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Model of Third-Party Risk Index for Unmanned Aerial Vehicle Delivery in Urban Environment

Author

Listed:
  • Xinhui Ren

    (College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China)

  • Caixia Cheng

    (College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China)

Abstract

In order to assess the airspace risk of unmanned aerial vehicles (UAVs) operating at low altitudes, the third-party risks of UAV urban operations were defined: the risks caused by drones to people on the ground who are not involved in operations and do not profit from operations, and the sources and objects of the risk were clarified. Taking into account the drone crash, noise, on-board camera, and ground environment factors, a UAV urban logistics risk index evaluation model was constructed. First, the UAV image regression model was used to construct a three-dimensional grid, and then a comprehensive third-party risk index model of UAV urban logistics was built based on the casualty and noise risks. Finally, the Southern District of Civil Aviation University of China was selected as an example scene, and surface data were obtained through a field investigation and instrument measurements. Then, the risk of drone operations in the airspace 30–60 m above this area was evaluated. The results showed that the third-party risk was lower when the UAV flying altitude above a building was greater. However, in other areas such as lakes, woods, roads, open spaces, a lower flight altitude had a lower risk. A comparison of the whole airspace showed that the third-party risk was the lowest when the drone operated at an altitude of 30 m. The results also showed that the third-party risk above the lake and greenery was the lowest when on the same plane, followed by the lower risk above the buildings and open squares, with the highest third-party risk above the canteen passage.

Suggested Citation

  • Xinhui Ren & Caixia Cheng, 2020. "Model of Third-Party Risk Index for Unmanned Aerial Vehicle Delivery in Urban Environment," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8318-:d:425582
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    References listed on IDEAS

    as
    1. Kim, Sang Hyun, 2020. "Choice model based analysis of consumer preference for drone delivery service," Journal of Air Transport Management, Elsevier, vol. 84(C).
    2. Al Haddad, Christelle & Chaniotakis, Emmanouil & Straubinger, Anna & Plötner, Kay & Antoniou, Constantinos, 2020. "Factors affecting the adoption and use of urban air mobility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 696-712.
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    Cited by:

    1. 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).
    2. Osakwe, Christian Nedu & Hudik, Marek & Říha, David & Stros, Michael & Ramayah, T., 2022. "Critical factors characterizing consumers’ intentions to use drones for last-mile delivery: Does delivery risk matter?," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    3. Thuy-Hang Tran & Dinh-Dung Nguyen, 2022. "Management and Regulation of Drone Operation in Urban Environment: A Case Study," Social Sciences, MDPI, vol. 11(10), pages 1-19, October.

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