident GUM GU Yes 2 Small hub, Primary 71,491 B737-800 90710 155500

6 NTSB GAA15CA168 2015 7 1 MDT 07:30 Accident SAA WY No 1 GA 8,940 Cessna A188B 2235 3300

7 NTSB GAA15CA150 2015 6 30 PDT 11:40 Accident O88 CA No 2 GA 3,500 Cessna TR182 1845 3100

8 NTSB GAA15CA144 2015 6 28 CDT 09:15 Accident ISN ND No 2 Non-hub, Primary 29,646 Bellanca 7GCBC 1200 1800

9 NTSB GAA15CA149 2015 6 28 PDT 11:45 Accident DVO CA No 1 Reliever 98,000 Piper PA-28-180 1380 2500 110 10 NTSB GAA15CA148 2015 6 27 EDT 17:21 Accident AGC PA Yes 2 Reliever 55,850 Mooney M20R 2194 3200

11 NTSB GAA15LA203 2015 7 23 EDT 19:00 Accident HZL PA No 1 GA 23,100 Welch Floyd L/Quickie Q2 490 1000

12 NTSB GAA15CA194 2015 7 22 CDT 14:00 Accident MVI CO No 3 GA 6,000 Air Tractor AT402B 4299 9170

13 NTSB GAA15CA156 2015 6 26 MDT 09:50 Accident VEL UT No 2 GA 8,960 Cessna 172N 1430 2300

14 NTSB GAA15CA187 2015 6 26 PDT 06:40 Accident SOW AZ No 2 GA 12,382 Hier James A/Long EZ 926 - -

15 NTSB GAA15CA152 2015 6 24 CDT 14:30 Accident 26R TX No 1 GA 7,620 Air Tractor AT502B 4546 9400

16 NTSB GAA15CA135 2015 6 23 PDT 06:18 Accident 97OG OR No 1 Private - WskPzl Mielec/M18A 5975 11700

17 NTSB GAA15CA134 2015 6 22 MDT 20:15 Accident MAN ID No 1 GA 72,000 Quicksilver/MX4371 - -

18 NTSB GAA15CA120 2015 6 11 PDT 15:00 Accident PAE WA Yes 3 Reliever 112,788 Beech RC45J 5680 8000

Grumman Acft Eng Cor-Schweize 19 NTSB GAA15CA113 2015 6 9 PDT 11:30 Accident MIT CA No 3 GA 45,000 3150 7020 866

20 NTSB GAA15CA117 2015 6 9 MDT 16:30 Accident 6V4 SD No 2 GA 3,406 Air Tractor AT401 4244 7860 Continued Table C- 1. Example of Collected Dataset

110

Table C-1 Continued # FLIGHT INFO METEOROLOGICAL INFO

FAR Temperature Case # Mission Phase Damage Fire VIS Con VIS Ceiling Light WIND Wind Event Precipitation Part (C )

BRK 1000 1 91 Personal Climbing Substantial None VMC 10 Day 090/04 Tailwind 19 None OVC 4600

2 91 Instructional Maneuvering Substantial None VMC 10 None Day 130/13 - 33 None

3 91 Personal Takeoff Substantial None VMC 10 - Day 150/09 - 30 None

4 91 Instructional LDG Roll Substantial None VMC 10 - Day Calm - 20 None

5 Foreign Passenger LDG Roll Minor None IMC - - Day - - - +RA

6 137 Agricultural LDG Roll Substantial None VMC 10 - Day 140/04 - 16 None

7 91 Instructional LDG Roll Substantial None VMC - - Day VAR/LGT - 35 HZ

8 91 Personal LDG Roll Substantial None VMC 10 None Day 060/04 - 20 None

111 9 91 Personal Flare or T/D Substantial None VMC 10 None Day 220/08 - 22 None BRK 10 91 Personal Landing Substantial None VMC 10 Day 170/08 Crosswind 24 None 3100 11 91 Personal LDG Roll Substantial None VMC 10 - Day 350/05 Crosswind 26 None Aerial 12 137 LDG Roll Substantial None VMC - - Day - Gust - - Application 13 91 Personal LDG Roll Substantial None VMC 10 None Day 080/11 Crosswind 28 None

14 91 Personal Flare or T/D Substantial None VMC 10 None Day 190/09 - 20 None

Aerial 15 137 Takeoff Substantial None VMC 10 None Day 140/11 - 32 None Application Aerial 16 137 Takeoff Substantial None VMC 10 None Day 330/08 - 19 None Application 17 91 Personal Takeoff Substantial None VMC 10 None Day 350/05 - 29 None

18 91 Personal LDG Roll Substantial None VMC 10 None Day 310/06 - 22 None

Aerial 19 137 LDG Roll Substantial None VMC 10 None Day 270/13 Tailwind 30 None Application Aerial 20 137 Takeoff Substantial None VMC 10 None Day 320/04 - 26 None Application Continued

111

Table C-1 Continued # RUNWAY INFO EXCURSION PILOT INFO INJURY INFO RWY RWY Total Length Width RWY Pavement Surface Friction Excursion Pilot Student Case # Length Width Obstacle Flight Fatal Serious Minor/None Group Group Condition Material Condition aids direction Age Pilot (ft) (ft) Time

1 5,600 3 100 2 Dry Asphalt Good None End Overrun - 0 - 0 0 2

2 4,000 2 100 2 - Asphalt Good Grooved Both Left - 0 - 0 0 0

3 1,910 1 25 0 Dry Dirt - None None Right 55 0 717 0 0 2

4 3,898 2 60 0 Dry Asphalt Good None Both Right 64 1 10 0 0 2

5 10,014 5 150 3 Wet Asphalt Good Grooved Both Right - 0 - - - -

6 8,801 4 100 2 Dry Asphalt Good None None Right 29 0 438 0 0 1

7 2,199 1 60 0 Dry Asphalt Good None THLD Left 58 0 1,063 0 0 3

8 6,650 3 100 2 Dry Asphalt Good Grooved End Right 66 0 591 0 0 1

112 9 3,300 2 75 1 Dry Asphalt Good None Both Left 51 0 388 0 0 4 10 6,501 3 150 3 Dry Asphalt Good Grooved Both Left 47 0 599 0 0 4

11 4,878 2 100 2 Dry Asphalt Good None Both Left 28 0 54 0 0 1

12 ------Right - 0 1,300 0 0 1

13 4,108 2 60 0 Dry Asphalt Good None Both Left 49 0 54 0 0 2

14 3,938 2 60 0 Dry Asphalt Good None None Right 53 0 104 0 0 1

15 3,393 2 70 1 Wet Asphalt Good None THLD Right 68 0 32,500 0 0 1

16 3,000 2 20 0 Dry Asphalt - None None Overrun 53 0 3,825 0 0 1

17 5,000 2 75 1 - Asphalt Good None Both Left 44 1 - 0 0 1

18 9,010 4 150 3 Dry Asphalt/Concrete Good Grooved THLD Left 58 0 5,000 0 0 1

19 4,501 2 100 2 - Asphalt Good None None Right 53 0 2,000 0 0 1

20 2,000 1 100 2 Dry Grass/turf Fair None None Overrun 65 0 5,152 0 0 1 Continued

