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

Xinhui Ren and Caixia Cheng
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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

Sustainability, 2020, vol. 12, issue 20, 1-15

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.

Keywords: safety; UAV delivery; third-party risk; risk assessment model; noise; privacy violation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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