National Transportation Safety Board Research Articles

A data-driven conceptual framework for understanding the nature of hazards in railway accidents

Hazards threaten railway safety by their potential to trigger railway accidents, resulting in significant costs and impacting the public's willingness to use railways. Whilst many prior works investigate railway hazards, few offer a holistic view of hazards across jurisdictions and time because the large number of primary sources make synthesising such learnings time consuming and potentially incomplete. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic modelling for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. The results allow the different aspects of each hazard to be listed along with the potential combinations of hazards that could trigger railway accidents. Better understanding of the aspects of individual hazards and the relationships between hazards and previous accidents can inform more effective hazard mitigation policies including technical or regulatory interventions. A case study of the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability to better inform policymaking for managing risks. The primary contributions of the framework proposed are to enable a large amount of knowledge accumulated to be summarised for an intuitive policymaking process, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could apply the technique to road, aviation or maritime accidents.

Survival After Ditching in Motorized Aircraft, 1989-2022.

INTRODUCTION: Although an unintended aircraft landing on water (referred to as ditching) is a rare event, the potential for occupant injury/fatality increases immediately following the event due to adverse conditions. However, to date, few studies have addressed the subject. Herein, ditching events and post-ditching survival were investigated.METHODS: Ditchings (1982-2022) in the United States were identified from the National Transportation Safety Board database. Occupant injury severity, aircraft type, pilot experience, flight conditions, and number of occupants were extracted. Poisson distribution, the Chi-squared test (2-tailed), Mann-Whitney U test, and Kruskal-Wallis one-way analysis of variance were employed.RESULTS: A total of 96 ditchings were identified. A systematic survey was hampered by the lack of a standardized reporting matrix in the reports. In total, 77 reports were included in the analysis. Across all ditchings, 128 of 169 (76%) occupants survived ditching and were rescued. Importantly, the initial ditching event was survived by 95% of all occupants. However, 32 (19%) occupants died post-ditching by drowning (21/32 cases) or for undetermined reasons. Considering probability per ditching event, in 26 (34%) of all ditchings, one or more occupants was/were fatally injured.DISCUSSION: Initial survival of the emergency ditching is high. Drowning was the leading cause of death after ditching and reduced the overall survival to 76%. Further investigation is needed to identify risk factors for fatal outcomes and/or improve probability of survival after ditching.Schick VC, Boyd DD, Hippler C, Hinkelbein J. Survival after ditching in motorized aircraft, 1989-2022. Aerosp Med Hum Perform. 2024; 95(5):254-258.

Nose-Over and Nose-Down Accidents in General Aviation: Tailwheels and Aging Airplanes

Safety in General Aviation has been a continuous concern. About 12% of all airplane accidents in General Aviation involve nose-overs and nose-down events. A total of 134 accidents reported by the National Transportation Safety Board that include nose-overs and nose-downs were analyzed for their main causes. It was found that 35% of the defining events involved a loss of control on the ground while 58% of the total dataset involved tailwheel-type aircraft. A relatively high proportion of aircraft built before 1950 were found, which are also aircraft that have tailwheel-type landing gear, and thereby a higher propensity for ground loops and nose-overs. It is shown that the high accident rate in General Aviation, especially for accidents that did not result in a fatality, was, to an important extent, explained by tailwheel and older aircraft in the US General Aviation airplane fleet struggling with controlling the aircraft on the ground. Attention to this group of aircraft in future studies may help to more effectively address the relatively high accident rates in General Aviation.

Transportation of hazardous material via railroad: Incident investigation and a case study of derailment in 2023

AbstractRailway transportation of hazardous materials (HAZMAT) is common and is generally considered safe. However, transporting toxic, flammable, and explosive substances via railways carries significant risk due to their high volume, proximity to populated areas, low public awareness, and potential domino effect. Particularly, the practice has come into question after a Norfolk Southern train derailed in Ohio in February of 2023. This derailment continuously reminds the public and the industry that such incidents can profoundly affect a community, critical infrastructure, and the environment. To identify the root cause of the Ohio train derailment and discover the deficiency of the safety system applied, combining the cause mapping approach with the safety triad concept was employed in this study. Based on this approach and the preliminary incident investigation released by the National Transportation Safety Board, the incident sequence is established, and the causal events leading to this incident are identified in the three essential pillars of its safety system: prevention, mitigation, and response, respectively. The study subsequently develops recommendations to improve the safety system of HAZMAT freight trains. This is expected to lower further the probability and consequence of HAZMAT freight train incidents and ultimately result in long‐term changes in railroad transportation.