112

Table C-1 Continued # FACTORS

Human Wind Case # Weather Aircraft (F) Airport (F) Procedure Policy Environment Obstacle (F) Manual Surface Primary Factor Factor (F)

1 1 0 1 0 0 0 0 0 0 0 0 Aircraft

2 0 0 1 0 0 0 0 0 0 0 0 Aircraft

3 1 0 1 0 0 0 0 0 0 1 0 Human Factor

4 1 0 0 0 0 0 0 0 0 0 0 Aircraft

5 ------

6 1 0 0 0 0 0 0 0 0 0 0 Human Factor

7 1 0 0 0 0 0 0 0 0 0 0 Human Factor

8 1 0 0 0 0 0 0 0 0 0 0 Human Factor 113 9 1 0 0 0 0 0 0 0 0 1 0 Human Factor

10 1 0 0 0 0 0 0 0 0 0 0 Human Factor

11 1 0 0 0 0 0 0 0 0 0 0 Human Factor

12 0 0 0 0 0 0 0 0 0 1 0 Wind

13 1 1 0 0 0 0 0 0 0 1 0 Wind

14 0 0 1 0 0 0 0 0 0 0 0 Aircraft

15 1 0 0 0 0 0 0 0 0 0 0 Human Factor

16 1 0 0 0 0 0 0 0 0 0 1 Human Factor

17 1 0 0 0 0 0 0 0 0 1 0 Wind

18 1 0 1 0 0 0 0 0 0 0 0 Aircraft

19 1 1 0 0 0 0 0 0 0 1 0 Wind

20 1 1 0 0 0 0 1 0 0 0 0 Human Factor Continued

113

Table C-1 Continued # RESULT Collision Collision Nose Loss of Evasive Rejected Aborted after L/A, G/A, Weather Hard Abnormal LDG area LDG gear Sys/comp Case # with during over/nose control Action T/O T/D M/A Encounter Landing RWY contact overshoot collapse malf/fail terr/obj TO/LDG down

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 1 0 0 0 0 0 0 0 0

3 1 1 0 1 0 0 0 0 0 0 0 0 0 0

4 1 1 0 0 0 0 0 0 0 0 0 0 0 0

5 0 1 0 0 0 0 0 0 0 0 0 0 0 0

6 1 1 0 0 0 0 0 0 0 0 0 0 0 0

7 1 1 0 0 0 0 0 0 0 0 0 0 0 0

8 1 1 0 0 1 0 0 0 0 0 0 0 0 0

114 9 0 0 0 0 0 0 1 0 0 0 0 0 0 0 10 0 1 0 0 0 0 0 0 0 0 0 0 0 0

11 1 0 0 0 0 0 0 0 1 0 0 0 0 0

12 ------

13 1 1 0 0 0 0 0 1 0 0 0 0 0 0

14 0 0 0 0 0 0 0 0 0 0 0 0 1 0

15 1 1 0 0 0 0 0 0 0 0 0 0 0 0

16 0 1 0 0 0 0 0 0 0 0 0 0 0 0

17 1 1 0 0 0 0 0 0 1 0 0 0 0 0

18 1 1 0 0 0 0 0 0 0 0 0 0 0 0

19 1 1 0 0 0 0 0 1 0 0 0 0 0 0

20 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Continued

114

Table C-1 Continued # RESULT NOTE

RWY Abrupt Dragged Case # Stall Preflight/dispatch Note Incursion maneuver wing/rotor/float/other

1 0 0 0 0 0

2 0 0 0 0 0

3 0 0 0 0 0

4 0 0 0 0 0

5 0 0 0 0 0

6 0 0 0 0 0

7 0 0 0 0 0

8 0 0 0 0 0

115 9 0 0 0 0 0 10 0 0 0 0 0

11 0 0 0 0 0

12 - - - - -

13 0 0 0 0 0

14 0 0 0 0 0 Parts separation from aircraft

15 0 0 0 0 0

16 0 0 0 0 0

17 0 0 0 0 0

18 0 0 0 0 0

19 0 0 0 0 0

20 0 1 0 0 0 Continued

115

Table C-1 Continued Event # SOURCE TIME AIRPORT AIRCRAFT type

RWY Case # Database Case ID Year Month Date Time Acc/Inc Airport State ATCT Type Ops/yr Model OEW MTOW Number

411 ASRS 1255669 2015 4 - X Incident ZZZ - - - - - C172 1,610 2,555

412 ASRS 1249804 2015 3 - X Incident ZZZ - - - - - BE-23 1713 2750

Non-hub, 413 ASRS 1246890 2015 3 - X Incident FRG NY Yes 2 191,619 PA-28 1689 2550 Primary 414 ASRS 1246010 2015 3 - X Incident ZZZ - - - - - EMB-120 15655 25353 415 ASRS 1239957 2015 2 - X Incident ZZZ - - - - - Stearman - - 416 ASRS 1239134 2015 2 - X Incident ZZZ - - - - - PA-46 2354 5092

417 ASRS 1237466 2015 2 - X Incident BLT - - - - - Small - -

418 ASRS 1223707 2014 11 - X Incident ZZZ - - - - - C172 1610 2555

Large 419 ASRS 1217985 2014 11 - X Incident MSP MN Yes 4 hub, 412,586 CRJ200 30292 53000 Primary

116 420 ASRS 1217034 2014 11 - X Incident AUO AL No 2 GA 65,445 C172 1610 2555

421 ASRS 1216515 2014 11 - X Incident ZZZ - - - - - BE-95 - -

422 ASRS 1213530 2014 10 - X Incident ZZZ - - - - - C172 1610 2555

423 ASRS 1210434 2014 10 - X Incident ZZZ - - - - - AA5 Series 1200 2200 424 ASRS 1204632 2014 9 - X Incident ZZZ - - - - - PA-24 2110 3600 425 ASRS 1202949 2014 9 - X Incident ZZZ - - - - - M-20B/C 1525 2575

426 ASRS 1202094 2014 9 - X Incident ZZZ - - - - - BE-76 2446 3900

427 ASRS 1201542 2014 9 - X Incident ZZZ - - - - - Decathlon 8KCAB 1340 1950 428 ASRS 1201245 2014 9 - X Incident ZZZ - - - - - C120 900 1500 429 ASRS 1194684 2014 8 - X Incident ZZZ - - - - - PA-18 946 1750 430 ASRS 1190688 2014 7 - X Incident ZZZ - - - - - G200 19200 34850 431 ASRS 1189383 2014 7 - X Incident ZZZ - - - - - Rockwell North American - - Continued

116

Table C-1 Continued # FLIGHT INFO METEOROLOGICAL INFO

FAR VIS Wind Temperature Case # Mission Phase Damage Fire VIS Ceiling Light WIND Precipitation Part Con Event (C )

