Identification of Traffic Patterns and Selection of Representative Traffic Samples for the Assessment of ATM Performance Problems

Despite being the only reliable way to assess the impact of future ATM solutions, the complexity of large-scale, bottom-up microsimulation models is often a barrier for their effective use to support decision-making. As a consequence, in many cases the simulations are limited to one or few particular days, usually selected based on expert judgement and/or simple rule-of-thumb criteria (e.g., simulate the day with the highest number of scheduled flights). This may not be representative of the impact of a given operational improvement under all possible traffic scenarios, especially considering the extreme complexity of the European airspace, with significantly different traffic flows on different days of the year in terms of traffic conditions. Hence, a realistic representation of traffic demand patterns is an essential condition for a comprehensive evaluation of new concepts, which may deliver very different performance gains depending on the level of traffic density and complexity. This paper proposes a methodology for the identification of traffic patterns and the selection of representative traffic samples (representative days) for the assessment of a specific ATM performance problem.