Machine Learning-based Live Predictive Warnings for Unstabilized Approaches in Aircraft

Unstabilized approaches are a major hazard for general aviation aircraft. An unstabilized approach can lead to runway excursions, structural damage on touchdown, or even Controlled Flight into Terrain (CFIT). The Aircraft Owners and Pilots Association reported that 3,257 general aviation accidents from 2009-2019 occurred during the landing phase of a flight. The advancement of machine learning technology offers the opportunity to develop low-cost and easily adaptable technology. This research is aimed at developing machine learning-based predictive warnings for pilots to abort an unstabilized approach and execute a go-around maneuver. As the first step, we collected feature-rich flight data which could be useful for making predictions of unstabilized approaches. The data utilized for the model preparation was derived from the Flight Data Monitoring (FDM) program of a Part-141 Flight Training Organization. As a first step of preprocessing, we decided to extract only the variables that would be determining factors when predicting approach stability, based on the developed criteria. Additionally, we structured the data by separating it into matrices corresponding to exactly one flight - defined as the period from one take-off through the subsequent landing - determined by the change of altitude, airspeed, and engine power variables. We will use deep neural networks to train our machine learning model to predict unstabilized approaches. Since the data is structured with data points corresponding to every second of the flight, i.e., it is time-series, we will use a Recurrent Neural Network which is specifically adept at modeling time-series data. To develop our model, we will use an 85% training set, a 5% development set, and a 10% testing set split for our complete dataset comprising approximately 42,000 flights. The deep neural network architecture will be designed using the Tensorflow 2 framework. The model developed in this project will be a low-cost, objective decision-making aid for pilots that will improve general aviation safety. The model will be integrable into avionics systems that are used by general aviation pilots, such as the Garmin G1000®, for their aircraft

Deep Learning Prediction Models for Runway Configuration Selection and Taxi Times Based on Surface Weather

Growth in air traffic demand in the United States has led to an increase in ground delays at major airports in the nation. Ground delays, including taxi time delays, directly impacts the block time and block fuel for flights which affects the airlines operationally and financially. Additionally, runway configuration selection at an airport significantly impacts the airport capacity, throughput, and delays as it is vital in directing the flow of air traffic in and out of an airport. Runway configuration selection is based on interrelated factors, including weather variables such as wind and visibility, airport facilities such as instrument approach procedures for runways, noise abatement procedures, arrival and departure demand, and coordination of ATC with neighboring airport facilities. The research problem of this study investigated whether runway configuration selection and taxi out times at airports can be predicted with hourly surface weather observations. This study utilized two sequence-to-sequence Deep Learning architectures, LSTM encoderdecoder and Transformer, to predict taxi out times and runway configuration selection for airports in MCO and JFK. An input sequence of 12 hours was used, which included surface weather data and hourly departures and arrivals. The output sequence was set to 6 hours, consisting of taxi out times for the regression models and runway configuration selection for the classification models. For the taxi out times models, the LSTM encoder-decoder model performed better than the Transformer model with the best MSE for output Sequence 2 of 41.26 for MCO and 45.82 for JFK. The SHAP analysis demonstrated that the Departure and Arrival variables had the most significant contribution to the predictions of the model. For the runway configuration prediction tasks, the LSTM encoder-decoder model performed better than the Transformer model for the binary classification task at MCO. The LSTM encoder-decoder and Transformer models demonstrated comparable performance for the multiclass classification task at JFK. Out of the six output sequences, Sequence 3 demonstrated the best performance with an accuracy of 80.24 and precision of 0.70 for MCO and an accuracy of 77.26 and precision of 0.76 for JFK. The SHAP analysis demonstrated that the Departure, Dew Point, and Wind Direction variables had the most significant contribution to the predictions of the model

The Effect of Electronic Flight Bags in Flight Training on Preflight Skill Development and Aeronautical Decision Making

This study was designed to evaluate the effects of utilizing Electronic Flight Bags (EFBs) in flight training with emphasis on preflight skill development and Aeronautical Decision Making. The study participants were student pilots or private pilots who used EFBs in flight training and had not logged more than 100 total flight hours. The study utilized a simulation of the preflight process of a Visual Flight Rules cross country flight in which the participants answered questions related to the flight preparation. Fifty percent of the study’s population completed this survey with the information provided through an EFB and the other 50% population had to answer the questions without an EFB using traditional unabridged raw data. A comparative analysis of the data collected from both groups was performed. The largest degradation of performance was noted in Notices to Airmen (NOTAM) interpretation and the least degradation in performance was noted in weather-related decision-making

