Flight Safety Research Articles

The Geographic Factors-Based Optimization of National Flight Hub Airport Locations for Enhances Aviation Safety Standard

Indonesia's strategic geographical location and conditions are a result of its archipelagic status. Indeed, this significantly impacts the livelihoods of Indonesians, particularly in the area of transportation. In order to facilitate the mobility of individuals between islands, the government constructed transportation infrastructure and facilities. This will serve as a reference for government regulations that aim to reduce inequality, specifically in the development and distribution of products and services outside Java, particularly in Papua. The primary objective of this research is to conduct a connectivity analysis as an initial step towards optimizing the transportation network and determining the level of connectivity between Indonesian provinces through air transportation. Next, the researchers will study the existing air transportation network's structure to understand its characteristics clearly. It will be helpful material for formulating optimization and network management scenarios. The structuring pattern in question is the application of the hub-and-spoke network concept. The hub-and-spoke network pattern focuses on optimization problems from the perspective of economies of scale. The researchers optimized the provincial capital airport's location to establish a hub airport and subsequently refined the analysis results to enhance the efficiency of the new flight service network. The above steps will compare air travel performance before and after the network arrangement scenario. Indicators for the network connectivity level will also be present, indicating that the final product optimization is now ready for verification. The target of this research is to produce a concept or optimize the location of airport hubs through a comprehensive study taking into account geographic factors and conducting an analysis of the location of National Aviation Hub airports, which is the novelty of this research.

The Use of Eye Tracking in Aviation

Eye movement tracking technology, known as eye tracking, has been gaining increasing attention in recent years in the aviation industry. Its applications range from analysing pilots' attention and cognitive load to optimising cockpit design and improving in-flight safety. This paper explores the various uses of eye tracking in aviation, including its use in pilot training, cognitive load monitoring, cockpit design, and improving the interaction between automated systems and humans. The aim of this research is to provide a comprehensive view of the benefits and challenges associated with this technology in the aviation industry.

UAV detection in complex background with multi-scale feature fusion enhancement and channel-weight matching up-sampling

Abstract The reckless flight of unmanned aerial vehicle (UAV) seriously threatens the public and aviation safety. Due to their small size and unobvious features, it remains a great challenge for the current detection algorithms to detect UAV, especially in complex backgrounds with backlighting. To address these issues, the multiscale feature fusion enhancement strategy and channel-weight matching (CWM) rule are proposed in this paper. A multiscale feature fusion enhancement strategy is presented to capture the multi-scale contextual information, which not only suppresses information conflicts but also enhances feature extraction capabilities. Then, an up-sampling method based on CWM is designed to enhance the sensitivity of small object, which uses different up-sampling techniques based on the importance level of each feature channel. Finally, a feature refinement module for small object is designed to further enhance the characterization of their features. The ablation and comparative experiments are carried out on the self-made UAV dataset. Compared to the original YOLOv5 algorithm, the proposed method shows an increase of 3.6% in mAP0.5 and 2.8% in mAP0.5:0.95, respectively. Moreover, the comparative experiments are implemented on the VisDrone2019 dataset, and the results indicate that the mAP0.5 and mAP0.5:0.95 of the proposed method also increase by 4.2% and 1.6%, respectively.

Numerical Study on Fire Suppression by Water Mist in Aircraft Cargo Compartments: Effects of Spray Pattern, Droplet Size, and Nozzle Layout

Aircraft cargo compartment fires are one of the main threats to the safety of civil aircraft. In this study, a series of numerical simulations on the fire suppression performance of water mist in cargo compartments was carried out to examine the effects of the spray pattern, droplet size, and nozzle layout. The fire dynamics simulator (FDS) code was used to construct a fire suppression scenario in a full-scale aircraft cargo compartment. The results show that the extinguishment time of a corner fire was longer compared with center and sidewall fires due to the relatively larger distance from the nozzle and, therefore, a lower effective number of droplets reaching the flame area. Solid and hollow spray patterns showed significant differences in the spray coverage area. For a fixed flow rate, the hollow spray showed better fire suppression performance than solid spray. When the droplet size varied from 50 to 400 µm, the fire extinguishment time first increased and then decreased, corresponding to the dominant mechanism of the smothering effect of small droplets and the cooling effect of large droplets. In addition, the nozzle layout affected the water coverage on the ground of the cargo compartment. With an increase in nozzle number, the water mist flux was more evenly distributed and the fire extinguishment effect also increased.

