A method for automatic airport operation counts using crowd-sourced ADS-B data
Abstract
Airports are tasked with counting and reporting their operations at least yearly. The counts are used at the local and national level to schedule maintenance, for research, and to receive funds, making their accuracy important. Historically, methods for counting operations at non-towered airports have relied on additional equipment at the airport or statistical estimates. In this work, we introduce a method to use crowd-sourced Automatic Dependent Surveillance – Broadcast (ADS-B) data from the OpenSky network to automatically count airport operations and report it separated by takeoffs and landings. We use two airports as case studies – Tulsa International Airport (TUL) and Purdue University Airport (LAF) – and compare the estimated operation counts from the ADS-B data algorithm to numbers reported through the Federal Aviation Administration’s (FAA) Air Traffic Activity Data System (ATADS).
Keyword : airport operation counts, ADS-B, OpenSky, non-towered airports, crowd-sourced data, airport count models
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Federal Aviation Administration. (2007). An overview of terminal facility traffic counting: Attachment 1 Comparison of APO and CC traffic count FY 2007. FAA.
Federal Aviation Administration. (2018). National Plan of Integrated Airport Systems (NPIAS) 2019-2023. U.S. Department of Transportation.
Ford, M., & Shirack, R. (1985). Statistical sampling of aircraft operations at non-towered airports. Federal Aviation Administration.
Johnson, M. E., & Gu, Y. (2017). Estimating airport operations at General Aviation airports using the FAA NPIAS airport categories. International Journal of Aviation, Aeronautics, and Aerospace, 4(1). https://doi.org/10.15394/ijaaa.2017.1151
Mott, J. (2018). Measurement of airport operations using a low-cost transponder data system. Journal of Air Transportation, 26(4), 147–156. https://doi.org/10.2514/1.D0117
Mott, J., & Bullock, D. M. (2018). Estimation of aircraft operations at airports using mode-C signal strength information. IEEE Transactions on Intelligent Transportation Systems, 19(3), 677–686. https://doi.org/10.1109/TITS.2017.2700764
Muia, M. J. (2007). Counting aircraft operations at non-towered airports. The National Academies Press.
Muia, M. J., & Johnson, M. E. (2015). Evaluating methods for counting aircraft operations at non-towered airports. The National Academies Press. https://doi.org/10.17226/22182
Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., & Wilhelm, M. (2014). Bringing up OpenSky: A large-scale ADS-B sensor network for research. In Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN) (pp. 83–94). IEEE. https://doi.org/10.1109/IPSN.2014.6846743
Sun, J., & Hoekstra, J. M. (2019). Integrating pyModeS and OpenSky historical database. In Proceedings of the 7th OpenSky Workshop, 67, 63–72. https://doi.org/10.29007/mmsb
Sun, J., Vû, H., Ellerbroek, J., & Hoekstra, J. M. (2019). pyModeS: Decoding Mode-S surveillance data for open air transportation research. IEEE Transactions on Intelligent Transportation Systems, 21(7), 2777–2786. https://doi.org/10.1109/TITS.2019.2914770
Tabassum, A., & Semke, W. (2018). UAT ADS-B data anomalies and the effect of flight parameters on dropout occurrences. Data, 3(2), 19. https://doi.org/10.3390/data3020019
United States Department of Transportation. (2020, August 10). Geospatial at Bureau of Transportation Statistics (BTS) Open Data Catalog: Runway Ends. https://data-usdot.opendata.arcgis.com/
Yang, C., & Mott, J. H. (2021). Using transponder signals to model aircraft performance at non-towered airports. In 2021 IEEE Aerospace Conference (50100). Big Sky, MT. https://doi.org/10.1109/AERO50100.2021.9438390
Yang, C., Mott, J., & Bullock, D. M. (2021). Leveraging aircraft transponder signals for measuring aircraft fleet mix at non-towered airports. International Journal of Aviation, Aeronautics, and Aerospace, 8(2), 1–18. https://doi.org/10.15394/ijaaa.2021.1563