Simulation Analysis of the Effects of Performance-Based Navigation on Fuel and Block Time Efficiency

The commercial aviation industry has been grown steadily in the past decade in terms of the number of flights operated and passengers traveled. The Federal Aviation Administration has introduced several technologies and initiatives under the NextGen Program. Performance-Based Navigation (PBN) is one such technology that is aimed at improving the efficiency of the National Airspace System which will improve system capacity and reduce delays. The airlines operate with restrained resources and investments into reducing fuel costs and flight times will directly or indirectly impact profit margins. PBN procedures and routes can help airlines reduce block times as flights will be able to fly more direct routes and utilize more efficient procedures in the terminal airspace. The study is aimed at analyzing the effect of PBN on fuel and block time efficiency for a flight. The study utilized a quantitative research method and utilized simulations of 20 flights on 10 different city-pairs. The 10 city-pairs were selected to sample routes of different distances. 10 flights were simulated utilizing the lowest form of PBN capability and without Global Positioning System (GPS) procedures and the other 10 flights utilized the highest PBN capability and GPS procedures. A comparative analysis was carried out with the data retrieved from the simulations for the 10 city-pairs. The results indicated that flight time could be reduced by an average of 7.3% by utilizing enhanced PBN procedures and fuel usage could be reduced by an average of 2.3%. The benefits offered by PBN improved with an increase in route distances due to larger fuel and time savings

Analysing the Threats of the Failure of Visual Awareness and Cognitive Bias During a Visual Approach for Commercial Operations

The purpose of this study is to conduct a detailed analysis of the limitation of visual awareness that flight crew experience while conducting visual approaches to an airport. Visual awareness is critical while conducting visual approaches and it is important to study the factors that can limit the capabilities of human beings to maintain visual awareness. This research will explore the limitations of visual awareness which special emphasis on change blindness, inattentional blindness, and visual masking. This study will also focus on forms of cognitive bias such as expectation and confirmation bias in the flight deck. Visual approaches expose pilots to multiple and critical visual stimuli that require strong visual awareness for safe operations. This research will explore visual approaches in air carrier operations around the world and conduct a detailed analysis of the Flight Safety Foundation accident database to study the reported incidents during visual approaches in air carriers from 2008-2018. The effect of human factors will be studied in those incidents with special emphasis on the role of visual awareness and cognitive bias. The results from the Flight Safety Foundation data is quantified and a trend analysis is carried out. Fatigue and distractions inside the cockpit such as annunciation and alerts during high task saturation periods are analyzed to be major factors for incidents during visual approaches. Enhanced Crew Resource Management (CRM) procedures and varying Standard Operating Procedures(SOPs) for different Flight Duty Periods(FDPs) are some of the recommended practices that were analyzed in the study

Analyzing the Effect of Delta Airlines\u27 Fleet Upgrade on Operations at Atlanta-Hartsfield Jackson International Airport

Commercial aviation has seen a growth in terms of passengers traveled and flights operated in the past decade (BTS, 2020). To cope up with this growth, airlines adopt various strategies such as network expansion and fleet upgrades. Airlines adopt fleet upgrade strategies to modernize their fleet, reduce costs, expand network coverage, and improve customer loyalty and experience. Research suggests that change in wake turbulence categorization and aircraft performance can directly impact the movements and delays at an airport. For this study, Delta Airline\u27s fleet upgrade program and its effect on operating parameters at Atlanta-Hartsfield Jackson International Airport was analyzed. The operating parameters analyzed for this study included runway delays, take-off delays, and runway movements. The traffic schedule of 11th November 2019 was simulated for the analysis with a total of 2,538 flights. A change in the fleet configuration would lead to a change in the wake turbulence categories of the aircraft that would directly impact operating parameters such as runway movement and delays. The results of the simulation indicate that operating Delta\u27s upgraded fleet would lead to an increase in runway delays and take-off delays and a decrease in runway movement for all five runways at Atlanta. The consistent results across all parameters indicate that Delta\u27s upgraded fleet and the corresponding change in aircraft performance and wake turbulence categorization would lead to a decrease in airport efficiency at Atlanta Airport

Analysing the Threats of the Failure of Visual Awareness and Cognitive Bias During a Visual Approach for Air Carrier Operations