Analisis Akibat Hukum Terhadap Kelalaian Pilot Dalam Menjaga Keselamatan Penerbangan (Kasus Pesawat Batik Air Tahun 2024)

Flight safety is a cornerstone of the aviation industry, heavily reliant on the professionalism and vigilance of pilots. This study examines the legal consequences of pilot negligence in maintaining flight safety, focusing on a case involving a fatigued Batik Air pilot. Employing a normative juridical approach, this research analyzes relevant legal frameworks, including Indonesia’s Law No. 1 of 2009 on Aviation, Civil Aviation Safety Regulations (CASR), International Civil Aviation Organization (ICAO) Annex 13, and related legal instruments. The findings reveal that negligence resulting from fatigue constitutes a severe violation of safety standards, breaching the duty of care expected in aviation operations. Such negligence may attract various legal repercussions, ranging from administrative sanctions, such as license revocation, to criminal charges as stipulated under Artt. 411 and 412 of Law No. 1 of 2009. Additionally, airlines may face civil liabilities for compensating passengers for damages incurred. The study also highlights the legal obligation of airlines to ensure the physical and mental fitness of pilots through stringent monitoring of work schedules and regular health evaluations. This paper concludes that strict law enforcement and the establishment of a robust safety culture within the aviation industry are essential to mitigating risks associated with human factors, particularly pilot negligence.

Optimal vibration control with considering non-probabilistic time-dependent reliability for hypersonic vehicles

Hypersonic vehicles have complex structures with multiple sources of uncertainties, and the presence of uncertainties may lead to the vehicles’ structural failure. Aiming to ensure the flight safety and structural reliability of a hypersonic vehicle, this study proposes a vibration optimization control method based on non-probabilistic time-dependent reliability. Due to the complex structure of the vehicle, its forebody and aftbody are modeled as two cantilever beams with the center of mass of the vehicle as the fixed fulcrum, respectively, and a vibration control model of cantilever is derived. Considering that the uncertainties in practical engineering are not easy to be counted, the uncertain parameters are quantified by using interval variables, and the dynamic response and time-dependent reliability of the uncertain system are analyzed based on the subinterval method and the non-probabilistic time-dependent reliability analysis method (NTRAM) designed in this paper. Further, to ensure the high reliability of the vehicle, an optimal robust linear quadratic regulator (ORLQR) method with the constraint of time-dependent reliability is proposed. The analysis of the numerical examples shows that the parameter uncertainty has a certain effect on the reliability of the vehicle, and verifies that the proposed ORLQR method can effectively control the vibration and slow down the reliability decline.

Design and analysis of thickened acoustic phase reversal Fresnel lens with high-focusing performance

ABSTRACT The accurate measurement of the contact stress of an aero-engine rotor mating surface is crucial for aviation safety. The focusing performance of ultrasonic lenses directly determines the accuracy of contact stress measurement. Phase reversal Fresnel zone plate (PR-FZP) is a new ultrasonic focusing lens suitable for contact stress measurement. A thickened phase reversal Fresnel zone lens (TPR-FZL) is proposed to improve the concentration ability. The lens replaces the path of sound waves travelling through the liquid with the Fresnel lens material, which reduces the energy loss of sound waves in the liquid and the interface of different materials, thus increasing the focus sound energy. The results showed that TPR-FZL, made of polymethyl pentene (PMP) and polyethene (PE), had relatively higher focal sound intensity. The larger the diameter of the piezoelectric plate, the stronger the focusing ability of TPR-FZL. When the material of TPR-FZL is PMP, the diameter of the piezoelectric plate of the transducer is 25 mm, TPR-FZL is the intermediate transmission type, and the thickness of the body is 35 mm, the focusing ability of TPR-FZL is the strongest. This study is of great significance for accurately measuring the contact stress on the mating surface of aero-engine rotors.

