Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-20T03:19:39.250Z Has data issue: false hasContentIssue false
  • English
  • Français

Airspace sectorisation via a weighted graph model

Published online by Cambridge University Press:  27 January 2016

D.F. Zhang  and
Y.Z. chen
D.F. Zhang*
Affiliation:
School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
Y.Z. chen*
Affiliation:
School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Extract

With the development of air traffic, flight delays happen frequently due to bad weather and traffic congestion. The problem can be solved partly by certain strategies, such as changing air routes. However, rerouting leads to a global imbalance in controller workload of current sectors and to an increase in co-ordination workload, and the workloads of some sectors may be beyond the controller’s ability to manage. Thus, airspace sectorisation is expected to migrate from the current static sectors to dynamically-changing ones capable of adapting to traffic demand. Besides addressing imbalance and controlling the increase in workload, the sectorisation has to meet additional geometric constraints such as convexity, connectivity, and minimum distance constraint.

Type
Technical Note
Information
The Aeronautical Journal , Volume 118 , Issue 1201 , March 2014 , pp. 267 - 274
Copyright
Copyright © Royal Aeronautical Society 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Trandac, H. and Duong, V. Optimised Sectorisation of Airspace with Constraints, Proceedings of the 5th Eurocontrol/FAA ATM R&D Seminar, Budapest, Hungary, June 2003.Google Scholar
2. Yousefi, A. and Donohue, G.L. Temporal and Spatial Distribution of Airspace Complexity for New Methodologies in Airspace Design, Proceedings of the 4th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Chicago, IL, USA, September 2004.Google Scholar
3. Klein, A. An Efficient Method for Airspace Analysis and Partitioning Based on Equalized Traffic Mass, Proceedings of the 6th USA/Europe Seminar on Air Traffic Management Research and Development, Baltimore, MD, USA, June 2005.Google Scholar
4. Drew, M. Analysis of an Optimal Sector Design Method, The 27th Digital Avionics Systems Conference, IEEE/AIAA, St. Paul, MN, USA, 2008, p 3, .B.4-1-3.B.4-1-10.Google Scholar
5. Tien, S.-L. and Hoffman, R. Optimizing Airspace Sectors for Varying Demand Patterns Using Multi-Controller Staffng, 8th USA/Europe Air Traffic Management Research and Development Seminar, California, USA, June 2009.Google Scholar
6. Brinton, C. and Pledgie, S. Airspace Partitioning Using Flight Clustering and computational Geometry, 27th Digital Avionics Systems Conference, NJ, USA, October 2008, 1, p 3, .B.3–1–3.B.3–10.Google Scholar
7. Basu, A., Mitchell, J. and Sabhnani, G. Geometric Algorithms for Optimal Airspace Design and Air Traffic Controller Workload Balancing, 16th Fall Workshop on Computational and Combinatorial Geometry, Cambridge, UK, November 2008.Google Scholar
8. Delahaye, D., Schoenauer, M and Alliot, J.M. Airspace Sectoring by Evolutionary Computation, Proceedings of the IEEE International Congress on Evolutionary Computation, Anchorage, USA, 1998.Google Scholar
9. Xue, M. Airspace Sector Redesign Based on Voronoi Diagrams, AIAA Guidance, Navigation, and Control Conference, August 2008.Google Scholar
10. Martinez, S., Chatterji, G., Sun, D and Bayen, A. A Weighted-graph Approach for Dynamic Airspace Configuration, Proceedings of the AIAA Guidance, Navigation, and Control Conference, Hilton Head, SC, USA, August 2007.Google Scholar
11. Zhang, D.F., Chen, Y.Z., Bi, H. and Song, Z.X. Airspace Sectorisation Based on Weighted Graph Spectral Bisection Algorithm, 12th COTA International Conference of Transportation Professionals, Beijing, China, August 2012.Google Scholar
12. Li, J.H., Wang, T., Savai, M. and Hwang, I. Graph-based algorithm for dynamic airspace configuration, J Guidance, Control, and Dynamics, 2010, 33, (1), pp 1082–1095.Google Scholar
13. Fortune, S.J. A sweepline algorithm for voronoi diagrams, Algorithmica, 1987, 2, pp 153–174.Google Scholar
14. Hendricksonand, B. and Leland, R. An improved spectral graph partitioning algorithm for mapping parallel computations, J SIAM Sci Comput, 1995, 16, (1), pp 452–469.Google Scholar
15. Hu, Y.F. and Blake, R.J. An improved diffusion algorithm for dynamic load balancing, J Parallel Computing, April 1999, 25, (4), pp 417–444.Google Scholar
16. Walshaw, C., Cross, M and Everett, M.G. Parallel dynamic partitioning for adaptive unstructured meshes, J Parallel And Distributed Computing, 1997, 47, pp 102–108.Google Scholar