The purpose of this study is to conduct a detailed analysis of the limitation of visual awareness that flight crew experience while conducting visual approaches to an airport. Visual awareness is critical while conducting visual approaches and it is important to study the factors that can limit the capabilities of human beings to maintain visual awareness. This research will explore the limitations of visual awareness which special emphasis on change blindness, inattentional blindness, and visual masking. This study will also focus on forms of cognitive bias such as expectation and confirmation bias in the flight deck. Visual approaches expose pilots to multiple and critical visual stimuli that require strong visual awareness for safe operations. This research will explore visual approaches in air carrier operations around the world and conduct a detailed analysis of the Flight Safety Foundation accident database to study the reported incidents during visual approaches in air carriers from 2008-2018. The effect of human factors will be studied in those incidents with special emphasis on the role of visual awareness and cognitive bias. The results from the Flight Safety Foundation data is quantified and a trend analysis is carried out. Fatigue and distractions inside the cockpit such as annunciation and alerts during high task saturation periods are analyzed to be major factors for incidents during visual approaches. Enhanced Crew Resource Management (CRM) procedures and varying Standard Operating Procedures(SOPs) for different Flight Duty Periods(FDPs) are some of the recommended practices that were analyzed in the study

Analyzing the Threats of the Failure of Visual Awareness during a Visual Approach for Transport Category Aircraft

The purpose of this study was to evaluate the risk posed by the failure of visual awareness during visual approaches for transport category aircraft. Visual awareness is critical while conducting visual approaches and it is important to study the factors that can limit the capabilities of human beings to maintain visual awareness. Visual approaches expose pilots to multiple and critical visual stimuli that require strong visual awareness for safe operations. The study conducted a detailed analysis of the Flight Safety Foundation accident database to study the reported incidents during visual approaches from 1998-2018. The effect of human factors was studied in those accidents with special emphasis on the role of visual awareness. Lack of Crew Resource Management, Fatigue, and loss of situational awareness in the cockpit during high task saturation periods were analyzed to be major factors for accidents during visual approaches. Enhanced Crew Resource Management procedures and risk management procedures to identify ‘high-risk airports’ and routes that consider flight duty periods, physiological factors such as ‘Low Circadian Levels’ during operations, and geographical features near the airport that could induce visual illusions were some of the recommended practices that were analyzed in the study

Creating a Competitive Environment for Defense Aerospace in a Protectionist Multipolar World: A Study of India and Israel

The paper studies protectionism in defense aerospace in a multipolar world and analyzes the strategies of two emerging powers: India and Israel. The emergence of protectionism in a multipolar world has left a visible and influential impact in the globally integrated defense industry. As the world has become increasingly multipolar, new military powers have emerged around the world. India and Israel are disparate in terms of their size, wealth, and international relations. There are interesting similarities between them when it comes to their defense strategies. As a result, they also present compelling case studies for understanding protectionism in a multipolar world, specifically in the defense aerospace sector. This paper studies the current strategies adopted by the two nations in their defense aerospace manufacturing sectors. The paper evaluates differences and similarities between the two nations in terms of the issues faced by the defense aerospace sectors of the two nations and the potential that lies ahead for them. In the recommendations made, it was discussed how Israel needs new defense partners to reduce its over dependence on the United States, while India needs to boost manufacturing in its defense aerospace industry through specific tax reforms and bureaucratic reforms. While India and Israel need to regulate the defense aerospace industry to some extent for national security reasons, they should open their industries to other countries and find favorable partners to do so

Data Mining Techniques to Predict Aircraft Damage Levels for Wildstrikes in United States

Wildlife strikes pose a major economic and threat to aviation safety all around the world. From 1990 through 2018 there were 209,950 wildlife strikes to aviation in the U.S. Approximately eight percent of those strikes caused damage to aircraft. A primary method to understand the magnitude of this economic and safety hazard is through data collection and analyses. Data mining methods can be used to predict the likelihood of events and significant factors of contribution based on historical datasets. Researchers will collect and analyze data (XXXX-2020) from the National Wildlife Strike Database. The purpose of this study is twofold: 1. To identify the potential predictors of damaging wildlife strikes to aviation in the U.S.; 2. To identify the potential predictors of substantial and minor damaging wildlife strikes to aviation in the U.S. Findings of the current study can help determine the nature and magnitude of this problem as well as provide the ground work for the development and implementation of integrated safety management and research efforts to improve aviation safety