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Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network

Author

Listed:
  • Peng Han

    (School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China)

  • Xinyue Yang

    (School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China)

  • Yifei Zhao

    (School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China)

  • Xiangmin Guan

    (Zhe Jiang Key Laboratory of General Aviation Operation Technology, Jiande 311612, China
    Department of General Aviation, Civil Aviation Management Institute of China, Beijing 100102, China)

  • Shengjie Wang

    (Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Administration of China (CAAC), Guanghan 300201, China)

Abstract

The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10 −8 under the action of risk-mitigation measures.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5733-:d:811631
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    References listed on IDEAS

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    1. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Yafei Li & Minghuan Liu, 2022. "Path Planning of Electric VTOL UAV Considering Minimum Energy Consumption in Urban Areas," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    2. 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.
    3. Snežana Tadić & Mladen Krstić & Miloš Veljović & Olja Čokorilo & Milica Milovanović, 2024. "Risk Analysis of the Use of Drones in City Logistics," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    4. Mian Ye & Jinchen Zhao & Quanli Guan & Xuejun Zhang, 2024. "Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm," Sustainability, MDPI, vol. 16(2), pages 1-30, January.
    5. Sun, Xuting & Hu, Yue & Qin, Yichen & Zhang, Yuan, 2024. "Risk assessment of unmanned aerial vehicle accidents based on data-driven Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

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