411 91 Personal Landing None None - - - - - Gust - -

412 91 Instructional Landing Minor None VMC 10 None Day - - - -

413 91 Personal Landing Minor None VMC 10 10000 - 210/10 - - -

414 91 Ferry Taxi None None VMC 10 - Day - - - SN 415 91 Personal Landing None None VMC 10 20000 Day - - - - 416 91 Personal Landing Minor None VMC 6 2200 Day 350/15 Gust - ICE

417 91 Instructional Landing None None VMC - 2800 Day - - - SN, ICE

418 91 Instructional Landing None None VMC 10 10000 Day - - - -

419 - - Taxi None None - - - Day - - - -SN

420 91 Personal Landing None None VMC 10 - Day - - - -

117 421 91 Personal Landing Minor None VMC 10 10000 Day - - - -

422 91 Instructional Landing Minor None - 10 10000 - - - - -

423 91 Personal Landing Minor None VMC 10 - Day - Windshear - - 424 91 Passenger Takeoff None None VMC 10 10000 Day - - - - 425 91 Personal Takeoff Minor None - 10 - - - Tailwind - -

426 91 Instructional Landing None None VMC - - Day - - - -

427 91 Personal Takeoff Minor None VMC 10 - Day - - - - 428 91 Personal Landing Minor None VMC 10 15000 Day Calm - - - 429 91 Personal Landing Minor None VMC 10 2500 Day - - - - 430 91 Passenger Landing None None VMC - - Day - - - - 431 91 Ferry Landing Minor None VMC 35 - Day - Gust - - Continued

117

Table C-1 Continued # RUNWAY INFO EXCURSION PILOT INFO INJURY INFO RWY RWY Total Length Width RWY Pavement Surface Excursion Pilot Student Case # Length Width Friction aids Obstacle Flight Fatal Serious Minor/None Group Group Condition Material Condition direction Age Pilot (ft) (ft) Time 411 ------Ambiguous ------

412 ------Right - 1 80 - - -

413 5,116 3 150 3 - Asphalt Good Grooved Both Ambiguous ------

414 ------Both ------415 ------Left ------416 ------Left ------No RWY 417 ------None - Right ------info

418 - - - - Wet - - Grooved - Left ------

No RWY 419 ------None - Ambiguous ------info No RWY 420 ------None - Left ------info

118 421 ------Right ------

422 ------Left - 1 100 - - -

423 ------Left ------424 ------Left ------425 ------Right ------

426 ------Right - 1 450 - - -

427 ------Ambiguous ------428 ------Right - - 4,900 - - - 429 ------Left ------430 ------Overrun ------431 ------Right - 0 12,000 - - - Continued

118

Table C-1 Continued # FACTORS

Human Wind Case # Weather Aircraft (F) Airport (F) Procedure Policy Environment Obstacle (F) Manual Surface Primary Factor Factor (F)

411 1 0 0 0 0 0 0 0 0 1 0 Wind

412 1 0 0 0 0 0 0 0 0 0 0 Human Factor

413 1 0 0 0 0 0 0 0 0 0 0 Human Factor

414 1 0 1 0 0 0 0 0 0 0 0 Ambiguous 415 0 0 1 0 0 0 0 0 0 0 0 Aircraft 416 1 1 0 1 0 0 0 0 0 1 1 Ambiguous

417 1 0 0 0 0 0 0 0 0 1 0 Ambiguous

418 1 1 0 1 0 0 0 0 0 1 1 Human Factor

419 1 0 0 0 0 1 0 0 0 0 1 Human Factor

420 1 0 0 0 0 0 0 0 0 0 0 Human Factor

119 421 0 0 1 0 0 0 0 0 0 0 0 Aircraft

422 1 0 0 0 0 0 0 0 0 0 0 Human Factor

423 1 0 0 0 0 0 0 0 0 1 0 Human Factor 424 1 0 1 0 0 0 0 0 0 0 0 Ambiguous 425 1 0 1 0 0 0 0 0 0 0 0 Human Factor

426 1 0 0 0 0 0 0 0 0 0 0 Human Factor

427 1 0 0 0 0 0 0 0 0 0 0 Human Factor 428 1 0 1 0 0 0 0 0 0 0 0 Human Factor 429 1 0 0 0 0 0 0 0 0 0 0 Human Factor 430 1 0 0 0 1 0 0 0 0 0 0 Human Factor 431 1 0 0 0 0 0 0 0 0 0 0 Human Factor Continued

119

Table C-1 Continued # RESULTS Collision Collision Nose Loss of Evasive Rejected Aborted after L/A, G/A, Weather Hard Abnormal LDG area LDG gear Sys/comp Case # with during over/nose control Action T/O T/D M/A Encounter Landing RWY contact overshoot collapse malf/fail terr/obj TO/LDG down 411 0 0 0 0 0 0 0 0 1 0 0 0 0 0

412 0 0 0 0 0 0 0 0 0 1 0 0 0 0

413 1 1 0 0 0 0 0 0 0 0 0 0 0 0

414 0 1 0 0 0 0 0 0 0 0 0 0 0 0 415 1 0 0 0 0 0 0 0 0 0 0 0 0 0 416 1 0 0 0 0 0 0 0 0 0 0 0 1 0

417 0 0 0 0 0 0 0 0 0 0 0 0 0 0

418 0 0 0 0 0 0 0 0 0 0 0 0 0 0

419 0 0 0 0 0 0 0 0 0 0 0 0 0 0

420 0 1 0 0 0 0 0 0 0 0 0 0 0 0 120 421 1 0 0 0 0 0 0 0 0 0 0 0 0 0

422 0 0 1 0 0 0 0 0 0 1 0 0 0 0

423 0 1 0 0 0 0 0 0 0 0 0 0 0 0 424 1 0 0 0 0 0 0 0 0 1 0 0 0 0 425 1 0 0 1 0 0 0 0 0 0 0 0 0 0

426 0 0 0 0 0 0 0 0 0 0 0 0 0 0

427 0 1 0 0 0 0 0 0 0 0 0 0 0 0 428 1 0 0 0 0 0 0 0 0 0 0 0 0 0 429 1 0 0 0 0 0 0 0 0 0 0 0 0 0 430 0 0 1 0 0 0 0 0 0 0 0 0 0 0 431 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Continued

120

Table C-1 Continued # RESULT NOTE

RWY Abrupt Dragged Case # Stall Preflight/dispatch Note Incursion maneuver wing/rotor/float/other

411 0 0 0 0 0

412 0 0 0 0 0

413 0 0 0 0 0

414 0 0 0 0 0 415 0 0 0 0 0 416 0 0 0 0 0

417 0 0 0 0 0

418 0 0 0 0 0

419 0 0 1 0 0

420 0 0 0 0 0

121 421 0 0 0 0 0

422 0 0 0 0 0

423 0 0 0 0 0 424 0 0 0 0 0 425 0 0 0 0 0

426 0 0 0 0 0

427 0 0 0 0 0 428 0 0 0 0 0 429 0 0 0 0 0 430 0 0 0 0 0 431 0 0 0 0 0 Continued

121

Table C-1 Continued Event # SOURCE TIME AIRPORT AIRCRAFT type

RWY Case # Database Case ID Year Month Date Time Acc/Inc Airport State ATCT Type Ops/yr Model OEW MTOW Number

