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This paper combines Bayesian networks (BN) and information theory to model the likelihood of severe loss of separation (LOS) near accidents, which are considered mid-air collision (MAC) precursors. BN is used to analyze LOS contributing factors and the multi-dependent relationship of causal factors, while Information Theory is used to identify the LOS precursors that provide the most information. The combination of the two techniques allows us to use data on LOS causes and precursors to define warning scenarios that could forecast a major LOS with severity A or a near accident, and consequently the likelihood of a MAC. The methodology is illustrated with a case study that encompasses the analysis of LOS that have taken place within the Spanish airspace during a period of four years.

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Aircraft Operators Companies (AOCs) are always willing to keep the cost of a flight as low as possible. These costs could be modelled using a function of the fuel consumption, time of flight and fixed cost (over flight cost, maintenance, etc.). These are strongly dependant on the atmospheric conditions, the presence of winds and the aircraft performance. For this reason, much research effort is being put in the development of numerical and graphical techniques for defining the optimal trajectory. This paper presents a different approach to accommodate AOCs preferences, adding value to their activities, through the development of a tool, called aircraft trajectory simulator. This tool is able to simulate the actual flight of an aircraft with the constraints imposed. The simulator is based on a point mass model of the aircraft.The aim of this paper is to evaluate 3DoF aircraft model errors with BADA data through real data from Flight Data Recorder FDR. Therefore, to validate the proposed simulation tool a comparative analysis of the state variables vector is made between an actual flight and the same flight using the simulator. Finally, an example of a cruise phase is presented, where a conventional levelled flight is compared with a continuous climb flight. The comparison results show the potential benefits of following user-preferred routes for commercial flights.

Binary decision diagram (BDD) methodology is the most recent approach to improve Boolean reliability models assessment. The final size of the BDD, and therefore the ultimate benefits of this technique, are very sensitive to the initial variable ordering that has to be fixed prior to conversion. Several variable ordering strategies have been proposed in the literature, all of them focused on the treatment of single fault tree models. This paper proposes some extensions of existing variable ordering schemes for the case of combinations of non-disjoint fault trees, as is the case in quantifying sequences of event trees. These extensions work by combining ordering schemes applied to each fault tree, and exploring the cases where variables within the domains intersection are kept together or not. They have been specifically designed to be applied together with an incremental procedure to compute the BDD of the sequence accumulatively and to be used to quantify sequences of dynamic event trees. Preliminary results show the potential of this approach.