Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147460
Title: Future demand and optimum distribution of droneports
Authors: Zeng, Yixi
Low, Kin Huat
Schultz, Michael
Duong, Vu N.
Keywords: Engineering
Issue Date: 2020
Source: Zeng, Y., Low, K. H., Schultz, M. & Duong, V. N. (2020). Future demand and optimum distribution of droneports. 2020 IEEE International Conference on Intelligent Transportation Systems (ITSC). https://dx.doi.org/10.1109/ITSC45102.2020.9294283
Abstract: 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.
URI: https://hdl.handle.net/10356/147460
DOI: 10.1109/ITSC45102.2020.9294283
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ITSC45102.2020.9294283
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

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