Discovering latent themes in aviation safety reports using text mining and network analytics

Aviation accidents, referring to unexpected and undesirable events involving aircraft, often cause great damage to property and human life. Learning from historical accidents is pivotal for improving safety in aviation. However, aviation accidents are typically documented and stored as unstructured or semi-structured free-text, rendering the ability to analyze such data a difficult task. This study presents a novel framework that combines text mining and network analytics techniques and provides the ability to analyze aviation accident reports automatically. The framework comprises a four-step modelling approach to: (1) the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix; (2) the identification of aviation accident topics using a Structural Topic Model (STM); (3) the production of a Word Co-occurrence Network (WCN) to determine the interrelations between aviation safety risk factors; and (4) quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety events. The proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board (NTSB). The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional Latent Dirichlet Allocation (LDA). Among the identified topics, “Fuel and Power” and “En-route Phase” have the highest occurrence rate according to STM. Additionally, “Aircraft Crash” is the most prevalent topic in aviation accidents that resulted in fatal injuries, while “Landing phase” is the most prevalent topic in non-fatal injuries on accidents. Based on the word co-occurrence network, three centrality measures highlight “inspection of equipment” and “take off” as the most important risk factors in aviation safety. The proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports, offering decision support for aviation safety management and accident prevention, thereby reducing risks and strengthening safety measures.

Assessment of aviation accident datasets in severity prediction through machine learning

The importance and seriousness of civil aviation safety have become more noticeable due to the intricate advancement of air transportation. It is crucial to apply diverse datasets to evaluate and anticipate the degree of aviation safety. Data on the different causes of accidents can be analyzed and applied to predict and prevent potential accidents. The main goal of this research is to explore how Machine Learning (ML) techniques can be used to identify correlations and connections between various contributing factors in aviation accidents. It applies ML algorithms on datasets extracted from original data sourced from the National Transportation Safety Board (NTSB) of the United States, Transportation Safety Board (TSB) of Canada, and Australian Transport Safety Bureau (ATSB) aviation accident records spanning from January 2008 to January 2023. Initially, this study uses Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM) models to predict the civil aviation accident rates for the United States, Canada, and Australia data separately. Sequentially, by extracting Common Features (CF) and reclassifying the Common Sub-Features (CSF) of these three countries, a comprehensive model is created. Finally, it evaluates the differences and limitations of these countries' datasets. The results show that the separate models exhibit a higher level of prediction accuracy and lower errors compared to the common models and the best results (81% accuracy) were achieved through SVM.

Aircraft accident reports: A corpus-based genre analysis

Based on a corpus of ‘Aircraft Accident Reports’ (AARs) collected from the official website of The National Transportation Safety Board (NTSB), issued during the years 2000–2021, this study used Bhatia’s Critical Genre Analysis (CGA) framework to examine how this type of techno-professional discourse is recognized by considering the text, the genre, the professional practice, and the professional culture of the corpus. It was found that (1) textually, both Move2 collecting information, and Move3 Analyzing Information are the lengthiest moves within the corpus regarding the number of tokens, while Move1 Summarizing the Accident, Move4 Reaching Conclusion, Move5 Sharing Recommendations, and Move6 Board Member Statement are the shortest in length; (2) the generic move structure of the corpus contained six-move pattern (five of them are obligatory, and only one is optional), 11 steps, and 40 sub-steps; (3) the professional practice of the corpus is expressed through two types of discursive processes, they are: genre-mixing, and genre-embedding; and (4) the professional culture is presented through a set of professional values imbedded in the corpus, they are: embracing change and learn from mistakes, and maintaining objectivity and impartiality. The results of this study contribute in understanding the realization of professional discourse and expand the application of CGA in professional practices.