ОГЛЯД ДОСЛІДЖЕНЬ ТА ОБҐРУНТУВАННЯ НОВИХ МАТЕМАТИЧНИХ МЕТОДІВ МОДЕЛЮВАННЯ АЕРОПРУЖНОСТІ КРИЛА З ЛЮФТОМ КЕРУЮЧИХ ПОВЕРХОНЬ

The phenomenon of instability in flight due to the interaction of aerodynamic, elastic and inertial forces. could prove disastrous. Therefore, due importance is given to the aeroelastic analysis of the aircraft at the design stage. Free play, sometimes called play, occurs as a result of wear on parts such as lugs or moving mounts, often due to the aging of the aircraft. Excessive free play is detrimental to the flight safety of the aircraft. This can lead to catastrophic aeroelastic instabilities such as limit cycle oscillation (LCO), a phenomenon associated with sustained vibration of a fixed amplitude. LCO can accelerate the accumulation of structural fatigue damage and compromise controllability during flight. Aircraft control systems play a critical role in ensuring stability, maneuverability and flight safety. Over the past few decades, nonlinear aeroelasticity has received increasing attention. Advanced computational capabilities have stimulated renewed interest among researchers in re-examining the control surface free play problem with the goal of refining analytical models for greater accuracy. Both modern theoretical and experimental analyzes of free play have opened up new possibilities for the study of aeroelasticity. Despite the apparent understanding of the fundamental problem and the availability of numerous modeling approaches, freewheeling research continues to generate varied predictions regarding aeroelastic dynamic behavior and associated properties. Free play, defined as the sag or movement of mechanical connections between control inputs and aircraft control surfaces, is a ubiquitous aspect of control system design. While a little play may be tolerable, excessive play can lead to non-linearities and uncertainties, potentially compromising flight performance and safety. The aim of the article is to review existing methods of numerical research, analyze new mathematical methods for modeling aeroelastic vibrations and formulate the problem of developing safety criteria for transport category aircraft in terms of preventing elastic instability in the presence of free play in the control mechanism.

Daily Engine Performance Trending Using Common Flight Regime Identification

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and functional principal component analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm (GA)-assisted orthogonal matching pursuit (OMP) for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Optimizing Wind Speed Predictions in Bangladesh: Unveiling the Power of Machine Learning Approaches

Wind speed is a naturally occurring phenomenon that arises from the intricate interplay of various atmospheric processes. Wind speed prediction is pivotal in various sectors worldwide, and Bangladesh is no exception. Beyond its impact on agriculture, water management, and disaster preparedness, wind speed also plays a crucial role in urban planning and construction projects. Architects and engineers rely on accurate wind speed forecasts to design buildings and infrastructure that can withstand local wind conditions. Furthermore, the aviation and maritime industries heavily depend on wind speed predictions to ensure the safety of flights and shipping routes. Predicting wind speeds in Bangladesh poses a significant challenge due to the region's susceptibility to frequent seasonal changes influenced by its coastal location and complex, nonlinear climate patterns. To address this important aspect, we leverage Machine Learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Random Forest (RF), K Nearest Neighbors (K-NN), and Support Vector Machine (SVM) to forecast wind speeds at various weather stations in Bangladesh. We utilized various accuracy metrics, including precision, sensitivity, specificity, F-measure, and overall accuracy, to evaluate the performances of the algorithms. The RF model outperformed the other models with an overall accuracy of 94.73% for predicting wind speed conditions in Bangladesh. On the other hand, the LDA model exhibited the lowest performance, achieving an accuracy of 93.27% in comparison to the other models. It is noticeable that the aforementioned five models showed more than 90% accuracy for windspeed prediction. Additionally, we complement our analysis with visual representations such as box plots, density plots, dot plots, parallel plots, and scatterplot matrix plots. These empirical results also highlighted the RF as the most suitable method for predicting contemporary wind speed patterns in Bangladesh. International Journal of Statistical Sciences, Vol. 24(2), November, 2024, pp 137-154

