Optimizing Lateral Airway Offset for Collision Risk Mitigation Using Differential Evolution

Abstract

A majority of aircraft are now using Global Navigation Satellite System (GNSS) for navigation. This has led to an effect of reducing the magnitude of lateral deviations from the route center line and, consequently, increasing the probability of a collision, should a loss of vertical separation between aircraft on the same route occur. The International Civil Aviation Organization (ICAO) has introduced Strategic Lateral Offset Procedures (SLOP) that allow suitably equipped aircrafts to fly with 1nmi or 2nmi lateral offset to the right of airway centerline in oceanic airspace. Very few aircraft, however, are using the SLOP procedure because of the lack of understanding of its safety benefits and implementation issues in identifying correct lateral offset that can reduce the collision risk. This paper proposes an Evolutionary Computation framework using Differential Evolution process to identify optimal lateral offsets for each airway in a given airspace such that it reduces the overall collision risk. Airway specific lateral offsets are then correlated with airway-traffic features using Multiple Regression models to identify which features can explain the optimal lateral offset. The proposed approach establishes a generic mapping that can suggest optimal lateral offsets for a given airspace based on airway-traffic features to mitigate collision risk. The proposed methodology is applied to Collision Risk assessment of one-day traffic data (710 flights) in Bahrain Upper Airspace (FL290-FL410) to estimate optimal lateral offset that resulted in significant reduction of collision risk. Further, the number of flights and crossings on an airway were identified as key features affecting optimal lateral offset.