Nicoletta Fala

Certified flight instructor (CFI) fatigue is a significant concern to both aviation safety and the quality of pilot training, as it can severely impair judgment, reaction times, and decision-making ability. This study combines a historical analysis of aviation accidents/incidents and survey questionnaires to investigate work-induced fatigue and burnout among CFIs. Our analysis of the ASRS database reveals a rising trend in safety reporting of incidents, particularly within operations under 14 CFR Part 91 (General Operating and Flight Rules). Analysis of the NTSB database shows that flight crew fatigue poses critical safety concerns leading to fatal accidents. Our survey findings indicate that fatigue is a prevalent issue among CFIs, with many experiencing it regularly, while burnout is less frequent but still considerable. The risk of burnout for CFIs increases with their fatigue levels, with some CFIs knowingly flying while fatigued. Flight students recognize the detrimental effects of CFI fatigue on training quality and safety. The findings indicate that work-induced fatigue is not solely an individual concern but a systemic challenge requiring collaborative efforts from flight schools, regulatory authorities, and CFIs.

This paper presents a lightweight simulation framework for rapidly assessing the risk and traffic impact of integrating uncrewed aerial vehicles (UAVs) with various trajectories into the National Airspace System. The framework combines agent-based modeling and Monte Carlo simulation with real air traffic data to generate realistic traffic patterns and quantify integration risk through loss of separation events. We validate the methodology by applying it to three representative scenarios: a terminal area near an airport, a rural region, and a state with diverse traffic densities. These case studies demonstrate the framework’s capability to provide fast, computationally efficient risk estimates for UAV operations. While we initially designed this approach for weather data collection missions, the methodology applies broadly to any planned UAV operation requiring airspace integration assessment. The framework offers aviation planners and regulators a practical tool for evaluating the safety implications of adding new actors to the airspace system.

Accurate identification of phases of flight is an essential step in analyses such as airport operation counts, fuel burn estimation, and safety studies. Past research has focused primarily on using positional data with rule-based or probabilistic-based decision-making to identify the phases of flight. Many of these efforts note that the task of correctly identifying phases of flight is challenging, often requiring extreme fine-tuning of methods. In this paper, we initially study whether combinations of dimensionality reduction of flight data records from general aviation aircraft impact clustering into the correct flight phases (climb, cruise, or descent) without any preprocessing or fine-tuning. For dimensionality reduction, we considered the low variance filter, the high correlation filter, principal component analysis, and autoencoders. We found that these dimensionality reduction algorithms do not offer any benefit for the phase identification task, as compared to feature selection that simply omits engine-specific features. For the clustering task, we considered [ ]-means and Gaussian mixture models. After performing clustering on eight test flights, we conclude that both methods are adequate at identifying the phases of flight in various general aviation flights and yield similar results.

This study evaluates pilots' cognitive workload and situational awareness during remote small unmanned aircraft system operations in different wind conditions. To complement the urban air mobility concept that envisions safe, sustainable, and accessible air transportation, we conduct multiple experiments in a realistic wind-aware simulator-user interface pipeline. Experiments are performed with basic and wind-aware displays in several wind conditions to assess how complex wind fields impact pilots' cognitive resources. Post-hoc analysis reveals that providing pilots with real-time wind information improves situational awareness while decreasing cognitive workload.

Airports are tasked with counting and reporting their operations at least yearly. The counts are used at the local and national level to schedule maintenance, for research, and to receive funds, making their accuracy important. Historically, methods for counting operations at non-towered airports have relied on additional equipment at the airport or statistical estimates. In this work, we introduce a method to use crowd-sourced Automatic Dependent Surveillance – Broadcast (ADS-B) data from the OpenSky network to automatically count airport operations and report it separated by takeoffs and landings. We use two airports as case studies – Tulsa International Airport (TUL) and Purdue University Airport (LAF) – and compare the estimated operation counts from the ADS-B data algorithm to numbers reported through the Federal Aviation Administration’s (FAA) Air Traffic Activity Data System (ATADS). Keyword : airport operation counts, ADS-B, OpenSky, non-towered airports, crowd-sourced data, airport count models

Spin training has not been required for students working towards their private or commercial certificates for the past 70 years. Switching to a stall-prevention mindset within training aimed to make spin recovery unnecessary; however, stall-type accidents, consisting of stalls, spins, and spirals, still occur and are highly fatal. Although past studies have analyzed accidents, interviewed pilots at different levels, and made recommendations for changes in the industry, stall-type accidents are no less fatal now, at a fatality ratio of approximately 40–50% yearly. The research discussed in this paper aims to summarize and present accident stall-type statistics in aggregate over the past five decades and motivate future pilot- and training-centered research to address the high presence of stall-type accidents in aviation. Specifically, this article uses NTSB accident reports to answer the research question of whether there have been changes in the prevalence of spins among both fatal and non-datal fixed-wing accidents in the United States over the past sixty years. The methodology breaks down the accident analysis in three groups, based on the time period in which they occurred, due to differences in the reporting methods used. This paper finds that the prevalence and fatality ratio of stall-type accidents has remained high over the past six decades and that stall-type accidents are more than twice as fatal as an average accident. To remedy the high accident count, we recommend experimental ground and simulator-based training to improve pilot knowledge, skill, and performance.

The probability of an airplane deviation from pre-planned trajectory is a core of aviation safety analysis. We propose to use a mixture of three probability density distribution functions it the task of aviation risk assessment. Proposed model takes into account the effect of navigation system error, flight technical error, and occurrence of rare events. Univariate Generalized Error Distribution is used as a basic component of distribution functions, that configures the error distribution model from the normal error distribution to double exponential distribution function. Statistical fitting of training sample by proposed Triple Univariate Generalized Error Distribution (TUGED) is supported by Maximum Likelihood Method. Optimal set of parameters is estimated by sequential approximation method with defined level of accuracy. The developed density model has been used in risk assessment of airplane lateral deviation from runway centreline during take-off and landing phases of flight. The efficiency of the developed model is approved by Chi-square, Akaike’s, and Bayes information criteria. The results of TUGED fitting indicate better performance in comparison with double probability density distribution model. The risk of airplane veering off the runway is considered as the probability of a rare event occurrence and is estimated as an area under the TUGED.