Developing Capacity Estimation Metrics for Airports Accommodating Smaller Aircraft Using Locally Collected Automated Dependent Surveillance-Broadcast Data

Airport operators need good capacity estimates for many purposes, including to bolster applications for funding support for capacity improvements. Based on a recognized need to enhance models for estimating the operating capacity of airports serving a user base of smaller aircraft, such as small general aviation airports, this paper describes research conducted to leverage automated dependent surveillance-broadcast (ADS-B) data to develop aircraft performance characteristics for use as inputs to future small airport capacity models. The research addresses this challenge by constructing home-built ADS-B data collection units using a Raspberry Pi-based hardware platform and an Amazon Web Services-based cloud architecture housing the study’s PostGreSQL database, and python-based codebase. The work was focused at three participating airports: The Ohio State University Airport in Columbus, OH (KOSU); College Park, MD (KCGS); and Republic Airport in Farmingdale, NY (KFRG). The hardware units were deployed at three study airports, collecting more than 90?million individual ADS-B messages transmitted by aircraft operating within the vicinity of the study airport. Models were developed to clean, process, and assess the data, and leveraged to determine aircraft performance characteristics within these environments. Initial work focusing on assessing the fleet mix, approach speeds, aircraft separation in arrival streams, and runway occupancy times was performed. Findings from this research revealed a very high accuracy and richness of collected and processed data, and performance metrics that were reasonable, valid, and applicable for use as inputs to future airport capacity models.


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  • Accession Number: 01847944
  • Record Type: Publication
  • Files: TRIS, TRB, ATRI
  • Created Date: Jun 3 2022 9:00AM