Amphibious Airplane Accidents: An Exploratory Analysis

Causes and contributing factors of amphibious airplane accidents are examined by comparing the proportion of fatal accidents for different causes of accidents, with a focus on landings on water and low-level flying maneuvers. A set of 183 accidents involving amphibious planes from 2005 to 2020 was extracted from the National Transportation Safety Board’s online database. Amphibious airplane accidents are reported to be fatal in 34% of cases, which is higher than the average of 20% for general aviation. Logistic regression analysis shows that the maneuvering flight phase and decision-making factors are significantly more often associated with fatal accidents than other flight phases and causes. In addition, the number of accidents associated with decision-making factors significantly increased during the studied time period. Amphibious airplanes benefit from accident analysis despite the absence of denominator data and the limitations of most general aviation accident reports. Intentional low-level flying is shown to be a central area of concern that may be addressed at the operational as well as the training level. Landing accidents could be avoided by introducing additional warning systems and training regarding (retractable) landing gear as well as general awareness training of decision-making during landings on water.

Wire Strikes and In-Air Obstacle Collisions During Agricultural Aviation Operations.

INTRODUCTION: Wire strikes and in-air collisions with obstacles are a leading cause of accidents in the aerial application industry. While some of these collisions occur due to unseen obstacles, some pilots report being previously aware of the obstacles that they collide with. Whether or not pilots are aware of obstacles pre-collision is an important factor to inform methods of accident prevention.METHODS: Final reports from the National Transportation Safety Board were analyzed for Part 137 Agricultural Operation accidents that took place between January 2020 and December 2022. A deeper analysis of cases that involved an in-air collision with an obstacle was performed, excluding cases that were attributable to an external cause (e.g., aerodynamic stall). The pilot's awareness of the obstacle pre-accident was inferred from accident narratives if available.RESULTS: Nearly half of all accidents (N = 45 of 107) involved an in-air collision with an obstacle (e.g., wire, tree, pole) as the defining event. In cases where pilot awareness of the obstacle was determinable through the accident report, over half of pilots (N = 21 of 39) had previously seen this obstacle yet still made contact with it.DISCUSSION: In-air obstacle collisions make up a substantial portion of accidents within Part 137 Agricultural Operations. Nearly half of pilots were already aware of the obstacle before collision, indicating that inadequate preparation in scoping the field is not a predominant driver of these events. Instead, these findings suggest that other factors including distractions, high task difficulty, and errors in decision-making may contribute.Baumgartner HM. Wire strikes and in-air obstacle collisions during agricultural aviation operations. Aerosp Med Hum Perform. 2023; 94(11):852-856.

Deficient Aeronautical Decision-Making Contributions to Fatal General Aviation Accidents.

INTRODUCTION: General aviation (GA), mainly comprised of light (≤12,500 lb) aircraft, maintains an inferior safety record compared with air carriers. To improve safety, aeronautical decision-making (ADM) practices have been advocated to GA pilots since 1991. Herein, we determined the extent to which GA pilots disregard such practices.METHODS: Fatal accidents (1991-2019) involving private pilots (PPLs) in single-engine airplanes were identified (N = 1481) from the National Transportation Safety Board AccessR database. Of these, deficient go/no-go and in-flight ADM-related mishaps were scored using the PAVE (pilot, aircraft, environment, external pressure)/IMSAFE (illness, medicine, stress, alcohol, fatigue, eating) and PPP (perceive, process, perform) models, respectively. Statistical testing used Poisson distributions, Fisher exact tests, and Mann-Whitney U-tests.RESULTS: Of the 1481 accidents, 846 were identified as deficient ADM-related. Electing to depart into a hazardous environment (PAVE), disregarding wellness (IMSAFE), and poor aircraft familiarity (PAVE) represented the most common categories (54%, 21%, and 20%, respectively) of errant go/no-go ADM. A 64% decline in fatal accidents related to errant go/no-go decisions for the environment category was evident over the 30-yr period, with little decrements in the other domains. Within the errant environment-related category accidents, the decision to depart into forecasted adverse weather (e.g., degraded visibility, icing, thunderstorms) constituted the most prevalent subcategory (56%, N = 195). Surprisingly, of this subcategory, accidents were overrepresented by over nine- and threefold for instrument-rated PPLs disregarding icing and thunderstorm forecasts, respectively.CONCLUSION: With little decrement in ADM-related accidents in the pilot, aircraft, and external pressure domains, new strategies to address such deficiencies for PPLs are warranted.Boyd DD, Scharf MT. Deficient aeronautical decision-making contributions to fatal general aviation accidents. Aerosp Med Hum Perform. 2023; 94(11):807-814.

