Human Control of Air Traffic Trajectory Optimizer

O. D. N. Turnbull; A. G. Richards

doi:10.1109/TITS.2017.2712637

Human Control of Air Traffic Trajectory Optimizer

O. D. N. Turnbull, A. G. Richards^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

4 Citations (Scopus)

492 Downloads (Pure)

Abstract

Supervisory constraints are developed for a trajectory optimizer for air traffic. By choosing to apply combinations of these constraints, a human controller can exercise intuitive influence over how conflicts between aircraft are resolved. This offers a compromise between the flexibility of an automated optimizer and the insight of a human controller. Requirements, such as the sense of resolution, - i.e., which aircraft goes first or over - are encoded as constraints on a mixed-integer linear program. Examples verify that the constraints work as expected and that the computation times required are reasonable.

Original language	English
Pages (from-to)	1091-1099
Number of pages	9
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	19
Issue number	4
Early online date	29 Jun 2017
DOIs	https://doi.org/10.1109/TITS.2017.2712637
Publication status	Published - 1 Apr 2018

Keywords

Air traffic control
human factors
trajectory optimization

Access to Document

10.1109/TITS.2017.2712637

Full-text PDF (accepted author manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/document/7959599/. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 596 KB

Handle.net

Persistent link

Cite this

@article{8ce23c28cbab44d4aea5e2193eeca3e5,

title = "Human Control of Air Traffic Trajectory Optimizer",

abstract = "Supervisory constraints are developed for a trajectory optimizer for air traffic. By choosing to apply combinations of these constraints, a human controller can exercise intuitive influence over how conflicts between aircraft are resolved. This offers a compromise between the flexibility of an automated optimizer and the insight of a human controller. Requirements, such as the sense of resolution, - i.e., which aircraft goes first or over - are encoded as constraints on a mixed-integer linear program. Examples verify that the constraints work as expected and that the computation times required are reasonable.",

keywords = "Air traffic control, human factors, trajectory optimization",

author = "Turnbull, \{O. D. N.\} and Richards, \{A. G.\}",

year = "2018",

month = apr,

day = "1",

doi = "10.1109/TITS.2017.2712637",

language = "English",

volume = "19",

pages = "1091--1099",

journal = "IEEE Transactions on Intelligent Transportation Systems",

issn = "1524-9050",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

number = "4",

}

TY - JOUR

T1 - Human Control of Air Traffic Trajectory Optimizer

AU - Turnbull, O. D. N.

AU - Richards, A. G.

PY - 2018/4/1

Y1 - 2018/4/1

N2 - Supervisory constraints are developed for a trajectory optimizer for air traffic. By choosing to apply combinations of these constraints, a human controller can exercise intuitive influence over how conflicts between aircraft are resolved. This offers a compromise between the flexibility of an automated optimizer and the insight of a human controller. Requirements, such as the sense of resolution, - i.e., which aircraft goes first or over - are encoded as constraints on a mixed-integer linear program. Examples verify that the constraints work as expected and that the computation times required are reasonable.

AB - Supervisory constraints are developed for a trajectory optimizer for air traffic. By choosing to apply combinations of these constraints, a human controller can exercise intuitive influence over how conflicts between aircraft are resolved. This offers a compromise between the flexibility of an automated optimizer and the insight of a human controller. Requirements, such as the sense of resolution, - i.e., which aircraft goes first or over - are encoded as constraints on a mixed-integer linear program. Examples verify that the constraints work as expected and that the computation times required are reasonable.

KW - Air traffic control

KW - human factors

KW - trajectory optimization

UR - https://www.scopus.com/pages/publications/85023749371

U2 - 10.1109/TITS.2017.2712637

DO - 10.1109/TITS.2017.2712637

M3 - Article (Academic Journal)

SN - 1524-9050

VL - 19

SP - 1091

EP - 1099

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

IS - 4

ER -

Human Control of Air Traffic Trajectory Optimizer

Abstract

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Inter-sample avoidance in trajectory optimizers using mixed-integer linear programming

SUPEROPT: Supervision of Route Optimizers

Professor Arthur G Richards

Cite this

Human Control of Air Traffic Trajectory Optimizer

Abstract

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Research output

Inter-sample avoidance in trajectory optimizers using mixed-integer linear programming

Projects

SUPEROPT: Supervision of Route Optimizers

Profiles

Professor Arthur G Richards

Cite this