Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Taeyun Yoo, Graduate Student
Lead Presenter's Name
Taeyun Yoo
Lead Presenter's College
DB College of Aviation
Faculty Mentor Name
Ryan Wallace
Abstract
This study addresses the increasing need to understand aircraft activity in low-altitude airspace, where emerging operations such as Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) are expected to heighten traffic complexity and safety risks. This research develops a scalable framework for constructing a nationwide, high-resolution, altitude-stratified airspace density atlas using Automatic Dependent Surveillance–Broadcast (ADS-B) data. The methodology incorporates large-scale data acquisition, terrain-referenced altitude normalization, and spatial aggregation using a hexagonal grid system, followed by statistical modeling to estimate traffic density across spatial and temporal dimensions. Preliminary results indicate the capability to generate detailed geospatial representations of aircraft activity, revealing spatial clustering, temporal trends, and variations across altitude bands. Anticipated outcomes include the creation of georeferenced traffic density datasets, advanced visualization tools, and improved estimates of airspace utilization. The significance of this work lies in its potential to support data-driven safety assessments, inform Federal Aviation Administration (FAA) policy and planning, and facilitate the risk-based integration of emerging aviation technologies. By converting large-scale surveillance data into actionable insights, this research advances the modernization and safe management of increasingly complex low-altitude airspace systems.
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
Characterizing Air Traffic Density Using Nationwide ADS-B Data
This study addresses the increasing need to understand aircraft activity in low-altitude airspace, where emerging operations such as Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) are expected to heighten traffic complexity and safety risks. This research develops a scalable framework for constructing a nationwide, high-resolution, altitude-stratified airspace density atlas using Automatic Dependent Surveillance–Broadcast (ADS-B) data. The methodology incorporates large-scale data acquisition, terrain-referenced altitude normalization, and spatial aggregation using a hexagonal grid system, followed by statistical modeling to estimate traffic density across spatial and temporal dimensions. Preliminary results indicate the capability to generate detailed geospatial representations of aircraft activity, revealing spatial clustering, temporal trends, and variations across altitude bands. Anticipated outcomes include the creation of georeferenced traffic density datasets, advanced visualization tools, and improved estimates of airspace utilization. The significance of this work lies in its potential to support data-driven safety assessments, inform Federal Aviation Administration (FAA) policy and planning, and facilitate the risk-based integration of emerging aviation technologies. By converting large-scale surveillance data into actionable insights, this research advances the modernization and safe management of increasingly complex low-altitude airspace systems.