Future demand and optimum distribution of droneports
Due to the growing usage of Unmanned Aerial Vehicles (UAVs, or drones) in commercial, civil, and military applications, thousands of drones are expected in the urban airspace for many decades to come. The large traffic volume of drones brings many concerns about safety issues especially during the t...
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| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/147460 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Due to the growing usage of Unmanned Aerial Vehicles (UAVs, or drones) in commercial, civil, and military applications, thousands of drones are expected in the urban airspace for many decades to come. The large traffic volume of drones brings many concerns about safety issues especially during the taking-off, approaching, and landing phases when most accidents and incidents occur. In this paper, a facility called droneport is conceived to accommodate and manage assorted drones taking off and landing in a protected space under air traffic control. We present several contributions to the concept of droneport: (1) The future delivery drone demand was forecasted using historical online retailer data and the Holt-Winters’ seasonal method. (2) The optimum number and distribution of droneports were determined by a multi-objective optimization model considering both costs and societal value from six aspects: maximizing e-commerce demand coverage, airtaxi demand coverage, subzone coverage, and area coverage, and minimizing service distance for both parcel and passenger delivery drones. (3) The optimization model integrates Gaussian noise to make the measurement of service distance more practical. (4) The future capacity of each droneport was estimated based on the number of droneports and their placement. A real-world case study was carried out for Singapore. Overall, this paper presented an intuitive and efficient optimization model for the placement of droneports with predicted drone demand and forecasted the capacity of each droneport. |
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