Complex domestic airspace requires collision risk models and monitoring tools suitable for arbitrary aircraft trajectories. This paper presents a new mathematically based collision risk approach that extends the International Civil Aviation Organisation (ICAO) models to full aircraft encounters based on real trajectory data. A new continuous time intervention model is presented, along with a position uncertainty propagation model that better reflects aircraft behaviour and allows generalisation to all trajectories to eliminate degenerate cases. The proposed risk model is computationally efficient compared to the models it is based on and can be applied to large-scale trajectory data. The utility of the model is demonstrated through a series of case studies using real aircraft trajectories.

Given the unprecedented growth in air transport, air traffic controllers and safety managers are exploring new approaches for collision risk assessment specifically in real time or near future term. In this paper a real time predictive approach for identifying worst case sector collision risk is proposed. An evolutionary framework is introduced to evolve flight maneuvers that may lead a traffic scenario to high collision risk. The proposed methodology discretizes the execution time of a baseline air traffic scenario into discrete time intervals based traffic scenarios. These traffic scenarios are then initialized at their given time intervals in a sector which are then perturbed with flight maneuvers to maximize collision risk. Flight maneuvers are evolved using an evolutionary framework with the objective of maximizing the collision risk for a given traffic scenario. Results indicate the effectiveness of the proposed methodology to successfully identify flight maneuvers which may increases the collision risk for a look ahead time. Results also indicate that when most of the flights are in the first half of their flight path, DESCEND and TURN RIGHT maneuvers may increase the collision risk. Also maneuvers, when flights are exiting the sector, do not significantly effect the collision risk. It was also found that traffic flow should be managed when flights enter the sector or when flights are exiting the sector (to facilitate coordination with neighboring sector controller) as this may reduce the collision risk.

One key measure for judging on the safety of operations in an airspace is the collision risk estimate. Comparing this estimate to the target level of safety (TLS), a quantitative value provides an objective assessment on airspace safety of operations. However, this quantitative value does not provide any reasoning or insight on the interaction between sector and traffic features on the one hand, and the air traffic controllers (ATC) actions for traffic flow management (TFM) on the other hand. There are two fundamentally different approaches to manage high risk scenarios. One is post the event, where we need to ask what we should do to reduce risk in a scenario that is already high risk. The second is a preventive pre-the-event approach, where we can ask the question of what are the causes that make a low risk scenario becoming a high risk one. By identifying these causes, one can prevent risk to escalate. This paper is about this second approach. We propose an evolutionary multi-objective scenario-based methodology for the systemic identification of airspace collision risk tipping points. The methodology attempts to help us to gain insight into the interaction of traffic and sector features with ATC actions that can lead to an increase in collision risk in an otherwise low risk traffic scenario. Results indicate that "risk-free" scenarios having collision risk below the TLS can become "risk-prone" by very few or even just one ATC action. We found that a "Turn To Next Waypoint" ATC action, which is very common in traffic flow management for expediting aircraft to meet their metering requirements, may significantly increase collision risk due to unexpected flight crossings with undesired crossing angles and relative speed with other aircraft in the vicinity. The results also provide an understanding of ATC actions in a given scenario that should be avoided to reduce collision risk. (C) 2013 Elsevier Ltd. All rights reserved.