A FRAMEWORK FOR DATA COLLECTION AND AIRSIDE OPERATION METRICS ANALYSIS AT SMALL AIRPORTS

Abstract

With the growing global demand for air travel, General Aviation (GA) airports are facing significant challenges. Unlike larger airports, many GA airports operate with limited infrastructure, leading to issues such as delays and congestion. Managing a mix of flight types, including training and regular flights, within tight budget constraints and limited runway capacity further complicates operations. Effective management and reliable capacity estimation are crucial, especially as these airports often depend on federal funding for future expansions. However, the lack of effective data collection mechanisms and equipment makes it difficult to implement data-driven management strategies or accurately estimate capacity, particularly given the complexities of handling diverse flight operations. Tasked by the Federal Aviation Administration (FAA), this project addresses the capacity estimation challenges at GA airports using Automatic Dependent Surveillance–Broadcast (ADS-B) technology. It proposes a comprehensive data pipeline and analysis system hosted on Amazon Web Services (AWS) to collect, decode, filter, analyze, and archive flight data. This system facilitates the extraction of key operational metrics for advanced capacity modeling. To ensure precise parameter extraction, the framework incorporates a rule-based model for accurate operation type classification. Additionally, a novel signal enhancement method is introduced to improve ADS-B data quality, ensuring more reliable and consistent flight trajectory timestamps. To support the development of the second generation of the Airport Capacity Model (ACM2) and define the required operational metrics, this work provides specifications for bounding boxes at target airports and establishes key operational benchmarks. The methodologies for calculating departure and arrival operational metrics based on the benchmarks are also detailed. Leveraging the advantages of the proposed data analysis system, this study demonstrates various applications of ADS-B data analysis. These include performance comparisons between flights with different operational purposes, correlations between squared flight speeds at various phases and density altitude, and time series predictions of air traffic flow at specific airports. By addressing these challenges, this project has the potential to significantly enhance the accuracy of capacity estimation across thousands of GA airports while delivering reliable aviation data and actionable insights to both the aviation research community and GA airport stakeholders.