Land Use Guidelines for Drone Hubs in the U.S.: Optimal Site Selection and Policy Frameworks
Main Article Content
Abstract
This study investigated public preferences regarding drone hub siting through a quantitative survey of 1,023 U.S. respondents. The findings reveal an overwhelming consensus (97.9%) in support of banning drone hubs near sensitive areas, such as residential neighborhoods and schools. Residential settings significantly influenced distance preferences, with urban residents showing greater acceptance of closer proximities (52.0% preferred 1/2 mile to 1 mile) compared to rural residents who favored greater distances (51.7% preferred 1 mile to 2 miles). Chi-square tests confirmed statistically significant relationships (p < .001) between residential settings, distance preferences, and noise tolerance. Gender and age also significantly influenced drone acceptance levels, with females and younger respondents showing more positive attitudes. The most effective mitigation measures included noise reduction technology (63.0%), limited operating hours (61.2%), and community involvement (56.0%). Based on these findings, the study proposes evidence-based guidelines for drone hub siting that include tiered setback requirements, context-sensitive noise standards, operational restrictions, and comprehensive stakeholder engagement strategies. These quantitative parameters provide a foundation for sustainable drone infrastructure development that balances technological advancement with community acceptance.
Article Details
References
Bacchini, A., & Cestino, E. (2019). Electric VTOL configurations comparison. Aerospace, 6(3), 26. https://doi.org/10.3390/aerospace6030026
Bauranov, A., Parks, J., Jiang, X., Rakas, J., & González, M. C. (2021). Optimizing urban drone delivery networks via distributed computing. Transportation Research Part C: Emerging Technologies, 125, 103046. https://doi.org/10.1016/j.trc.2021.103046
Christian, A. W., & Cabell, R. (2017). Initial investigation into the psychoacoustic properties of small unmanned aerial system noise. In 23rd AIAA/CEAS Aeroacoustics Conference (p. 4051). https://doi.org/10.2514/6.2017-4051
Clothier, R. A., Greer, D. A., Greer, D. G., & Mehta, A. M. (2015). Risk perception and the public acceptance of drones. Risk Analysis, 35(6), 1167-1183. https://doi.org/10.1111/risa.12330
Dukowitz, Z. (2022, October 5). Drone noise pollution: How loud are drones? UAV Coach. https://uavcoach.com/drone-noise-pollution/
Federal Aviation Administration. (2021). UAS beyond visual line of sight operations aviation rulemaking committee: Final report. https://www.faa.gov/regulations_policies/rulemaking/committees/documents/media/UAS _BVLOS_ARC_FINAL_REPORT_03102022.pdf
Federal Aviation Administration. (2023a). Drone delivery beyond visual line of sight. https://www.faa.gov/uas/advanced_operations/package_delivery_drone
Federal Aviation Administration. (2023b). UAS by the numbers. https://www.faa.gov/uas/resources/by_the_numbers
Federal Aviation Administration. (2024). Remote identification of drones. https://www.faa.gov/uas/getting_started/remote_id
Freeman, P. K., & Freeland, R. S. (2022). Integrating drones into local government: Policy considerations and frameworks for urban areas. Journal of Urban Technology, 29(1), 89- 107. https://doi.org/10.1080/10630732.2021.1982032
Gkartzonikas, C., & Gkritza, K. (2019). What have we learned? A review of stated preference and choice studies on autonomous vehicles. Transportation Research Part C: Emerging Technologies, 98, 323-337. https://doi.org/10.1016/j.trc.2018.12.003
Goldman Sachs. (2020). Drones: Reporting for work. Goldman Sachs Global Investment Research. https://www.goldmansachs.com/insights/technology-driving- innovation/drones/