The Five “I” Framework of crash investigation: Linking investigation practices to safety reform

Crash investigation is vital to the advancement of road safety, and it is a foundation for determining effective countermeasures and interventions for true road safety reform. However, the way in which road crash investigations are conducted substantially influences the quality of understanding and the effectiveness of responses. In crash investigations, we traditionally focus first on the question: “What caused this crash?” when it would be more efficient to ask immediately: “What could have prevented this crash?” or better yet, “What are all the ways this crash could have been prevented?”In this paper, we first explore a few common road crash investigation approaches where prosecution, retribution, or compensation are primary. We then examine investigation approaches where prevention is primary, especially investigations aimed at determining every point on the timeline preceding the crash where an intervention would have prevented the outcome, as illustrated by the Swiss Cheese Model. We draw from examples from the aviation industry, the occupational health and safety field, the U.S. National Transportation Safety Board (NTSB), and others, to identify strengths and weaknesses. We bring together good practices from several investigative approaches through the lens of diverse experiences in transportation safety and root-cause analysis to present a practical and proactive framework for road crash investigation. The Five “I” Framework provides guiding characteristics for prevention-focused safety investigations for road crashes: Immediate, In-Depth, Impartial, Independent, and Injury Prevention.The Five “I” Framework is a practical guide for investigations to move beyond crash causation to crash prevention, aligning with the Safe Systems Approach, Vision Zero, and the public health perspective. Rather than focusing primarily on on any single factor such as vehicle defect or driver error, it leads investigations to an array of countermeasures that involve collective action and systems change, and thus, to more effective road safety reform. Nevertheless, as a practical framework, it is the start (not the end) of discussions on how we can continue to move towards more multidisciplinary, collaborative, innovative, and ethical prevention efforts.

Nonlinear Time Series Analysis and Prediction of General Aviation Accidents Based on Multi-Timescales

General aviation accidents have complex interactions and influences within them that cannot be simply explained and predicted by linear models. This study is based on chaos theory and uses general aviation accident data to conduct research on different timescales (HM-scale, ET-scale, and EF-scale). First, time series are constructed by excluding seasonal patterns from the statistics of general aviation accidents. Secondly, the chaotic properties of multi-timescale series are determined by the 0–1 test and Lyapunov exponent. Finally, by introducing the sparrow search algorithm and tent chaotic mapping, a CSSA-LSSVM prediction model is proposed. The accident data of the National Transportation Safety Board (NTSB) of the United States in the past 15 years is selected for case analysis. The results show that the phase diagram of the 0–1 test presents Brownian motion characteristics, and the maximum Lyapunov exponents of the three scales are all positive, proving the chaotic characteristics of multi-timescale series. The CSSA-LSSVM prediction model’s testing results illustrate its superiority in time series predicting, and when the timescale declines, the prediction error reduces gradually while the fitting effect strengthens and then decreases. This study uncovers the nonlinear chaotic features of general aviation accidents and demonstrates the significance of multi-timescale research in time series analysis and prediction.

Review of the History of Mesoscale Convective System Forecasts on Aviation

Airports are important national resources, and Aviation Weather Services are critical to the aviation industry's success. According to the National Transportation Safety Board's (NTSB) analysis of weather-related circumstances that influence near- surface aircraft operations, wind and turbulence caused 1381 accidents, visibility, ceiling height (hc), and precipitation-related accidents occurred 485 times, and aircraft icing caused 150 accidents between 2003 and 2007. Mesoscale convective systems (MCSs) arise when cumulonimbus clouds merge into a single entity that can span hundreds of miles and continue for hours, posing a higher threat to aviation due to its size and duration. The mesoscale downdraft of a squall-line MCS's stratiform area sometimes merges with the convective downdrafts in the leading line of convection, and these mergers can produce strong effects, with the gust front surging forward and triggering new convection in the form of a “bow echo," according to Doppler radar. Bow echo events are of particular concern to forecasters because they are typically associated with strong, damaging surface winds. Because MCSs are still a major socioeconomic issue, it's critical to construct climate models that incorporate them, whether through cloud-resolving modeling or parameterization. MCS characteristics are influenced by the increasingly contaminated aerosol environment in most parts of the world, and as the Earth warms, MCS patterns will certainly change.

