Collision Risk Modeling and Analysis for Lateral Separation to Support Unmanned Traffic Management
This article reports on the use of collision risk modeling approaches for lateral separation to support unmanned aircraft traffic management and network design. The authors briefly review the field of manned collision risk modeling and then introduce the Reich model. They go on to discuss the commonly-used overlap probability equation, strategies to analyze collision risk surfaces for trading off separation distance with navigation performance, the use of the Reich model for collision risk in an airspace consisting of multiple parallel tracks. The authors present a case study (Brisbane, Australia) to investigate the impact of navigation performance on separation distance and the collision risk, including how this affects airspace design over an urban area. The authors conclude that lateral separation distances less than 100 m are achievable for small unmanned aerial systems (UAS), and that the separation standards are mostly affected by the proportion of poorly navigating aircraft. Three appendices offer details of the mathematical methods used in this study.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/02724332
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Authors:
- Kallinen, Valtteri
- McFadyen, Aaron
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Print
- Features: Appendices; Figures; Photos; References; Tables;
- Pagination: pp 854-881
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Serial:
- Risk Analysis
- Volume: 42
- Issue Number: 4
- Publisher: Society for Risk Analysis
- ISSN: 0272-4332
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1539-6924
Subject/Index Terms
- TRT Terms: Air traffic control; Crash risk forecasting; Mathematical models; Risk assessment; Unmanned aircraft systems
- Geographic Terms: Brisbane (Australia)
- Subject Areas: Aviation; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01858898
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 26 2022 9:10AM