Flight risk analysis for logistics drone routes:

DOI
  • Feng JingGe
    Graduate School of Life and Environmental Sciences University of Tsukuba

Bibliographic Information

Other Title
  • 商用物流ドローンの配達航路とリスク分析
  • ―つくば市を例に―
  • A case study of Tsukuba city

Abstract

Abstract<br>  With the rapid development of drone technology, many researchers have started to conduct studies on the use of drones in logistics. In 2013, Amazon first proposed the concept of using drone for logistics distribution. Since then, other companies, like DHL, Google, and Alibaba and JD in China, have also started their development programs in drone logistics. In Japan, several Specific Economic Zones and many supporting policies have been especially set up by the government for supporting scientific research and promoting industry development for using drone in logistics. However, the study in this field is still in the experimental stage on the world wide, and there has not been any mature business models. The main reason includes that, the legislation and regulation processes are far behind the rapid technical development, and the public still holds great concerns about the safety of this new technology. <br>  Approaching from geographic methods, this paper conducts the risk cost analysis of flight routes for drones in logistics.  First, this paper focuses on the risk factors during the landing and flight phases and calculates the feasible landing zone and flight space, using the land use analysis method in GIS technology. Second, through Euclidean distance analyses and raster calculations, this paper assesses the impacts of each risk factor and evaluates the specific risk cost, using an example in Tsukuba city. Third, based on the risk cost analysis, this paper uses cost distance analysis to design different flight routes for different scenarios. This study contributes to providing academic references for improving basic safety and reducing flight risks in popularizing the use of drones in logistics.

Journal

Details 詳細情報について

  • CRID
    1390282680672332160
  • NII Article ID
    130005635696
  • DOI
    10.14866/ajg.2017s.0_100243
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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