Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Jadia Ewing, Graduate student Alexander Van Baelan, Graduate student Conrad Prisby, Graduate student Rafal Smietana , Graduate student
Lead Presenter's Name
Jadia Ewing
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Dr. Gamage Dumindu Samaraweera
Abstract
Air traffic congestion is an increasingly important factor in aviation safety as global flight activity continues to grow. This project investigates whether higher traffic density is associated with an increased risk of aviation incidents and identifies key contributing factors. Using publicly available flight (ADS-B) and incident (NTSB) data, we apply several machine learning models to analyze traffic patterns and predict risk. We begin with logistic regression to evaluate the relationship between density and incident probability, followed by decision trees to extract interpretable rules describing high-risk conditions. K-nearest neighbors (KNN) is used to examine similarity in traffic patterns among incident flights, while support vector machines (SVM) capture more complex relationships. Ensemble methods, including random forests and boosting, are applied to improve performance, with SHAP analysis used to interpret feature importance. Since incidents are rare, we prioritize recall and F1-score to better detect high-risk scenarios.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Modeling Aircraft Collision Risk Using Machine Learning and Traffic Density Data AC
Air traffic congestion is an increasingly important factor in aviation safety as global flight activity continues to grow. This project investigates whether higher traffic density is associated with an increased risk of aviation incidents and identifies key contributing factors. Using publicly available flight (ADS-B) and incident (NTSB) data, we apply several machine learning models to analyze traffic patterns and predict risk. We begin with logistic regression to evaluate the relationship between density and incident probability, followed by decision trees to extract interpretable rules describing high-risk conditions. K-nearest neighbors (KNN) is used to examine similarity in traffic patterns among incident flights, while support vector machines (SVM) capture more complex relationships. Ensemble methods, including random forests and boosting, are applied to improve performance, with SHAP analysis used to interpret feature importance. Since incidents are rare, we prioritize recall and F1-score to better detect high-risk scenarios.