Citation

Nguyen MIH, Alam S. IEEE Trans. Intel. Transp. Syst. 2018; 19(1): 48-57.

Copyright

(Copyright © 2018, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2017.2691000

PMID

unavailable

Abstract

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.

Language: en

Keywords

2D geographic information system model; aerospace computing; air safety; air traffic control; air traffic data; air traffic safety; air-traffic crossings; Aircraft; airspace collision risk hot-spot identification; Atmospheric modeling; Bahrain en-route airspace; cluster features; cluster potential collisions; clustering; Clustering methods; clustering models; collision avoidance; Collision risk; collision risk estimate; Computational modeling; data visualisation; geographic information systems; Mathematical model; Numerical models; one-month traffic data; pattern clustering; quantitative basis; risk assessment; risk management; Safety