590 FAA AIDS 20150725011719I 2015 7 25 X Incident IXD KS Yes 2 Reliever 46,344 CE-172 1260 2200 Small hub, 591 FAA AIDS 20150624009289I 2015 6 24 X Incident CAK OH Yes 2 80,528 M-20 2194 3200 Primary Non-hub, 592 FAA AIDS 20141227023789I 2014 12 27 X Incident STS CA Yes 2 83,463 BE-35 2517 3650 Primary 593 FAA AIDS 20140501005259I ???? - - 13:27 UTC Incident ------PA-28 1290 2150 594 FAA AIDS 20131008021219I ???? 10 8 17:50 EDT Incident RMY MI No 1 GA 8,250 CIRRUS-SR 20 2050 2900 595 FAA AIDS 20120904021049I ???? 9 4 X Incident ------PA-28RT 1380 2550 596 FAA AIDS 20120729026169I 2012 7 29 X Incident PTK MI Yes 3 Commercial 126,070 CE-560 11910 20000 597 FAA AIDS 20120719015219I 2012 7 19 X Incident UUU - No 2 GA 18,708 CE-182 1621 2550

598 FAA AIDS 20120328004269I 2012 3 28 X Incident APA CO Yes 3 Reliever 301,476 CIRRUS-SR - -

599 FAA AIDS 20120319003599I 2012 3 19 X Incident ARB MI Yes 2 GA 63,521 CE-152 1131 1675 600 FAA AIDS 20120229002299I 2012 2 29 X Incident PEX MN No 1 GA 3,600 PA-28 1290 2150 601 FAA AIDS 20110820018929I 2011 8 20 X Incident Beach - - - - - CE-207 2176 3600

122 602 FAA AIDS 20110526008719I 2011 5 26 X Incident JVY IN No 2 Reliever 44,430 TBM-700 4629 7394

603 FAA AIDS 20110214006109I 2011 2 14 X Incident GKY TX Yes 1 Reliever 88,222 CE-172 1260 2200 604 FAA AIDS 20101209055619I 2010 12 9 X Incident FTY GA Yes 3 Reliever 60,000 CE-172 1260 2200 Large hub, 605 FAA AIDS 20100121027669I 2010 1 21 X Incident SAN CA Yes 1 187,793 737 72490 164000 Primary

606 FAA AIDS 20070510013479I 2007 5 10 X Incident APA CO Yes 3 Reliever 301,476 CE-404 4834 8400

607 FAA AIDS 20060904019449I 2006 9 4 X Incident 16J (?) GA No 1 GA 6,900 PA-12 950 1750 608 FAA AIDS 20060131001229I 2006 1 31 X Incident 50J - No 1 GA 19,050 PA-46 2354 4100 Non-hub, 609 FAA AIDS 20051227033539I 2005 12 27 X Incident SAW MI Yes 1 17,208 1900 10650 17120 Primary Non-hub, 610 FAA AIDS 20051006025129I 2005 10 6 X Incident HDN CO No 1 9,430 CITATION 500 I 6631 11850 Primary Continued

122

Table C-1 Continued # FLIGHT INFO METEOROLOGICAL INFO

FAR VIS Wind Temperature Case # Mission Phase Damage Fire VIS Ceiling Light WIND Precipitation Part Con Event (C )

590 91 - Flare or T/D Minor None ------

591 91 - LDG Roll Minor None VMC 10 4200 - 340/07 Crosswind - -

592 91 - LDG Roll Minor None - - - - Calm - - -

593 91 - Flare or T/D Minor None VMC 10 8000 - 280/10 Crosswind - - 594 141 - Approach Minor None ------595 91 - Taxi Minor None ------596 135 - Taxi Minor None ------597 91 - Takeoff Minor None ------

598 91 - TAXI Minor None ------

599 91 - LDG roll None None ------600 91 - Flare or T/D Minor None - - - - 010/12 Crosswind - - 601 135 Signtseeing Takeoff Minor None ------

123 602 91 - Takeoff Minor None ------

603 91 - LDG Roll Minor None ------

604 91 - LDG Roll Minor None ------

605 121 - LDG Roll Minor None - - - - 160/20 Gust - -

606 91 Instructional LDG Roll Minor None ------

607 91 - Landing Minor None VMC ------608 91 - Taxi Minor None VMC - - - - Crosswind - -

609 121 - LDG Roll Minor None VMC - - Night - - - -

610 91 - LDG Roll Minor None - - - Day - - - - Continued

123

591 8204 4 150 3 - Asphalt Good Grooved None Overrun - 0 700 - - -

No RWY 592 ------None - Ambiguous - 0 1,080 0 0 3 Info 593 ------Right - 0 300 - - - 594 3501 2 75 1 - Asphalt Good None Both Ambiguous - 1 149 0 0 1 595 ------Ambiguous - 0 178 - - - Porous 596 6521 3 150 3 - Asphalt Fair Friction THLD Left - 0 - - - - Courses 597 2999 2 75 1 - Asphalt Fair None Both Ambiguous - 0 147 0 0 4 No RWY 598 ------None - Ambiguous - 0 22,000 - - - Info 599 3505 2 75 1 - Concrete Fair Grooved Both Left - 1 27 0 0 1 Seal Coat 600 3302 2 75 1 - Asphalt Fair None Ambiguous - 0 107 - - - Separating 12 601 ------Ambiguous - 0 15,000 - - -

4 No RWY

602 ------None - Ambiguous - 0 2,847 - - - Info 603 6000 3 100 2 - Concrete Good None None Left - 0 52 - - - 604 2801 2 60 0 - Asphalt Fair None Both Left - 1 40 - - -

605 9400 4 200 4 - Asphalt/Concrete Good Grooved Both Right - 0 9,369 - - -

606 4800 2 75 1 - Asphalt Good None THLD Left - 1 4,000 - - -

607 4507 2 75 1 - Asphalt Fair None None Ambiguous - 1 200 - - - 608 4351 2 75 1 - Concrete Excellent None Both Ambiguous - 0 2,500 - - -

609 12366 5 150 3 - Asphalt/Concrete Fair Grooved None Left - 0 3,500 - - -

610 10000 4 150 3 - Asphalt Good Grooved Both Left - 0 3,430 - - - Continued

124

Table C-1 Continued # FACTORS

Human Wind Case # Weather Aircraft (F) Airport (F) Procedure Policy Environment Obstacle (F) Manual Surface Primary Factor Factor (F)

590 0 0 0 0 0 0 0 0 0 1 0 Wind

591 1 0 0 0 0 0 0 0 0 0 0 Human Factor

592 0 0 1 0 0 0 0 0 0 0 0 Aircraft

593 0 0 1 0 0 0 0 0 0 0 0 Aircraft 594 1 0 0 0 0 0 0 0 0 0 0 Human Factor 595 1 0 0 0 0 0 0 0 0 0 0 Human Factor 596 0 0 1 0 0 0 0 0 0 0 0 Aircraft 597 1 0 0 0 0 0 0 0 0 0 0 Human Factor