Airborne soil-derived dust hazards in aviation

Abstract Airborne mineral dust poses a safety challenge for aviation. Several fatal accidents have happened in dust-laden air due to reduced visibility, strong gusty winds, and wind shear. Dust-induced icing also contributed at least to two fatal accidents. Furthermore, atmospheric dust has long- and short-term effects on aircraft operating condition due to corrosion and abrasion on the aircraft surfaces, and molten ingress deterioration of engine hot section components. The combined impact can increase operating and maintenance costs, and increase the overall cost of ownership. While the scientific community has started preparing and providing products based on atmospheric dust modeling and observation, there are still important data and information gaps in the fundamental science. These include (i) insufficient data which could be used to better understand the effects of dust on aircraft as well as on ground systems and operations (e.g., four-dimensional information of dust mineralogy, cost-benefit analysis of the impact of dust on aviation along flight routes), (ii) the identification of airborne dust monitoring and modeling products and services that could enable the flow of relevant information in commercial aviation and in decision-making workflows, and (iii) the underdeveloped, unclear, or absent role of dust hazards in regulations and operational procedures as well as in the training, skillset, and knowledge base of pilots. This review is aimed at both academic and aviation stakeholders, and presents the current state-of-the-art knowledge at the intersection of dust hazards, aviation safety, and impacts on flight operations and aircraft maintenance.

Multi‐target cognitive electronic reconnaissance for unmanned aerial vehicles based on scene reconstruction

AbstractModel‐free deep reinforcement learning (DRL) is regarded as an effective approach for multi‐target cognitive electronic reconnaissance (MCER) missions. However, DRL networks with poor generalisation can significantly reduce mission completion rates when parameters such as reconnaissance area size, target number, and platform speed vary slightly. To address this issue, this paper introduces a novel scene reconstruction method for MCER missions and a mission group adaptive transfer deep reinforcement learning (MTDRL) algorithm. The algorithm enables quick adaptation of reconnaissance strategies for varied mission scenes by transferring strategy templates and compressing multi‐target perception states. To validate the method, the authors developed a transfer learning model for unmanned aerial vehicle (UAV) MCER. Three sets of experiments are conducted by varying the reconnaissance area size, the target number, and the platform speed. The results show that the MTDRL algorithm outperforms two commonly used DRL algorithms, with an 18% increase in mission completion rate and a 5.49 h reduction in training time. Furthermore, the mission completion rate of the MTDRL algorithm is much higher than that of a typical non‐DRL algorithm. The UAV demonstrates stable hovering and repeat reconnaissance behaviours at the radar detection boundary, ensuring flight safety during missions.

Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios

Excessive mental workload can lead to less remaining resources for pilots to perform concurrent tasks during emergency flights, affecting aviation safety. Based on a flight simulator, this study investigated 25 cadet pilots using functional near-infrared spectroscopy (fNIRS) and subjective ratings to assess their mental workload under three subtasks with different equipment failures. fNIRS data included oxyhemoglobin, deoxyhemoglobin, and total hemoglobin signals, yielding 10545 functional connectivity (FC) features from four brain regions: prefrontal, right motor, left motor, and occipital cortexes. A deep residual shrinkage network classified mental workload levels, outperforming convolutional neural network and random forest models with 89.58% accuracy after feature selection employing an interpretable machine learning algorithm. The results suggest that brain FC from three hemoglobin signals could be used to differentiate the three different levels of pilot mental workload. This study could contribute to improving pilot training and supporting the development of pilots’ competencies during emergency scenarios.

Psychosocial risks and occupational health: Fatigue and sleep disturbances among aviation professionals

This study investigates the relationship between exposure to psychosocial risk factors at work, fatigue, and sleep quality among civil aviation professionals. Psychosocial risk factors such as excessive workload, lack of control over tasks, job insecurity, and inadequate social support are prevalent in high-demand environments like civil aviation, where constant vigilance and rapid decision-making are crucial. The research involved 200 participants, including pilots and flight attendants, who completed questionnaires measuring psychosocial stressors, sleepiness, and fatigue. Results indicated significant associations between role conflict and ambiguity, lack of social support, work/family conflict, and the pressure of responsibility with increased fatigue and sleep disturbances. Gender differences were noted, with women experiencing higher levels of work/family conflict. The study emphasizes the need for organizational strategies to mitigate these psychosocial stressors by enhancing role clarity, providing social support, fostering work-life balance, and managing job responsibilities to improve health outcomes and operational safety in aviation. Future research should consider longitudinal and diverse studies to explore these dynamics further and test intervention efficacy.

