Efficiency and Fairness in Unmanned Air Traffic Flow Management
Name
Chin-etal-UTM-IEEE_ITS_2020.pdf
Description
Accepted version
Size
5.63 MB
Format
Adobe PDF
Checksum (MD5)
67158e22049a8498889cd076fca082eb
Author(s)
Chin, Christopher
Gopalakrishnan, Karthik
Egorov, Maxim
Evans, Antony
Balakrishnan, Hamsa
Date Issued
2021
Journal
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chin, Christopher, Gopalakrishnan, Karthik, Egorov, Maxim, Evans, Antony and Balakrishnan, Hamsa. 2021. "Efficiency and Fairness in Unmanned Air Traffic Flow Management." IEEE Transactions on Intelligent Transportation Systems, 22 (9).
Version
Author's final manuscript
Abstract
IEEE As the demand for Unmanned Aircraft Systems (UAS) operations increases, UAS Traffic Flow Management (UTFM) initiatives are needed to mitigate congestion, and to ensure safety and efficiency. Congestion mitigation can be achieved by assigning airborne delays (through speed changes or path stretches) or ground delays (holds relative to the desired takeoff times) to aircraft. While the assignment of such delays may increase system efficiency, individual aircraft operators may be unfairly impacted. Dynamic traffic demand, variability in aircraft operator preferences, and differences in the market share of operators complicate the issue of fairness in UTFM. Our work considers the fairness of delay assignment in the context of UTFM. To this end, we formulate the UTFM problem with fairness and show through computational experiments that significant improvements in fairness can be attained at little cost to system efficiency. We demonstrate that when operators are not aligned in how they perceive or value fairness, there is a decrease in the overall fairness of the solution. We find that fairness decreases as the air-ground delay cost ratio increases and that it improves when the operator with dominant market share has a weak preference for the fairness of its allocated delays. Finally, we implemented UTFM in a rolling-horizon setting with dynamic traffic demand, and find that efficiency is adversely impacted. However, the impact on fairness is varied and depends on the metric used.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TITS.2020.3048356