598 ------

599 1 0 0 0 0 0 0 0 0 0 0 Human Factor 600 1 0 0 0 0 0 0 0 0 1 0 Human Factor 601 0 0 0 0 0 0 1 0 0 0 1 Surface

12 602 0 0 1 0 0 0 0 0 0 0 0 Aircraft

5 603 1 0 0 0 0 0 0 0 0 0 0 Human Factor 604 1 0 0 0 0 0 0 0 0 0 0 Human Factor

605 0 0 1 0 0 0 0 0 0 0 0 Aircraft

606 1 0 0 0 0 0 0 0 0 0 0 Human Factor

607 1 0 0 0 0 0 0 0 0 0 0 Human Factor 608 1 0 0 0 0 0 0 0 0 0 0 Human Factor

609 1 0 0 0 0 0 0 0 0 0 0 Human Factor

610 0 0 1 0 0 0 0 0 0 0 0 Aircraft Continued

125

591 1 1 0 0 0 0 0 0 0 0 1 0 0 0

592 0 0 0 0 0 0 0 0 0 0 0 0 1 0

593 0 0 0 0 0 0 0 0 0 0 0 0 1 0 594 0 0 0 0 0 0 0 0 0 0 0 0 0 0 595 1 0 0 0 0 0 0 0 0 0 0 0 0 0 596 1 0 0 0 0 0 0 0 0 0 0 0 0 0 597 0 0 0 0 0 0 0 0 0 0 0 0 0 0

598 ------

599 1 0 0 0 0 0 0 0 0 0 0 0 0 0 600 0 1 0 0 0 0 0 0 0 0 0 0 0 0 601 0 0 0 0 0 0 0 0 0 0 0 0 1 0

12 602 0 0 0 0 0 0 0 0 0 0 0 0 1 0

6 603 0 1 0 0 0 0 0 0 0 0 0 0 0 0

604 0 1 0 0 0 0 0 0 0 0 1 0 0 0

605 1 0 0 0 0 0 0 0 0 0 0 0 0 0

606 0 0 0 0 0 0 0 0 0 0 0 0 1 0

607 1 0 0 0 0 0 0 0 0 0 0 0 0 0 608 0 0 0 0 0 0 0 0 0 0 0 0 1 0

609 0 1 0 0 0 0 0 0 0 0 0 0 0 0

610 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Continued

126

Table C-1 Continued # RESULT NOTE

RWY Abrupt Dragged Case # Stall Preflight/dispatch Note Incursion maneuver wing/rotor/float/other

590 0 0 0 0 0

591 0 0 0 0 0

592 0 0 0 0 0

593 0 0 0 0 0 594 0 0 0 0 0 595 0 0 0 0 0 596 0 0 0 0 0 597 0 0 0 0 0

598 - - - - -

599 0 0 0 0 0 600 0 0 0 0 0 601 0 0 0 0 0

602 0 0 0 0 0 12

7 603 0 0 0 0 0

604 0 0 0 0 0

605 0 0 0 0 0

606 0 0 0 0 0

607 0 0 0 0 0 608 0 0 0 0 0

609 0 0 0 0 0

610 0 0 0 0 0

127

Appendix D: List of Non-towered General Aviation Airports in Ohio [34]

128

FAA Airport name Role/Type Operations AKR Akron Fulton International Airport GA 26000

2D1 Barber Airport GA 9500

3G4 Ashland County Airport GA 8225

HZY Ashtabula County Airport GA 16886

UNI Ohio University Airport (Snyder Field) GA 52734

6G5 Barnesville-Bradfield Airport GA 10150

I69 Clermont County Airport GA 30650

EDJ Bellefontaine Regional Airport GA 8325

5G7 Bluffton Airport GA 71980

1G0 Wood County Airport GA 27405

0G6 Williams County Airport GA 12010

17G Port Bucyrus-Crawford County Airport GA 24871

8G6 Harrison County Airport GA 11900

CDI Cambridge Municipal Airport GA 6040

TSO Carroll County-Tolson Airport GA 34950

CQA Lakefield Airport GA 16212

HTW Lawrence County Airpark GA 41910

RZT Ross County Airport GA 50150

CYO Pickaway County Memorial Airport GA 35450

I40 Richard Downing Airport GA 19550

I19 Greene County-Lewis A. Jackson Regional Airport GA 42900

DFI Defiance Memorial Airport GA 9130

DLZ Delaware Municipal Airport GA 39300

02G Columbiana County Airport GA 31156

FDY Findlay Airport GA 24550

FZI Fostoria Metropolitan Airport GA 7900

S24 Sandusky County Regional Airport GA 5616

GQQ Galion Municipal Airport GA 6216

GAS Gallia-Meigs Regional Airport GA 19800

GEO Brown County Airport GA 8212

I67 Cincinnati West Airport GA 30197

HOC Highland County Airport GA 15850

I43 James A. Rhodes Airport GA 6053

89D Kelleys Island Land Field GA 25495

1G3 Kent State University Airport GA 72500 Continued Table D- 1. List of Non-towered Airports in Ohio

129

Table D-1 Continued

I95 Hardin County Airport GA 6562

LHQ Fairfield County Airport GA 43066

I68 Lebanon-Warren County Airport GA 31525

AOH Lima Allen County Airport GA 32500

UYF Madison County Airport GA 41410

MFD Mansfield Lahm Regional Airport GA

MNN Marion Municipal Airport GA 42650

MRT Union County Airport GA 31886

22I Vinton County Airport GA 5225

3T7 Middle Bass Island Airport GA 6500

7G8 Geauga County Airport GA 7450 Middletown Regional Airport (Hook Field) (was Hook MWO GA 40050

Field Muni)