Effect of daytime and nighttime on helicopter pilot’s gaze behavior: a preliminary study in real flight conditions

Nighttime affects pilot visual scan patterns and increase the risks of helicopter operations, contributing to many helicopter accidents and incidents. Several past studies have attempted to examine the effect of nighttime on helicopter pilot gaze behavior, but researchers had limited success due to the difficulty of collecting representative data under real flight conditions. The present study attempted to address this challenge by conducting a real flight study involving daytime and nighttime traffic pattern tasks and using a Tobii Glasses 3 eye-tracking device to collect helicopter pilot eye-tracking data. This study preliminarily explored the feasibility of data collection in real flight conditions in the context of eye-tracking research on civil helicopter pilots in China. Due to safety considerations, only one pilot was recruited to collect data in multiple tasks. Differences and correlations were examined for all gaze behavior metrics. The results suggested that pilot gaze behavior metrics and their correlations differed between daytime and nighttime flights in aspects critical to aviation safety. Pilot gaze behavior also varied with the flight phase. The findings from this study serve as a reference for optimizing helicopter pilot training systems, improving pilot performance during nighttime flights, and ensuring flight safety on helicopters.

Investigation of the Crash Process of China Eastern Airlines Flight MU5735 Based on Seismic Data

Abstract Investigating aircraft accidents is a critical and challenging endeavor in the field of aviation safety. Current practices in seismology-based air accident investigations focus on locating crash sites to aid in the search and recovery of missing aircraft. In this study, we conducted an analysis of the China Eastern Airlines Boeing 737–800 crash on 21 March 2022, by leveraging three-component seismic records acquired from a location 21 km away from the crash site, in conjunction with publicly available radar data from Flightradar24 (an online service). Our approach helps to reconstruct the flight state of the aircraft at the time of the impact by modeling the dynamics of the impact with the Earth’s surface. Through an inversion process, we successfully obtained the force–time function and determined the direction of the impact force. In addition, we conducted inquiries into the weight of the 737–800 airliner, which enabled us to estimate the speed of the aircraft at the time of the accident to be about 200–264 m/s using the momentum theorem. These results are in agreement with time of the last radar signal and the measured dive angle of the airplane, providing valuable insights into the circumstances surrounding the crash.

Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism

The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention mechanism for the anomaly detection of aero-engine time-series data. The dataset utilized in this study was simulated from real data and injected with fault information. A fault detection model is developed utilizing normal data samples for training and faulty data samples for testing. The LSTM auto-encoder processes the time-series data through an encoder–decoder architecture, extracting latent representations and reconstructing the original inputs. Furthermore, the self-attention mechanism captures long-range dependencies and significant features within the sequences, thereby enhancing the detection accuracy of the model. Comparative analyses with the traditional LSTM auto-encoder, as well as one-class support vector machines (OC-SVM) and isolation forests (IF), reveal that the experimental results substantiate the feasibility and effectiveness of the proposed method, highlighting its potential value in engineering applications.

The Effectiveness of Operational Residual Risk Assessment: The Case of General Aviation Organizations in Enhancing Flight Safety in Alignment with Sustainability