Intaratep, N., Alexander, W. N., Devenport, W. J., Grace, S. M., & Dropkin, A. (2016). Experimental study of quadcopter acoustics and performance at static thrust conditions. In 22nd AIAA/CEAS Aeroacoustics Conference (p. 2873). https://doi.org/10.2514/6.2016- 2873
Ison, D. (2023). Analysis of noise distributions at heliports and vertiports: A guide for site selection and land use planning. Journal of Air Transport Management. https://doi.org/10.3926/jairm.403
Kopardekar, P., Rios, J., Prevot, T., Johnson, M., Jung, J., & Robinson, J. E. (2016). Unmanned aircraft system traffic management (UTM) concept of operations. In 16th AIAA Aviation Technology, Integration, and Operations Conference (p. 3292). https://doi.org/10.2514/6.2016-3292
Kuzma, J., Ahomaa, L., Wollmann, N., & Hatzakis, T. (2021). Public perceptions and attitudes toward urban drone use: A cross-national study. Technology in Society, 66, 101687. https://doi.org/10.1016/j.techsoc.2021.101687
Lidynia, C., Philipsen, R., & Ziefle, M. (2017). Droning on about drones—acceptance of and perceived barriers to drones in civil usage contexts. In Advances in Human Factors in Robots and Unmanned Systems (pp. 317-329). Springer. https://doi.org/10.1007/978-3-319-41959-6_26
McKinsey & Company. (2020). Commercial drones are here: The future of unmanned aerial systems. McKinsey Global Institute. https://www.mckinsey.com/industries/travel- logistics-and-infrastructure/our-insights/commercial-drones-are-here-the-future-of- unmanned-aerial-systems
Merkert, R., & Bushell, J. (2020). Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control. Journal of Air Transport Management, 89, 101929. https://doi.org/10.1016/j.jairtraman.2020.101929
Pauner, C., Kamara, I., & Viguri, J. (2018). Drones, privacy,,, and data protection: Regulatory responses in the EU and Spain. Computer Law & Security Review, 34(6), 1328-1342. https://doi.org/10.1016/j.clsr.2018.09.006
Pongsakornsathien, N., Bijjahalli, S., Gardi, A., Symons, A., Xi, Y., Sabatini, R., & Kistan, T. (2020). Performance assessment of drone detection techniques for airport environments in 3D using ML and MCDM methods. Transportation Research Part C: Emerging Technologies, 119, 102756. https://doi.org/10.1016/j.trc.2020.102756
Rao, B., Gopi, A. G., & Maione, R. (2016). The societal impact of commercial drones. Technology in Society, 45, 83-90. https://doi.org/10.1016/j.techsoc.2016.02.009
Rice, S., Winter, S. R., Mehta, R., & Ragbir, N. K. (2018). What factors predict the type of person who is willing to fly in an autonomous commercial airplane? Journal of Air Transport Management, 75, 131-138. https://doi.org/10.1016/j.jairtraman.2018.12.002
Rothstein, M. A. (2022). Drones and private property rights: The need for clear drone policies. Real Estate Law Journal, 50(3), 234-251.
Schäffer, B., Pieren, R., Heutschi, K., Wunderli, J. M., & Becker, S. (2021). Drone noise emission characteristics and noise effects on humans—A systematic review. International Journal of Environmental Research and Public Health, 18(11), 5940. https://doi.org/10.3390/ijerph18115940
Torija, A. J., Li, Z., & Self, R. H. (2020). Effects of a hovering unmanned aerial vehicle on urban soundscapes perception. Transportation Research Part D: Transport and Environment, 78, 102195. https://doi.org/10.1016/j.trd.2019.11.024
Torija, A. J., Self, R. H., & Lawrence, J. L. (2019). Psychoacoustic characterization of a small fixed-pitch quadcopter. The Journal of the Acoustical Society of America, 146(4), 3291- 3291. https://doi.org/10.1121/1.5130797
Wang, Y., Xia, H., Yao, Y., & Huang, Y. (2021). Public acceptance of drone food delivery services: Evidence from China. Journal of Cleaner Production, 307, 127223. https://doi.org/10.1016/j.jclepro.2021.127223
Yoo, W., Yu, E., & Jung, J. (2018). Drone delivery: Factors affecting the public's attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700. https://doi.org/10.1016/j.tele.2018.04.014