Airspace Collision Risk Hot-Spot Identification using Clustering Models

• Published in: IEEE transactions on intelligent transportation systems 2018-01, Vol.19 (1), p.48-57
• Main Authors: Nguyen, Minh-Ha, Alam, Sameer
• Format: Article
• Language: English
• Subjects: air traffic safety; Aircraft; Atmospheric modeling; clustering; Clustering methods; Collision risk; Computational modeling; Mathematical model; Numerical models; Safety
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2017.2691000

A key safety indicator for airspace is its collision risk estimate, which is compared against a target level of safety to provide a quantitative basis for judging the safety of operations in airspace. However, this quantitative basis fails to provide any insight regarding the magnitude, location, and timing of the risk of collision, distributed within a given airspace. In this paper, we propose a methodology for the identification of collision risk hot spots in a given airspace. The proposed methodology consists of processing air traffic data and developing traffic routes based on entry and exit points within the airspace. These routes and other flight information are then used to project air-traffic crossings and cluster potential collisions. The proposed method then estimates the collision risk for each identified cluster, culminating in risk assessment for the entire airspace. The model extends and adopts the state-of-the art clustering models, systemically identifies airspace collision risk hot spots, and further analyses hot spots by analyzing cluster features (number of points and contribution to overall risk) with flight levels and time of day. Experiments were conducted using one-month traffic data (25 440 flights) from Bahrain en-route airspace. By visualizing crossing points and clustering them in a 2-D geographic information system model we are able to identify collision risk hot spots, which contribute significantly to overall collision risk.