Airspace sectorisation based on Voronoi diagrams
The increasing air traffic demand has called for more efficient methods of airspace sectorisation to prevent overloaded sectors. Overloaded sectors lead to congestion which can impose delays and traffic rerouting, resulting in billions of dollars lost every year. Airspace sectorisation partitioned t...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/68603 |
| _version_ | 1826116725113880576 |
|---|---|
| author | Ang, Ding Jie |
| author2 | Mao Jianfeng |
| author_facet | Mao Jianfeng Ang, Ding Jie |
| author_sort | Ang, Ding Jie |
| collection | NTU |
| description | The increasing air traffic demand has called for more efficient methods of airspace sectorisation to prevent overloaded sectors. Overloaded sectors lead to congestion which can impose delays and traffic rerouting, resulting in billions of dollars lost every year. Airspace sectorisation partitioned the airspace into sectors such that workloads between sectors are balanced and total system cost is minimised. This work examined the use of Voronoi Diagrams and Genetic Algorithms to sectorise
airspace.
Two airspace sectorisation models were developed and test using flight data from a small airspace with modest traffic. The first model successfully balanced surveillance workload between sectors. However, multiple optimal solutions were obtained. The second one managed to minimise coordination workload, while balancing surveillance workload between sectors. With the inclusion of minimum distance constraint and minimum sector crossing time constraint, a particular unique solution type was obtained from the model.
Through the implementation of these two models, the strength and weakness of using Voronoi Diagrams and Genetic Algorithm became clear. Using Voronoi Diagrams automatically fulfils the convexity and connectivity constrains, but unique solutions are usually not achievable as different site locations can give rise to sector boundaries that are essentially the same. Genetic Algorithm can be implemented with relative ease, but it does not scale well with complexity and the efficiency of the model can be significantly affected by the local optima problem. |
| first_indexed | 2024-10-01T04:16:24Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/68603 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:16:24Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/686032023-03-04T18:24:12Z Airspace sectorisation based on Voronoi diagrams Ang, Ding Jie Mao Jianfeng School of Mechanical and Aerospace Engineering DRNTU::Engineering The increasing air traffic demand has called for more efficient methods of airspace sectorisation to prevent overloaded sectors. Overloaded sectors lead to congestion which can impose delays and traffic rerouting, resulting in billions of dollars lost every year. Airspace sectorisation partitioned the airspace into sectors such that workloads between sectors are balanced and total system cost is minimised. This work examined the use of Voronoi Diagrams and Genetic Algorithms to sectorise airspace. Two airspace sectorisation models were developed and test using flight data from a small airspace with modest traffic. The first model successfully balanced surveillance workload between sectors. However, multiple optimal solutions were obtained. The second one managed to minimise coordination workload, while balancing surveillance workload between sectors. With the inclusion of minimum distance constraint and minimum sector crossing time constraint, a particular unique solution type was obtained from the model. Through the implementation of these two models, the strength and weakness of using Voronoi Diagrams and Genetic Algorithm became clear. Using Voronoi Diagrams automatically fulfils the convexity and connectivity constrains, but unique solutions are usually not achievable as different site locations can give rise to sector boundaries that are essentially the same. Genetic Algorithm can be implemented with relative ease, but it does not scale well with complexity and the efficiency of the model can be significantly affected by the local optima problem. Bachelor of Engineering (Aerospace Engineering) 2016-05-30T02:06:11Z 2016-05-30T02:06:11Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68603 en Nanyang Technological University 71 p. application/pdf |
| spellingShingle | DRNTU::Engineering Ang, Ding Jie Airspace sectorisation based on Voronoi diagrams |
| title | Airspace sectorisation based on Voronoi diagrams |
| title_full | Airspace sectorisation based on Voronoi diagrams |
| title_fullStr | Airspace sectorisation based on Voronoi diagrams |
| title_full_unstemmed | Airspace sectorisation based on Voronoi diagrams |
| title_short | Airspace sectorisation based on Voronoi diagrams |
| title_sort | airspace sectorisation based on voronoi diagrams |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/68603 |
| work_keys_str_mv | AT angdingjie airspacesectorisationbasedonvoronoidiagrams |