IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12247-d1214729.html
   My bibliography  Save this article

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

Author

Listed:
  • Hongbo He

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China)

  • Xiaohan Liao

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China
    The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China)

  • Huping Ye

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China
    The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China)

  • Chenchen Xu

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China
    The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China)

  • Huanyin Yue

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China
    The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

With the rapid increase in unmanned aerial vehicles (UAVs), ensuring the safety of airspace operations and promoting sustainable development of airspace systems have become paramount concerns. However, research dedicated to investigating the population exposure risks of UAV operations in urban areas and their spatial pattern is still missing. To address this gap, this study evenly divides the urban space into uniform grids and calculates critical areas for two UAV types within each grid. By integrating geospatial data, including buildings, land use, and population, data-driven risk maps are constructed to assess the spatial distribution patterns and potential population exposure risks of two UAV types and compare them with commonly used census units. The results indicate that the mean time between failures (MTBF) for the selected generic and rotary-type UAVs can be up to 9.04 × 10 8 h and 1.22 × 10 8 h, respectively, at acceptable risk levels, considering uncertainties. The spatial pattern of population exposure risk exhibits spatial heterogeneity and multi-scale effects in urban areas, aligning with population distribution. High-risk areas concentrate in regions characterized by high population mobility, such as transport hubs, commercial service areas, residential zones, and business districts. Additionally, the comparation emphasizes the potential bias introduced by using census units in risk assessment, especially in regions with significant urban build-up. This framework enables the evaluation of safety and acceptability across diverse urban land use areas and offers guidance for airspace management in megacities, ensuring the safe integration of UAVs in urban environments.

Suggested Citation

  • Hongbo He & Xiaohan Liao & Huping Ye & Chenchen Xu & Huanyin Yue, 2023. "Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12247-:d:1214729
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12247/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/16/12247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guolei Zhou & Chenggu Li & Yanjun Liu & Jing Zhang, 2020. "Complexity of Functional Urban Spaces Evolution in Different Aspects: Based on Urban Land Use Conversion," Complexity, Hindawi, vol. 2020, pages 1-12, September.
    2. Dario Floreano & Robert J. Wood, 2015. "Science, technology and the future of small autonomous drones," Nature, Nature, vol. 521(7553), pages 460-466, May.
    3. Melnyk, Richard & Schrage, Daniel & Volovoi, Vitali & Jimenez, Hernando, 2014. "A third-party casualty risk model for unmanned aircraft system operations," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 105-116.
    4. Peng Han & Xinyue Yang & Yifei Zhao & Xiangmin Guan & Shengjie Wang, 2022. "Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network," Sustainability, MDPI, vol. 14(9), pages 1-13, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ilona Kulikovskikh & Sergej Prokhorov & Tomislav Lipić & Tarzan Legović & Tomislav Šmuc, 2019. "BioGD: Bio-inspired robust gradient descent," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.
    2. Stöcker, Claudia & Bennett, Rohan & Koeva, Mila & Nex, Francesco & Zevenbergen, Jaap, 2022. "Scaling up UAVs for land administration: Towards the plateau of productivity," Land Use Policy, Elsevier, vol. 114(C).
    3. Zenan Shen & Shaoquan Liu & Wei Zhu & Daoyuan Ren & Qiang Xu & Yu Feng, 2024. "A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-Rotor Drones," Energies, MDPI, vol. 17(16), pages 1-36, August.
    4. Kloster, Konstantin & Moeini, Mahdi & Vigo, Daniele & Wendt, Oliver, 2023. "The multiple traveling salesman problem in presence of drone- and robot-supported packet stations," European Journal of Operational Research, Elsevier, vol. 305(2), pages 630-643.
    5. Richard Melnyk & Daniel Schrage & Vitali Volovoi & Hernando Jimenez, 2014. "Sense and Avoid Requirements for Unmanned Aircraft Systems Using a Target Level of Safety Approach," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1894-1906, October.
    6. Zhu, Xun & Pasch, Timothy J. & Bergstrom, Aaron, 2020. "Understanding the structure of risk belief systems concerning drone delivery: A network analysis," Technology in Society, Elsevier, vol. 62(C).
    7. Yandong Xiao & Xiaokang Lei & Zhicheng Zheng & Yalun Xiang & Yang-Yu Liu & Xingguang Peng, 2024. "Perception of motion salience shapes the emergence of collective motions," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    8. Kai Fukami & Kunihiko Taira, 2023. "Grasping extreme aerodynamics on a low-dimensional manifold," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    9. Gonzalo Fernandez-Sanchez & Alvaro Fernandez-Heredia, 2018. "Strategic Thinking for Sustainability: A Review of 10 Strategies for Sustainable Mobility by Bus for Cities," Sustainability, MDPI, vol. 10(11), pages 1-15, November.
    10. Blom, Henk A.P. & Jiang, Chenpeng & Grimme, Wouter B.A. & Mitici, Mihaela & Cheung, Yuk S., 2021. "Third party risk modelling of Unmanned Aircraft System operations, with application to parcel delivery service," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Schniederjans, Dara G. & Curado, Carla & Khalajhedayati, Mehrnaz, 2020. "Supply chain digitisation trends: An integration of knowledge management," International Journal of Production Economics, Elsevier, vol. 220(C).
    12. Jianwei Sun & Koichi Yonezawa & Eiji Shima & Hao Liu, 2023. "Integrated Evaluation of the Aeroacoustics and Psychoacoustics of a Single Propeller," IJERPH, MDPI, vol. 20(3), pages 1-23, January.
    13. Christian Wankmüller & Christian Truden & Christopher Korzen & Philipp Hungerländer & Ewald Kolesnik & Gerald Reiner, 2020. "Optimal allocation of defibrillator drones in mountainous regions," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 785-814, September.
    14. Ghada Talat Alhothali & Felix T. Mavondo & Bader A. Alyoubi & Haneen Algethami, 2024. "Consumer Acceptance of Drones for Last-Mile Delivery in Jeddah, Saudi Arabia," Sustainability, MDPI, vol. 16(13), pages 1-21, June.
    15. David Reiser & Galibjon M. Sharipov & Gero Hubel & Volker Nannen & Hans W. Griepentrog, 2023. "Development and Experimental Validation of an Agricultural Robotic Platform with High Traction and Low Compaction," Agriculture, MDPI, vol. 13(8), pages 1-15, July.
    16. He Sun & Xueming Li & Yingying Guan & Shenzhen Tian & He Liu, 2021. "The Evolution of the Urban Residential Space Structure and Driving Forces in the Megacity—A Case Study of Shenyang City," Land, MDPI, vol. 10(10), pages 1-19, October.
    17. Wang, Ning & Mutzner, Nico & Blanchet, Karl, 2023. "Societal acceptance of urban drones: A scoping literature review," Technology in Society, Elsevier, vol. 75(C).
    18. Oh, Soohwan & Yoon, Yoonjin, 2024. "Urban drone operations: A data-centric and comprehensive assessment of urban airspace with a Pareto-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    19. Fügener, A. & Grahl, J. & Gupta, A. & Ketter, W., 2019. "Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation," ERIM Report Series Research in Management ERS-2019-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. Pang, Bizhao & Hu, Xinting & Dai, Wei & Low, Kin Huat, 2022. "UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12247-:d:1214729. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.