10G Holmes County Airport GA 21500

4I9 Morrow County Airport GA 22608

4I3 Knox County Airport GA 20150

7W5 Henry County Airport GA 15637

I86 Perry County Airport GA 4550

PHD Harry Clever Field GA 54880

VTA Newark-Heath Airport GA 12457

3X5 North Bass Island Airport GA 1000

5A1 Norwalk-Huron County Airport GA 10100

OWX Putnam County Airport GA 11910

OXD Miami University Airport GA 16708

PMH Greater Portsmouth Regional Airport GA 45830

3W2 Put-in-Bay Airport GA 15125

29G Portage County Airport GA 9071

SGH Springfield-Beckley Municipal Airport GA 9245

2G2 Jefferson County Airpark GA 15969

16G Seneca County Airport GA 60165

56D Wyandot County Airport GA 7410

I74 Grimes Field GA 23480

VNW Van Wert County Airport GA 20516

VES Darke County Airport GA 9238

3G3 Wadsworth Municipal Airport GA 15325

AXV Neil Armstrong Airport GA 9805

62D Warren Airport GA 14738 Continued

130

Table D-1 Continued

I23 Fayette County Airport GA 29405

USE Fulton County Airport GA 21123

EOP Pike County Airport GA 2012

AMT Alexander Salamon Airport GA 5210

I66 Clinton Field GA 27860

4G5 Monroe County Airport GA 3324

BJJ Wayne County Airport GA 96520

ZZV Zanesville Municipal Airport GA 33312

0D7 Ada Airport - 555

1D4 Mayfield Airport - 450

4G3 Miller Airport - 3500

7B4 Miller Farm Landing Strip - 360

2D7 Beach City Airport - 6112

3D8 Bordner Airport - 2200

I62 Brookville Air-Park - 29359

I10 Noble County Airport - 5950

5D6 Parsons Airport - 1662

6CM Chapman Memorial Field - 4000

03I Clarks Dream Strip - 2520

5D9 Bandit Field Airdrome - 140

4G8 Columbia Airport - 5150

04I Columbus Southwest Airport - 11833

I73 Moraine Airpark - 19188

I44 Dahio Trotwood Airport (Dayton-New Lebanon Airport) - 1853

6D7 Deshler Municipal Landing Strip - 2000

1G1 Elyria Airport - 14300

14G Fremont Airport - 38450

7D8 Gates Airport - 4200

7D9 Germack Airport - 840

82D Weiker Airport - 320

88D Hinde Airport - 1850

92D Harlan Airfield - 1155

R47 Ruhe's Airport - 5000

I71 Morgan County Airport - 5625

3W9 Middle Bass-East Point Airport - 1300

6G4 Wynkoop Airport - 5525

O74 Elliot's Landing Airport - 1560 Continued

131

Table D-1 Continued

41N Braceville Airport - 425

2G1 Concord Airpark - 4510

6C2 Ohio Dusting Company Airport - 3020

2H8 Paulding Airport - 2100

3I7 Phillipsburg Airport - 68000 I17 Piqua Airport (Hartzell Field) - 10200

5E9 Packer Airport - 3181

38D Salem Airpark - 16920

8G8 Koons Airport - 2546

3G6 Tri-City Airport - 10555

12G Shelby Community Airport - 2012

2P7 Alderman Airport - 6150

O12 Grand Lake St. Marys Seaplane Base - 8

1G8 Eddie Dew Memorial Airpark - 3540

I54 Mad River Airport - 15350

37I Troy Skypark - 4264

1WF Waco Field - 1500

38I Weller Airport - 300

15G Weltzien Skypark - 79130

I64 Wakeman Airport - 10115

40I Red Stewart Airfield - 16800

67D Reader-Botsford Airport - 18700

80G Tri-City Airport - 8085

8G1 Willard Municipal Airport - 2715

ILN Airborne Airpark - -

2B6 Hollister Field - 250

04G Lansdowne Airport - 3500

42I Parr Airport - 16025 SUM 2,301,638

132

Appendix E: Non-towered Airport Operation Estimation based on 2014 Ohio Flown Hour [35]

133

Flown Hour 2014 GA Ratio to OPS Estimation State Rank Flown Hour Ohio Estimation × 5yrs 1 California 2,166,084 3.0259 6,964,622 34,823,111 2 Florida 2,052,126 2.8667 6,598,212 32,991,062 3 Texas 2,001,797 2.7964 6,436,389 32,181,946 4 Alaska 771,965 1.0784 2,482,103 12,410,517 5 Oklahoma 756,291 1.0565 2,431,707 12,158,534 6 Arizona 730,679 1.0207 2,349,356 11,746,782 7 Ohio 715,838 1.0000 2,301,638 11,508,190 8 Colorado 714,758 0.9985 2,298,165 11,490,827 9 Louisiana 650,348 0.9085 2,091,068 10,455,338 10 Pennsylvania 620,700 0.8671 1,995,740 9,978,701 11 Oregon 607,479 0.8486 1,953,231 9,766,153 12 New York 594,028 0.8298 1,909,982 9,549,908 13 Alabama 535,402 0.7479 1,721,481 8,607,406 14 Kansas 530,327 0.7408 1,705,163 8,525,817 15 Georgia 486,646 0.6798 1,564,716 7,823,578 16 Virginia 483,889 0.6760 1,555,851 7,779,255 17 Washington 480,368 0.6711 1,544,530 7,722,650 18 North Carolina 480,223 0.6709 1,544,064 7,720,319 19 Michigan 444,711 0.6212 1,429,882 7,149,409 20 Illinois 433,580 0.6057 1,394,092 6,970,461 21 Utah 424,682 0.5933 1,365,482 6,827,412 22 Nevada 418,090 0.5841 1,344,287 6,721,436 23 Minnesota 400,668 0.5597 1,288,270 6,441,351 24 New Jersey 391,394 0.5468 1,258,451 6,292,257 25 Wisconsin 386,953 0.5406 1,244,172 6,220,861 26 Tennessee 372,224 0.5200 1,196,814 5,984,070 27 Missouri 360,933 0.5042 1,160,510 5,802,550 28 Indiana 353,501 0.4938 1,136,614 5,683,069 29 Arkansas 310,583 0.4339 998,619 4,993,096 30 Idaho 263,235 0.3677 846,381 4,231,905 31 Mississippi 253,720 0.3544 815,787 4,078,937 32 Iowa 242,041 0.3381 778,236 3,891,179 33 North Dakota 241,328 0.3371 775,943 3,879,716 34 Messachusetts 224,656 0.3138 722,338 3,611,689 35 Maryland 215,826 0.3015 693,947 3,469,733 36 Montana 212,692 0.2971 683,870 3,419,349 37 New Mexico 205,590 0.2872 661,035 3,305,173 38 South Carolina 183,283 0.2560 589,311 2,946,554 39 Conneticut 182,299 0.2547 586,147 2,930,735 40 Nebraska 173,035 0.2417 556,360 2,781,802 41 D.C 150,914 0.2108 485,235 2,426,173 42 New Hampshire 133,710 0.1868 429,919 2,149,593 43 Delaware 131,294 0.1834 422,150 2,110,752 44 South Dakota 130,226 0.1819 418,716 2,093,582 Continued Table E- 1. Non-towered Airport Operation Estimation based on 2014 Ohio Flown Hour

134

Table E-1 Continued

45 Hawaii 121,406 0.1696 390,357 1,951,787 46 Kentucky 108,767 0.1519 349,719 1,748,596 47 Wyoming 98,730 0.1379 317,447 1,587,236 48 Maine 98,662 0.1378 317,228 1,586,142 49 West Virginia 72,766 0.1017 233,965 1,169,825 50 Puerto Rico 41,113 0.0574 132,191 660,954 51 Vermont 39,624 0.0554 127,403 637,016 52 Rhode Island 38,277 0.0535 123,072 615,361 SUM: 373,609,857

135

Appendix F: The Longest Runways at Airports in Ohio [33], [34]

