<p>General Aviation (GA) is a category of aircraft operations, exclusive of all military and commercial operations. According to Federal Aviation Administration (FAA), fixed-wing aircraft (also known as airplanes) account for 76.2% of all the estimated registered GA fleet in the United States. Out of all the GA accidents that the National Transportation Safety Board (NTSB) investigated in 2017, 87.7% of the accidents involved fixed-wing aircraft. The NTSB reports on all GA accidents and records the accident details in their database. The NTSB database has an abundance of accident data, but the data is not always logically complete and has missing information. Many researchers have conducted several studies to provide GA fixed-wing accident causation using the NTSB accident data. The quantitative analyses conducted by the researchers focused on a chain of events approach and identified the most frequent events in accidents. However, these studies provided little insight into why the events in the accidents happened. In contrast, the qualitative analyses conducted an in-depth study of limited accidents from the NTSB database. This approach helps in providing new findings but is difficult to apply to large scale datasets. Therefore, our understanding of GA fixed-wing accident causation is limited. This research uses a state-based approach, developed by Rao (2016), to provide a potentially better understanding of causes for GA fixed-wing accidents. I analyzed 10,500 fixed-wing accidents in 1982–2017 that involved inflight loss of control (LOC-I) using the state-based approach. I investigated the causes of LOC-I using both a conventional approach and a state-based approach. I analyzed fatal, non-fatal and overall LOC-I accidents in three timeframes: 1989–1998, 1999–2008 and 2008–2017. This multi-year analysis helped in discerning changes in the causation trends in the last three decades. A mapping of the LOC-I state definition to the NTSB codes helped in identifying 2350 more accidents in the database that were not discernible using the conventional approach. The conventional analysis revealed “directional control not maintained” as the top cause for the LOC-I accidents, which provides little information about how loss of control happened in accidents. The state-based analysis highlighted some important findings that contribute to LOC-I accidents that were not discernible using the conventional approach. The state-based analysis identified preflight mechanical issue as one of the new causes for LOC-I with a presence in 5.1% of LOC-I accidents in 2009–2017. It also helped in inferring some of the missing information in the accident data by modeling the accidents in a logical order. Using the logic rules in the state-based approach, I inferred that the pilot’s tendency to hit objects or terrain caused loss of control in 19.9% of LOC-I accidents in 2009–2017. Further, the logic rules helped in inferring that 7.5% of LOC-I accidents in 2009–2017 involved hazardous condition of an aircraft before the start of flight. A comparison of the findings from state-based approach with the GAJSC (General Aviation Joint Steering Committee) safety enhancements revealed that the state-based approach encompassed all the potential issues addressed in the safety enhancements. Additionally, a state-based analyses of larger datasets of fatal and non-fatal accidents suggested some new potential issues (such as improper maintenance) that were not explicitly addressed in the GAJSC safety enhancements. </p>

