The ash cloud from the Eyjafjallajökull volcano in Iceland caused a significant impact on aviation in April and May 2010. In just the period between April 14 and 21, more than 100,000 flights were cancelled, with more than $1.7 billion in lost revenues for airlines and more than 10 million stranded passengers. The magnitude of the impact was caused by the extent of the ash cloud coupled with the duration of the event and the consequent volcanic ash procedures in effect. This event provided significant insight into the handling of the volcanic ash crisis and stirred aviation regulators and other aviation organizations all over the world into action. The unfolding of events is presented; an overview of the lessons learned–such as the need for determination of hazardous ash concentration levels, improvement of forecast validation, and amelioration of information exchange–is given.
This paper extends the use of peak-load pricing (PLP) to the context of the European Air Traffic Management system, as EU regulation No 391/2013 allows the modulation of en-route charges to avoid network capacity-demand imbalance in a specific area or on a specific route at specific times. In particular, we propose a centralised approach to PLP (CPLP) where a Central Planner (CP) is responsible for setting en-route charges on the network and Airspace Users (AUs) assess the routing of each flight. Set en-route charges should guarantee that air navigation service providers (ANSPs) are able to recover their operational costs, and that AUs perform their flights avoiding imbalances between demand and available airspace capacity. Like in the current charging system, in CPLP AUs react to en-route charges (which are imposed by CP instead of ANSPs) by choosing alternative and cheaper routes. Hence, we model this relationship between the CP and the AUs as a Stackelberg game where a leader (CP) makes his/her decision first, with complete knowledge on how the follower(s) (AUs) would react to it. The Stackelberg equilibrium is obtained by means of an optimisation problem formulated as a bilevel mixed-integer linear programming model, where the CP sets, for each ANSP, one peak and one off-peak en-route charge and the AUs make their routing choice. Preliminary results on real data instances on a regional scale are presented.
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