136

RWY RWY RWY FAA Airport name Role/Type Tower Ops Length Width Name (ft) (ft) Akron-Canton Regional CAK P-S Yes 80528 RWY 5/23 8204 150 Airport Cincinnati Municipal RWY LUK P-N Yes 82574 6101 150 Lunken Airport 3R/21L Cleveland-Hopkins RWY CLE P-M Yes 116690 9956 150 International Airport 6R/24L Port Columbus International RWY CMH P-M Yes 124119 10113 150 Airport 10R/28L Rickenbacker International RWY LCK P-N Yes 37035 12102 200 Airport 5R/23L James M. Cox Dayton RWY DAY P-S Yes 53693 10901 150 International Airport 6L/24R TOL Toledo Express Airport P-N Yes 38439 RWY 7/25 10599 150 Youngstown-Warren YNG Regional P-N Yes 20807 RWY 14/32 9003 150 Airport / Youngstown ARS RWY BKL Burke Lakefront Airport CS Yes 49749 6604 150 6L/24R Erie-Ottawa International PCW Airport (Carl R. Keller CS No 26050 RWY 9/27 5646 100 Field) Cuyahoga County CGF Airport (Robert D. Shea R Yes 28841 RWY 6/24 5102 100 Field) TZR Bolton Field R Yes 74511 RWY 4/22 5500 100 Ohio State University RWY OSU R Yes 71094 5004 100 Airport 9R/27L Dayton-Wright Brothers MGY R No 89045 RWY 2/20 5000 100 Airport Butler County Regional HAO R No 61687 RWY 11/29 5500 100 Airport Lorain County Regional LPR R No 42610 RWY 7/25 5002 100 Airport 1G5 Medina Municipal Airport R No 79685 RWY 9/27 3556 75 TDZ Toledo Executive Airport R No 90700 RWY 14/32 5829 100 Willoughby Lost Nation LNN R No 45085 RWY 5/23 5028 100 Municipal Airport Akron Fulton International AKR GA No 26000 RWY 7/25 6336 150 Airport 2D1 Barber Airport GA No 9500 RWY 18/36 3500 80 3G4 Ashland County Airport GA No 8225 RWY 1/19 3501 75 HZY Ashtabula County Airport GA No 16886 RWY 9/27 5197 100 Ohio University UNI GA No 52734 RWY 7/25 5600 100 Airport (Snyder Field) Barnesville-Bradfield 6G5 GA No 10150 RWY 9/27 4003 65 Airport I69 Clermont County Airport GA No 30650 RWY 4/22 3566 75 Bellefontaine Regional EDJ GA No 8325 RWY 7/25 5000 100 Airport Continued Table F- 1. List of the Largest Runways at Airports in Ohio

137

Table F-1 Continued 5G7 Bluffton Airport GA No 71980 RWY 5/23 4126 75 1G0 Wood County Airport GA No 27405 RWY 10/28 4199 75 0G6 Williams County Airport GA No 12010 RWY 7/25 4782 75 Port Bucyrus-Crawford 17G GA No 24871 RWY 4/22 3895 75 County Airport 8G6 Harrison County Airport GA No 11900 RWY 13/31 4154 75 Cambridge Municipal CDI GA No 6040 RWY 4/22 4298 75 Airport Carroll County-Tolson TSO GA No 34950 RWY 7/25 4300 75 Airport CQA Lakefield Airport GA No 16212 RWY 8/26 4400 75 HTW Lawrence County Airpark GA No 41910 RWY 8/26 3001 70 RZT Ross County Airport GA No 50150 RWY 5/23 5405 100 Pickaway County Memorial CYO GA No 35450 RWY 1/19 4346 75 Airport I40 Richard Downing Airport GA No 19550 RWY 4/22 5001 75 Greene County-Lewis A. I19 GA No 42900 RWY 7/25 4500 75 Jackson Regional Airport DFI Defiance Memorial Airport GA No 9130 RWY 12/30 4199 72 DLZ Delaware Municipal Airport GA No 39300 RWY 10/28 5000 100 02G Columbiana County Airport GA No 31156 RWY 7/25 3503 75 FDY Findlay Airport GA No 24550 RWY 18/36 6499 100 Fostoria Metropolitan FZI GA No 7900 RWY 9/27 5005 100 Airport Sandusky County Regional S24 GA No 5616 RWY 6/24 5500 100 Airport GQQ Galion Municipal Airport GA No 6216 RWY 5/23 3504 75 Gallia-Meigs Regional GAS GA No 19800 RWY 5/23 3999 75 Airport GEO Brown County Airport GA No 8212 RWY 18/36 3530 65 I67 Cincinnati West Airport GA No 30197 RWY 1/19 2803 60 HOC Highland County Airport GA No 15850 RWY 5/23 3520 75 I43 James A. Rhodes Airport GA No 6053 RWY 1/19 5201 75 89D Kelleys Island Land Field GA No 25495 RWY 18/36 2270 35 Kent State University 1G3 GA No 72500 RWY 1/19 4000 69 Airport I95 Hardin County Airport GA No 6562 RWY 4/22 4797 75 LHQ Fairfield County Airport GA No 43066 RWY 10/28 5004 75 Lebanon-Warren County I68 GA No 31525 RWY 1/19 4502 65 Airport AOH Lima Allen County Airport GA No 32500 RWY 10/28 6000 150 UYF Madison County Airport GA No 41410 RWY 9/27 4001 75 Mansfield Lahm Regional MFD GA RWY 14/32 9001 150 Airport MNN Marion Municipal Airport GA No 42650 RWY 7/25 5000 100 MRT Union County Airport GA No 31886 RWY 9/27 4218 75 Continued

138

Table F-1 Continued 22I Vinton County Airport GA No 5225 RWY 9/27 3725 75 3T7 Middle Bass Island Airport GA No 6500 RWY 10/28 1852 75 7G8 Geauga County Airport GA No 7450 RWY 11/29 3500 65 Middletown Regional MWO Airport (Hook Field) (was GA No 40050 RWY 5/23 6100 100 Hook Field Muni) 10G Holmes County Airport GA No 21500 RWY 9/27 4400 65 4I9 Morrow County Airport GA No 22608 RWY 10/28 3497 65 4I3 Knox County Airport GA No 20150 RWY 10/28 5498 100 7W5 Henry County Airport GA No 15637 RWY 10/28 4001 65 I86 Perry County Airport GA No 4550 RWY 8/26 3498 75 PHD Harry Clever Field GA No 54880 RWY 14/32 3951 100 VTA Newark-Heath Airport GA No 12457 RWY 9/27 4649 75 3X5 North Bass Island Airport GA No 1000 RWY 1/19 1804 60 Norwalk-Huron County 5A1 GA No 10100 RWY 10/28 4210 75 Airport OWX Putnam County Airport GA No 11910 RWY 9/27 4504 75 OXD Miami University Airport GA No 16708 RWY 5/23 4011 70 Greater Portsmouth PMH GA No 45830 RWY 18/36 5001 100 Regional Airport 3W2 Put-in-Bay Airport GA No 15125 RWY 3/21 2870 75 29G Portage County Airport GA No 9071 RWY 18/36 1800 85 Springfield-Beckley SGH GA No 9245 RWY 6/24 9009 150 Municipal Airport 2G2 Jefferson County Airpark GA No 15969 RWY 14/32 4401 60 16G Seneca County Airport GA No 60165 RWY 6/24 4000 75 56D Wyandot County Airport GA No 7410 RWY 18/36 3997 75 I74 Grimes Field GA No 23480 RWY 2/20 4400 100 VNW Van Wert County Airport GA No 20516 RWY 9/27 4000 75 VES Darke County Airport GA No 9238 RWY 9/27 4512 75 Wadsworth Municipal 3G3 GA No 15325 RWY 2/20 3529 5 Airport AXV Neil Armstrong Airport GA No 9805 RWY 8/26 5500 100 62D Warren Airport GA No 14738 RWY 4/22 2907 30 I23 Fayette County Airport GA No 29405 RWY 5/23 5097 75 USE Fulton County Airport GA No 21123 RWY 9/27 3882 75 EOP Pike County Airport GA No 2012 RWY 7/25 4899 75 AMT Alexander Salamon Airport GA No 5210 RWY 5/23 3762 65 I66 Clinton Field GA No 27860 RWY 3/21 3579 65 4G5 Monroe County Airport GA No 3324 RWY 7/25 3805 75 BJJ Wayne County Airport GA No 96520 RWY 10/28 5189 100 Zanesville Municipal ZZV GA No 33312 RWY 4/22 5000 150 Airport Continued