Operational risk management (ORM) is crucial for every aviation organization. The assessment of operational risk (OR) is a critical area of study, as organizations must continuously evaluate and mitigate potential risks to maintain high levels of performance and safety. The motivation for writing this paper was to address the cognitive gap identified through literature analysis. The aim of this paper is to assess the effectiveness of operational residual risk assessment in general aviation (GA) organizations, with a specific focus on its role within the decision-making process to enhance flight safety in line with sustainability. By addressing a cognitive gap identified in the literature, this study seeks to determine whether current risk management practices adequately assess and mitigate residual risks, especially in organizations where operational risk is inherently high. Based on a literature review, the authors present adopted concepts of ORM and OR. The survey methodology involved a questionnaire on OR assessment, consisting of 32 questions completed by respondents twice—before and after 63 series of flights. The survey was carried out across two groups with significantly varying levels of flying experience (students and instructor pilots), with a particular focus on the influence of human factors. The conclusions are based on a comparative analysis of the difference in the results obtained after and before the series of flights in both surveyed groups. The survey was conducted in three selected general aviation organizations in Poland using nonprobability convenience sampling. The results demonstrate an overall significant underestimation of OR in both student and instructor groups. Therefore, it can be concluded that operational risk assessment in the selected organizations was not sufficiently effective. Additionally, it was shown that staff experience affected the effectiveness of OR assessment. Moreover, this study identified specific aspects of operational risk that were most underestimated. By addressing the cognitive gap, this study enhances both the theoretical and practical understanding of residual risk management, particularly in relation to safety and efficient resource use in aviation. It also offers the Operational Residual Risk Underestimation Index (ORRUI) as an actionable parameter developed to quantify and standardize the level of underestimation of operational residual risks in civil aviation.

Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Real-time measurement parameters are crucial for diagnosing faults in aero-engine gas path performance, ensuring engine reliability, and mitigating potential economic losses. Traditional aero-engines performance diagnosis was mainly based on the measurements of steady-state condition and lacked the utilization of data under transient conditions. Gas path diagnosis of aero-engines under transient conditions is crucial for early fault detection and safety of flight within the envelope. The challenge lies in the inconsistent distribution of performance deviations caused by variable operating conditions, especially with complex fault types, which can undermine diagnostic credibility. To improve reliability of gas path diagnosis under transient conditions, an offline reinforcement learning fault diagnosis framework based on a transient aero-engine performance model is proposed. To address the issue of variable operating conditions during transient states, a domain adaptive approach is utilized to reconstruct the measurement baseline and facilitate the transfer of different performance deviation distributions. Additionally, by adding spool acceleration as a measurement parameter, the multi-component fault coupling is solved. Finally, validation with actual operating data simulates fault cases, demonstrating the proposed method's efficacy in quantitatively detecting gradual, sudden, and multiple component faults under transient conditions with high accuracy and efficiency. The method proposed in this study achieves a computational speed improvement by 64% compared to the conventional method, achieving a time of 0.13 seconds, with an average error of less than 0.00389%. Additionally, it demonstrates strong robustness in the presence of noise, with an average error of less than 0.03125%. This proposed method improves real-time fault detection under transient conditions for its higher accuracy and efficiency, and therefore significantly enhance gas path health monitoring and diagnosis capability.

Evaluating near midair collision reporting systems using aircraft surveillance data: A case study at a university airport

Introduction: A near midair collision (NMAC) is defined by the Federal Aviation Administration as an event in which the crew of an aircraft perceives a situation that could lead to a midair collision or an event in which the separation between two aircraft is less than 500 feet (Federal Aviation Administration, 2018). NMAC reports collected by safety reporting systems have long been used to study and mitigate midair collision risk. However, reports submitted are subjective and require the reporter to voluntarily provide this information. With the implementation of Automatic Dependent Surveillance-Broadcast, aircraft temporal and positional data are easily collectible. Method: Using internal safety system, NASA Aviation Safety Reporting System, and FAA Near Midair Collision System reports together with ADS-B data, the effectiveness of different safety reporting systems to collect NMAC data around a towered university airport were compared over a six-month period. Unreported events were identified by utilizing ADS-B data to calculate aircraft separation events of less than 500 feet. Results: While 10 events were reported to the internal safety system, only one was reported to the NASA ASRS, and none to the FAA NMACS within the study’s scope. Sixteen events in which aircraft were within 500 feet of each other were found using ADS-B data, with none of these events having been reported to any safety reporting system. Conclusions: The findings of this study highlight the need for increased sharing in aviation safety data and show the potential of ADS-B data as a tool in studying near midair collisions and midair collision risk. Practical Applications: NMAC safety reporting systems may be less effective than expected. The addition of other sources of data such as ADS-B may be necessary to identify and investigate NMACs and other relevant near miss safety events.