Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo. (December 2018)
- Record Type:
- Journal Article
- Title:
- Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo. (December 2018)
- Main Title:
- Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo
- Authors:
- Murça, Mayara Condé Rocha
Hansman, R. John
Li, Lishuai
Ren, Pan - Abstract:
- Highlights: A data analytics framework is developed to characterize air traffic flows. Trajectory patterns and traffic flow patterns are learned from flight tracks. Terminal area operations in three multi-airport systems are compared. Structural, operational and performance differences are identified. The most dynamic system shows the highest average traffic flow efficiency. Abstract: Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results revealHighlights: A data analytics framework is developed to characterize air traffic flows. Trajectory patterns and traffic flow patterns are learned from flight tracks. Terminal area operations in three multi-airport systems are compared. Structural, operational and performance differences are identified. The most dynamic system shows the highest average traffic flow efficiency. Abstract: Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather. … (more)
- Is Part Of:
- Transportation research. Volume 97(2018)
- Journal:
- Transportation research
- Issue:
- Volume 97(2018)
- Issue Display:
- Volume 97, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 97
- Issue:
- 2018
- Issue Sort Value:
- 2018-0097-2018-0000
- Page Start:
- 324
- Page End:
- 347
- Publication Date:
- 2018-12
- Subjects:
- Trajectory data analytics -- Air traffic flows -- Terminal area -- Multi-airport systems -- Machine learning
Transportation -- Periodicals
Transportation -- Technological innovations -- Periodicals
388.011 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0968090X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.trc.2018.10.021 ↗
- Languages:
- English
- ISSNs:
- 0968-090X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9026.274620
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9003.xml