139

Table F-1 Continued 0D7 Ada Airport No 555 RWY 9/27 1955 110 1D4 Mayfield Airport No 450 RWY 9/27 2315 110 4G3 Miller Airport No 3500 RWY 9/27 2912 50 7B4 Miller Farm Landing Strip No 360 RWY 9/27 3250 50 2D7 Beach City Airport No 6112 RWY 10/28 3175 115 3D8 Bordner Airport No 2200 RWY 9/27 703 30 I62 Brookville Air-Park No 29359 RWY 9/27 2500 30 I10 Noble County Airport No 5950 RWY 5/23 3811 65 5D6 Parsons Airport No 1662 RWY 9/27 2530 75 6CM Chapman Memorial Field No 4000 RWY 9/27 3200 80 03I Clarks Dream Strip No 2520 RWY 13/31 2375 90 5D9 Bandit Field Airdrome No 140 RWY 18/36 2576 80 RWY 4G8 Columbia Airport No 5150 3152 40 18R/36L Columbus Southwest 04I No 11833 RWY 6/24 2382 120 Airport I73 Moraine Airpark No 19188 RWY 8/26 3500 65 Dahio Trotwood I44 Airport (Dayton-New No 1853 RWY 4/22 2900 52 Lebanon Airport) Deshler Municipal Landing 6D7 No 2000 RWY 9/27 2480 70 Strip 1G1 Elyria Airport No 14300 RWY 9/27 3053 48 14G Fremont Airport No 38450 RWY 9/27 4137 60 7D8 Gates Airport No 4200 RWY 8/26 2800 100 7D9 Germack Airport No 840 RWY 1/19 3260 28 82D Weiker Airport No 320 RWY 18/36 1744 90 88D Hinde Airport No 1850 RWY 11/29 3081 70 92D Harlan Airfield No 1155 RWY 9/27 2670 95 R47 Ruhe's Airport No 5000 RWY 9/27 4455 65 I71 Morgan County Airport No 5625 RWY 12/30 3500 65 Middle Bass-East Point 3W9 No 1300 RWY 9/27 2085 67 Airport 6G4 Wynkoop Airport No 5525 RWY 6/24 3355 90 O74 Elliot's Landing Airport No 1560 RWY 15/33 2750 110 41N Braceville Airport No 425 RWY 1/19 3000 30 2G1 Concord Airpark No 4510 RWY 2/20 2181 38 Ohio Dusting Company 6C2 No 3020 RWY 9/27 3112 150 Airport 2H8 Paulding Airport No 2100 RWY 18/36 2861 80 3I7 Phillipsburg Airport No 68000 RWY 3/21 3000 40 Piqua I17 No 10200 RWY 8/26 3998 75 Airport (Hartzell Field) 5E9 Packer Airport No 3181 RWY 9/27 3470 90 Continued

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Table F-1 Continued RWY 38D Salem Airpark No 16920 3404 50 10L/28R 8G8 Koons Airport No 2546 RWY 9/27 1850 100 3G6 Tri-City Airport No 10555 RWY 17/35 2768 45 12G Shelby Community Airport No 2012 RWY 18/36 3174 50 2P7 Alderman Airport No 6150 RWY 1/19 2840 50 Eddie Dew Memorial 1G8 No 3540 RWY 16/34 2268 145 Airpark I54 Mad River Airport No 15350 RWY 9/27 3405 110 37I Troy Skypark No 4264 RWY 5/23 3450 100 1WF Waco Field No 1500 RWY 18/36 2428 100 38I Weller Airport No 300 RWY 9/27 2534 75 15G Weltzien Skypark No 79130 RWY 3/21 2410 37 I64 Wakeman Airport No 10115 RWY 4/22 3800 55 40I Red Stewart Airfield No 16800 RWY 8/26 3142 150 67D Reader-Botsford Airport No 18700 RWY 18/36 2850 100 80G Tri-City Airport No 8085 RWY 10/28 3000 100 8G1 Willard Municipal Airport No 2715 RWY 10/28 4028 65 RWY ILN Airborne Airpark 10701 150 4L/22R 2B6 Hollister Field No 250 RWY 5/23 3203 80 04G Lansdowne Airport No 3500 RWY 2/20 3073 50 42I Parr Airport No 16025 RWY 10/28 3100 26

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Appendix G: Calculation of VMC and IMC operations based on flown hours

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VMC IMC Total TWR GA VMC Operation IMC Operation Year Flown Flown Flown Calculation Hrs/Ops VMC RE IMC RE Ops Estimation Estimation Hours Hours Hours

24,801,626 hrs 2010 21,658,977 3,142,649 24,801,626 26,475,482 0.94hrs/ops 23,120,736 3,354,746 9 1 26,475,482 ops 24,602,297 hrs 2011 21,355,468 3,246,829 24,602,297 25,957,089 0.95hrs/ops 22,531,465 3,425,624 12 1 25,957,089 ops 24,402,967 hrs 2012 21,051,958 3,351,009 24,402,967 25,954,531 0.94hrs/ops 22,390,462 3,564,069 14 1 25,954,531 ops 22,875,950 hrs 2013 19,490,725 3,385,225 22,875,950 25,855,562 0.88hrs/ops 22,029,409 3,826,153 15 1

25,855,562 ops 1

43 23,271,185 hrs 2014 19,679,375 3,591,810 23,271,185 25,622,176 0.91hrs/ops 21,667,500 3,954,676 13 1

25,622,176 ops

SUM 111,739,571 18,125,269 63 5 Table G- 1. Calculation of VMC and IMC Operation Estimation

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