Pilot Training Metrics at a Part 141 University Training Program

The study evaluates training at a collegiate flight training program providing metrics for time and costs from zero time to a Private Pilot. Training times for flights and activities are pulled from a sophisticated database used at Embry-Riddle Aeronautical University (ERAU) and matched with flight and ground school lessons and then further subdivided to determine the amount of time spent training in areas of operation that are prescribed by the Federal Aviation Administration in the published Practical Test Standards and Airman Certification Standards for those seeking pilot licenses and ratings. Provided are mean times and costs for a prospective pilot to attain Private licenses at Embry-Riddle. The records of 286 students in the FAA approved Private pilot course were pulled, de-identified, and analyzed. ANOVA was used to compare the training times across areas of operation. The results provide insight into those areas requiring the most training and would perhaps benefit the Simplified Vehicle Operation program at NASA by helping to identify candidate technologies proposed to be developed by the program office

A Machine Learning Approach Towards Analyzing Impact of Surface Weather on Expect Departure Clearance Times in Aviation

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, the weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stops and Expect Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline’s operating network. The use of time-series data mining can be used to assess and quantify the impact of surface weather variables on EDCTs. A major hub airport in the United States, Charlotte Douglas International Airport, was chosen for the model development and assessment, and Vector Autoregression and Recurrent Neural Network models were developed. While both models were assessed to have demonstrated acceptable performance for the assessment, the Vector Autoregression outperformed the Recurrent Neural Network model. Weather variables up to six hours before the prediction time period were used to develop the proposed lasso regularized Vector Autoregression equation. Precipitation values were assessed to be the most significant predictors for EDCT values by the Vector Autoregression and Recurrent Neural Network models

Spring 2023 School of Graduate Studies Newsletter

Table of Contents Message from the Associate Dean Ph.D. in Aviation News MS in Aviation News MSOSM News MSUS News Faculty Focus Student Spotlight ChatGTP: Artificial intelligence (AI) Predictive Analytics Research in Aviation Safety Dissertation Defenses College of Aviation Academic Awards Doctoral Medallion Ceremony 13 Scholarly Activityhttps://commons.erau.edu/db-sgs-newletter/1022/thumbnail.jp

Predicting Expect Departure Clearance Times Based on Surface Weather Observations for a Major Hub Airport: A Machine Learning Approach

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stop and Estimate Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline’s operating network. The use of time-series machine learning models has demonstrated effectiveness in predicting different types of flight delays. For the purpose of predicting EDCTs based on surface weather observations at Charlotte Douglas International Airport, Vector Autoregression and Recurrent Neural Network, specifically Long Short Term Memory, models were developed. The two models were evaluated on Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error. While both models were assessed to have demonstrated acceptable performance, the Vector Autoregression outperformed the Recurrent Neural Network model. Weather-related variables up to six hours before the prediction time period were used to develop the lasso regularized Vector Autoregression equation. Precipitation values were assessed to be the most significant for EDCT prediction by the Vector Autoregression and Recurrent Neural Network models

Utilizing Deep Learning to Predict Unstabilized Approaches for General Aviation Aircraft

Unstabilized approaches pose a major hazard for general aviation aircraft. In the period from 2009 to 2019, 3,257 general aviation accidents occurred during the landing phase of flight in which loss of control was analyzed to be the leading cause of accidents [1]. Previous studies have explored the use of machine learning to develop low-cost and easily adaptable predictive tools as possible mitigation tools for unstabilized approaches. This study was aimed at developing a machine learning-based predictive warning system for pilots to abort an unstabilized approach and execute a go-around maneuver. Deep neural networks were trained predict unstabilized approaches for a light multi-engine general aviation aircraft. Since the data was structured with data points corresponding to every second of the flight and exhibited qualities of a time-series dataset, a Recurrent Neural Network architecture was used to model the timeseries relationships. To develop and validate the model, a dataset comprising of approximately 42,000 landings was used. The model developed in this study was able to predict an unstabilized approach with an accuracy of 84%, and the vertical speed of an aircraft was determined to be the most significant predictor of an unstabilized approach

The Flight Risk Perception Scale (FRPS): A Modified Risk Perception Scale for Measuring Risk of Pilots in Aviation

Risk and risk perception remain focal areas of research within the aviation domain. The purpose of the current study was to assess an existing measure of a 26-item self-risk perception scale for pilots. A sample of 490 participants was used in the present study, and a confirmatory factor analysis was conducted on the original 26-item instrument. The findings indicated that there was a poor model fit of the original instrument. Through the use of modification indices, a new 13-item scale was produced, which resulted in a second-order CFA model. Flight risk was shown to be the second-order construct with general flight risk, high risk, and altitude risk as the first-order constructs. The new model reported good psychometric values of GFI of 0.933, AGFI of 0.893, CFI of 0.947, NFI of 0.923, normed chi-squared of 3, and RMSEA of 0.071. The findings produce a new 13-item scale that can be used by aviation researchers who wish to conduct studies related to the pilot\u27s self-assessment of risk perception