Clear of Clouds? An Assessment of Appealed 91.155 Enforcement Actions

When flying under visual flight rules, pilots must remain clear of clouds. The exact distance varies by airspace class, and this is determined by specific regulatory requirements found within 14 C.F.R. Section 91.155. But there are important questions about the extent to which pilots comply with this regulation, as determining one’s exact distance from clouds is challenging. In this conference paper, we assess the decisions of the National Transportation Safety Board (NTSB) in 20 Federal Aviation Administration (FAA) legal enforcement actions involving a violation of cloud clearance requirements. Among these cases, we examine how 91.155 violations were discovered, the form of sanction imposed by the FAA, the timelines associated with appeals for these cases, and the vote composition of the NTSB in these decisions. 

Fatal communication: disaster at Quincy

Research methodology The technical report released by the National Transportation Safety Board, along with the primary flight cockpit voice recorder data and archival interview data, were used as the basis for this case. Other available public data such as news reports were used to round out the synopsis of the case study. Case overview/synopsis United Express Flight 5925 was a scheduled commuter passenger flight operated by Great Lakes Airlines with a Beechcraft 1900 twin turboprop. It was a regularly scheduled flight from Chicago O'Hare International Airport to Quincy, Illinois, with an intermediate stop in Burlington, Iowa. Drawing from various first-hand accounts (cockpit voice recorder) and secondary evidence (news reports, archival interview data, and online sources) of the tragedy, the case provides a detailed account of the key events that took place leading up to the accident at Quincy regional airport. The case describes how the radio interactions, a jammed door and degradation of situational awareness all contributed to the accident. Through many of the quotes in the text and eyewitness accounts, readers gain an understanding of the impressions and perceptions of the pilots, including how they felt about many of the critical decisions in the last minutes of the flight and the situation at the airport. Complexity academic level When the authors teach this case, the students are required to read it as pre-reading before class. Various readings and materials (see supplemental readings below and Exhibit 3) are made available to students before class, and the instructor can choose to use some of these materials to further explore areas of interest. This case is best explored over a 90-min session but could be expanded to take up one 3-h session. This case can be covered in an undergraduate senior capstone organizational behaviour seminar, any general organizational behaviour class (including introductory in nature), an undergraduate communication theory class or an MBA class that focuses on applied organizational behaviour concepts. It works particularly well in the MBA class, as students with work experience can make the links between the behaviours explored in the case and their everyday workplaces.

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

AbstractRepositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open‐source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state‐of‐the‐art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

How the black hole illusion environment affects operational performance at different flight phases in aviation

The black hole illusion (BHI) is a subtype of spatial disorientation that can result in fatal consequences in aviation. Research on the BHI has generally focused on altitude deviation, and few studies have examined the effect across different flight phases. In a simulation-based experiment, 18 participants performed 12 simulated approach and landing tasks in normal and BHI environments. Flight performance was analyzed with 14 flight parameters and was compared across five points and three phases, which were referenced from a National Transportation Safety Board report and other previous studies. Results showed that multiple flight parameters were significantly impaired and that their influences varied from the initial approach to the final touchdown. In the BHI environment, participants tended to descend aggressively during the approach phase and flew a lower but similar glidepath during the last approach phase. They might have realized the abnormal situation induced by the BHI but usually were unable to recover from the dangerous maneuver in time. Additionally, the result of glide path error, one of the most commonly used variables in previous BHI research, was only significant during the last approach phase. Flight stability was also impaired in the BHI environment. This is the first study to systematically analyze the BHI effects on multiple flight parameters at different flight phases. The use of this experimental paradigm could facilitate future research to evaluate and prevent the BHI in a more comprehensive way.