General aviation in the United States has been an important part of the economy and American life. General aviation is defined as all flying excluding military and scheduled airline operations, and is utilized in many areas of our society. The majority of aircraft operations and airports in the United States are categorized as general aviation, and general aviation contributes more than one percent to the United States gross domestic product each year. Despite the many benefits of general aviation, the lead emissions from aviation gasoline consumption are of great concern. General aviation emits over half the lead emissions in the United States or over 630 tons in 2005. The other significant negative externality attributed to general aviation usage is aircraft accidents. General aviation accidents have caused over 8000 fatalities over the period 1994 - 2006. A recent Federal Aviation Administration proposed increase in the aviation gasoline tax from 19.4 to 70.1 cents per gallon has renewed interest in better understanding the implications of such a tax increase as well as the possible optimal rate of taxation. Few studies have examined aviation fuel elasticities and all have failed to study general aviation fuel elasticities. Chapter one fills that gap and examines the elasticity of aviation gasoline consumption in United States general aviation. Utilizing aggregate time series and dynamic panel data, the price and income elasticities of demand are estimated. The price elasticity of demand for aviation gasoline is estimated to range from -0.093 to -0.185 in the short-run and from -0.132 to -0.303 in the long-run. These results prove to be similar in magnitude to automobile gasoline elasticities and therefore tax policies could more closely mirror those of automobile tax policies. The second chapter examines the costs associated with general aviation accidents. Given the large number of general aviation operations as well as the large number of fatalities and injuries attributed to general aviation accidents in the United States, understanding the costs to society is of great importance. This chapter estimates the direct and indirect costs associated with general aviation accidents in the United States. The indirect costs are estimated via the human capital approach in addition to the willingness-to-pay approach. The average annual accident costs attributed to general aviation are found to be $2.32 billion and $3.81 billion (2006 US$) utilizing the human capital approach and willingness-to-pay approach, respectively. These values appear to be fairly robust when subjected to a sensitivity analysis. These costs highlight the large societal benefits from accident and fatality reduction. The final chapter derives a second-best optimal aviation gasoline tax developed from previous general equilibrium frameworks. This optimal tax reflects both the lead pollution and accident externalities, as well as the balance between excise taxes and labor taxes to finance government spending. The calculated optimal tax rate is $4.07 per gallon, which is over 20 times greater than the current tax rate and 5 times greater than the Federal Aviation Administration proposed tax rate. The calculated optimal tax rate is also over 3 times greater than automobile gasoline optimal tax rates calculated by previous studies. The Pigovian component is $1.36, and we observe that the accident externality is taxed more severely than the pollution externality. The largest component of the optimal tax rate is the Ramsey component. At $2.70, the Ramsey component reflects the ability of the government to raise revenue aviation gasoline which is price inelastic. The calculated optimal tax is estimated to reduce lead emissions by over 10 percent and reduce accidents by 20 percent. Although unlikely to be adopted by policy makers, the optimal tax benefits are apparent and it sheds light on the need to reduce these negative externalities via policy changes.

This diploma thesis examines the influence of wind on civil aviation safety. Author investigates the methods of wind measuring on board of small airplane and identifies the most common individual errors.

This Master´s thesis examines Investigation of aircraft accidents / incidents, which were caused by human error. My thesis is focused on fixed wing aircrafts with maximum take off weight up to 2500 kg. Practices of Aircraft accident / incident Investigation are described in Annex L13 which is document published by Ministry of Transport of the Czech Republic. This document provides some support for investigators but does not contain methodology which should be used to discover human error by which the accident / incident was caused. This thesis has two major purposes. First purpose is to analyze Final reports of investigations and suggest improvements which can be made. Second goal of this thesis is to create own methodology for investigating the causes of aviation accidents / incidents caused by human factor.

<p>General Aviation (GA) encompasses all aircraft operations, excluding scheduled, military, and commercial operations. GA accidents comprise approximately 94% of all aviation accidents in the United States annually. 75% of these accidents involve pilot-related factors (pilot actions or conditions). Inflight loss of control means that the flight crew was unable to maintain control of the aircraft in flight. With almost 50% of loss of control accidents being fatal yearly, it continues to be the deadliest cause of GA accidents.</p> <p><br></p> <p>The most common approach to understanding accident causation is analyzing historical data from sources such as the National Transportation Safety Board (NTSB) database. The NTSB database has abundant rich information. In contrast to the extensive investigations into and detailed reports on commercial aviation accidents, GA accident investigations tend to be shorter, and the resulting reports tend to be brief and limited—especially regarding human factors’ role in accidents. Only relying on historical data cannot provide a complete understanding of accident causation.</p> <p><br></p> <p>There is a clear need to better understand the role of human factors involved in GA accidents to prevent such accidents and thus improve aviation safety. In my research, I focus on a specific type of accidents, inflight loss of control (LOC-I), the deadliest cause of GA accidents. I use historical data analysis and human-subjects research with pilots to investigate the role of human factors in loss of control accidents. Building on previous work, I created a state-based modeling framework that maximizes data extraction and insight formation from the NTSB accident reports by (1) developing a structured modeling language to represent accident causation in the form of states and triggers; (2) populating the language lexicon of states and triggers using insights from accident reports and pilots perspectives via surveys and interviews; and (3) applying Natural Language Processing (NLP) and machine learning techniques to automatically translate accident narratives into the language lexicon. The framework is focused on LOC-I but can be extended to other types of accidents. Findings from my study may help in consistent accident analysis, better accident reporting, and improving training methods and operating procedures for GA pilots.</p>