Situational Awareness and General Aviation Accidents

Kalagher, Hilary; Voogt, Alex de; Boulter, Colin

doi:10.1027/2192-0923/a000207

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Pilots' mismanagement of marginal visibility procedures in VFR flight was related to a significant rate of weather-related general aviation accidents (Aircraft Owners and Pilots Association; Kalagher et al, 2021). This study addressed simulator scenarios that enable student pilots to experience the challenges related to VFR flight into marginal visibility conditions such as disorientation, tendency to fly at lower altitudes and collision risk (Johnson & Wiegmann, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…A significant rate of fatal weather-related general aviation accidents occurred when pilots continued a visual flight rules (VFR) flight in instrument meteorological conditions (IMC) with inadequate visibility (Aircraft Owners and Pilots Association; Kalagher et al, 2021). Whereas flying in IMC can be safely and legally done with the appropriate license and aircraft instruments, this study focuses on pilots that are only VFR-rated.…”

Section: Introductionmentioning

confidence: 99%

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The Role Of Simulator Scenarios In Learning Low Visibility Procedures In Flight

Koglbauer¹

2023

The European Proceedings of Educational Sciences

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The continuation of visual flight in meteorological conditions with inadequate visibility accounted for the majority of fatal weather-related general aviation accidents. During practical flight training in the aircraft, the standardization of low visibility procedures training is very limited due to weather variability and regulations restraining from flying below visibility minima. This study describes and evaluates a theoretical briefing and simulator scenarios for low visibility procedures that are practiced by ab initio student pilots and can be used to extend the current syllabus. The simulator scenarios included flying traffic patterns in conditions of degrading visibility (80 km, 8 km, 5 km and 1.5 km), returning or flying through a mountain pass in marginal conditions, and 180° turns out of clouds. The simulator training program was evaluated in a pretest-training-posttest design with ab initio student pilots assigned to a training and a control group. The results show a significant effect of simulator training to improve the performance of student pilots. The research presented in this study addresses improvements of the training methods for low visibility procedures, supporting pilots' management of weather-related threats and errors and the standardization of the syllabus.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role Of Simulator Scenarios In Learning Low Visibility Procedures In Flight

Koglbauer¹

2023

The European Proceedings of Educational Sciences

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“…Recent data on civil aviation accidents highlight that the approach and landing stages, characterized by a gradual decrease in energy, are the most accident-prone stages, as shown in Figure 1 [1,2]. One of the key causes of an approach-landing accident is that of the improper management of the aircraft's energy; an abnormal energy approach and landing are the main causes of further accidents such as stalling, heavy landing, off-runway accidents, and so on [3,4].…”

Section: Introductionmentioning

confidence: 99%

Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model

Hu,

Yan,

Cao

et al. 2024

Aerospace

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The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.

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Predicting the Severity of Runway Excursions from Aviation Safety Reports

Garcia

Jaedicke

Lim

et al. 2023

Journal of Aerospace Information Systems

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Despite all technological advances, runway excursions (REs) remain among the most frequent aviation accidents. Several factors have been shown to contribute to RE incidents. Although different studies have addressed the prediction of REs using quantitative data, the present study contributes to RE research by applying advanced quantitative analysis methods using text mining of voluntarily submitted reports to predict aircraft damage as the outcome of a runway excursion event. The study also intended to unveil relevant predictors from the voluntary safety reports data. The random forest model presented better results than the naïve Bayes and the gradient boosting approaches. The positive predictive value of the random forest method was measured at 74.90%, which is in contrast to 63.58% of the naïve Bayes method and 72.56% of the gradient boosting method. Although several generalizable topics were identified in the reports (such as runway and touchdown zone characteristics, flight control during approach, aircraft malfunction, weather factors, and runway surface and braking action conditions), no defined predictors for runway excursion events resulting in aircraft damage could be established.

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Situational Awareness and General Aviation Accidents

Cited by 3 publications

References 19 publications

The Role Of Simulator Scenarios In Learning Low Visibility Procedures In Flight

The Role Of Simulator Scenarios In Learning Low Visibility Procedures In Flight

Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model

Predicting the Severity of Runway Excursions from Aviation Safety Reports

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