Business Case for Transit Bus Automated Collision Avoidance Systems

Although the National Transportation Safety Board has strongly recommended the application of collision avoidance warning systems and automated emergency braking (CAWS/AEB), the transit industry has lagged behind autos and trucks. CAWS/AEB for buses is more complex than for cars and trucks and represents a niche market. If bus manufacturers and suppliers are to invest in developing this technology, they will need to be convinced there is a market for it. Transit agencies also will need to be convinced that the technology works and is affordable. For both vendors and purchasers, a business case could be made that some or all of the expense of procuring and deploying the technology can be recovered through savings from reduced insurance premiums and claims payouts by preventing or mitigating collisions. This paper describes two methods for estimating the business case for equipping transit buses with CAWS/AEB. First, industrywide data on the numbers of collisions and casualty and liability expenses incurred by transit agencies were examined, and statistical relationships were modeled to determine whether reducing the numbers of collisions would reduce casualty and liability expenses. Second, individual collision claims records for specific transit agencies were examined to determine whether the collisions could have been prevented by CAWS/AEB, and the potential savings.

High-Performance Perception: A camera-based approach for smart autonomous electric vehicles in smart cities

Mobility is fundamental for the wealth and health of the world’s population, and it has a significant influence on our daily life. However, with the increasing complexity of traffic, the need to transport goods, and growing urbanization, improving the quality of mobility in terms of time, space, and air becomes more challenging. An autonomous electric vehicle offers technology and potential for new mobility concepts in smart cities. Today, many vehicles have been developed with automated driving capabilities. A safety driver is still required to intervene in most cases if the autonomous electric vehicle is not able to handle a situation in a safe and, at the same time, reliable way. One important aspect to achieve safety and reliability goals is a robust and efficient perception of the vehicle’s environment. The tragic accident involving an Uber self-driving car killing a pedestrian in 2018 highlighted the importance of perception in autonomous driving. In its investigation, the U.S. National Transportation Safety Board found that the Uber self-driving car and its safety driver involved in the accident failed to detect the pedestrian at the same time. Since this accident, the autonomous vehicle industry has been working to improve perception systems through the use of advanced sensors, machine learning algorithms, and other technologies. A variety of sensor technologies are used in vehicles to detect objects and perceive the vehicles’ surroundings. Cameras and radars are among the widely used technologies for sensing systems, as they are cheap and reliable, and as these sensors are operating at different wavelengths, they are not susceptible to common errors. While some companies solely use cameras and radars, others also use lidars in their sensing systems. These sensors are widely used in the industry, and the technology itself is continuously enhanced, leading to new developments, such as 4D lidar, which are capable of measuring not only the distance of an object but also its velocity by evaluating the phase shift of the returned light. Various methods for the perception of the environment exist and rely on different kinds of sensors. Machine learning-based methods have evolved rapidly in recent years and are currently leading the field in perception, particularly in the tasks of object detection and classification.

A Regional Approach to Aviation Accident Analysis in Hawaii

BACKGROUND: The geographical circumstances, such as mountains and ocean, and specific aviation operations, especially sightseeing, make the state of Hawaii stand out in aviation. These conditions support a regional approach to aviation accident analysis.METHODS:Accident reports of aviation accidents collected from the online National Transportation Safety Board database were used to study a 10-yr time period between 2008 and 2017.RESULTS: There was a significantly higher proportion of fatal accidents during night, dawn, and dusk (6 out of 13) than during daytime (13 out of 74). In addition, a significantly higher proportion of accidents occurred in diminished light conditions among fixed wing airplanes (11 out of 48) as opposed to other aircraft (2 out of 39), and among twin-engine aircraft (6 out of 12) as opposed to single-engine aircraft (7 out of 74). Out of seven weight-shift control aviation accidents, four were reported to be fatal; the latter all took place during instruction.DISCUSSION: Light conditions are the main environmental concern in Hawaiian aviation that particularly affect twin-engine fixed wing aircraft and warrant specific attention in advanced training exercises. Helicopter operations have not exhibited a diminished safety record since the 1990s, showing a lasting effect of a previous safety intervention. A relatively high number of fatal weight-shift control aircraft accidents requires further research in other parts of the United States.de Voogt AJ, Brause J. A regional approach to aviation accident analysis in Hawaii. Aerosp Med Hum Perform. 2023; 94(3):131-134.