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

Abstract

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.

Keywords

aviation advanced analytics and data science air traffic management airport operations capacity delay analysis

Get full access to this article

View all access options for this article.

References

General Aviation Manufacturers Association. 2018 Annual Report. GAMA, Washington, D.C., 2018.

Federal Aviation Administration. Advisory Circular AC: 150/5060-5 - Airport Capacity and Delay. U.S. Department of Transportation, Washington, D.C., 1983.

National Academies of Sciences, Engineering, and Medicine. Evaluating Airfield Capacity. ACRP Report 79. The National Academies Press, Washington, D.C., 2012.

Kuzminski

P. C.

An Improved RunwaySimulator — Simulation for Runway System Capacity Estimation. Proc., Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, 2013, pp. 1-11. https://doi.org/10.1109/ICNSurv.2013.6548575.

Sharma

D. P.

Samuel

Ramoutar

Lowe

David

Raspberry Pi Based Real Time Data Acquisition Node for Environmental Data Collection. Journal of Basic and Applied Engineering Research, Vol. 4, 2017, pp. 307-312.

Piracci

E. G.

Galati

Pagnini

ADS-B Signals Reception: A Software Defined Radio Approach. Proc., 2014 IEEE Metrology for Aerospace (MetroAeroSpace), Benevento, Italy, IEEE, New York, 2014.

Sun

The 1090 Megahertz Riddle: A Guide to Decoding Mode S and ADS-B Signals. TU Delft OPEN Publishing, Delft, 2021.

Mitkas

D. Z.

Lovell

D. J.

A Data Collection Scheme to Support Applications for Capacity Enhancements at Small Airports. Proc., International Conference on Research in Air Transportation (ICRAT2020), Tampa, FL, 2020.

Mitkas

D. Z.

Lovell

D. J.

Venkatesh

Young

Activity Identification Using ADS-B Data at General Aviation Airports. Proc., AIAA (American Institute of Aeronautics and Astronautics) Aviation Forum, Virtual Event, 2021.

10.

Young

S. B.

Classification of Runway Configurations for Capacity Analysis of Airports Serving Small Aircraft. Proc., International Conference on Research in Air Transportation (ICRAT2020), Tampa, FL, 2020.

11.

Sun

Ellerbroek

Hoekstra

Large-Scale Flight Phase Identification From ADS-B Data Using Machine Learning Methods. Proc., 7th International Conference on Research in Air Transportation (ICRAT2016), Philadelphia, 2016.

12.

Hochreiter

Schmidhuber

Long Short-Term Memory. Neural Computation, Vol. 9, No. 8, 1997, pp. 1735–1780.

13.

Sun

Ellerbroek

Hoekstra

Modeling Aircraft Performance Parameters With Open ADS-B Data. In 12th Air Traffic Management Research and Development Seminar (ATM2017), Delft University of Technology, 2017.

14.

Sun

Ellerbroek

Hoekstra

Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance-Broadcast Datasets. Journal of Aerospace Computing, Information and Communication, Vol. 14, No. 10, 2017, pp. 566-572. https://doi.org/10.2514/1.I010520.

15.

Mitkas

D. Z.

Lovell

D. J.

Venkatesh

Young

Leveraging Local ADS-B Transmissions to Assess the Performance of Air Traffic at General Aviation Airports. Presented at 14th USA/Europe Air Traffic Management Research and Development Seminar (ATM2021), New Orleans, 2021.

16.

Meijers

N. P.

Hansman

R. J.

A Data-Driven Approach to Runway Occupancy Time. Proc., 2019 AIAA (American Institute of Aeronautics and Astronautics) Aviation Forum, Dallas, TX, 2019.

17.

Kumar

Sherry

Kicinger

Runway Occupancy Time Extraction and Analysis Using Surface Track Data. George Mason University, Fairfax, VA, 2009.