Ricardo Herranz

Mayte Cano, Andrés Perillo, Juan Antonio López et al.

This White Paper sets out to explain the value that metamodelling can bring to air traffic management (ATM) research. It will define metamodelling and explore what it can, and cannot, do. The reader is assumed to have basic knowledge of SESAR: the Single European Sky ATM Research project. An important element of SESAR, as the technological pillar of the Single European Sky initiative, is to bring about improvements, as measured through specific key performance indicators (KPIs), and as implemented by a series of so-called SESAR 'Solutions'. These 'Solutions' are new or improved operational procedures or technologies, designed to meet operational and performance improvements described in the European ATM Master Plan.

Rodrigo Marcos, Oliva García-Cantú, Ricardo Herranz

Air Traffic Flow and Capacity Management (ATFCM) is one of the constituent parts of Air Traffic Management (ATM). The goal of ATFCM is to make airport and airspace capacity meet traffic demand and, when capacity opportunities are exhausted, optimise traffic flows to meet the available capacity. One of the key enablers of ATFCM is the accurate estimation of future traffic demand. The available information (schedules, flight plans, etc.) and its associated level of uncertainty differ across the different ATFCM planning phases, leading to qualitative differences between the types of forecasting that are feasible at each time horizon. While abundant research has been conducted on tactical trajectory prediction (i.e., during the day of operations), trajectory prediction in the pre-tactical phase, when few or no flight plans are available, has received much less attention. As a consequence, the methods currently in use for pre-tactical traffic forecast are still rather rudimentary, often resulting in suboptimal ATFCM decision making. This paper proposes a machine learning approach for the prediction of airlines route choices between two airports as a function of route characteristics, such as flight efficiency, air navigation charges and expected level of congestion. Different predictive models based on multinomial logistic regression and decision trees are formulated and calibrated with historical traffic data, and a critical evaluation of each model is conducted. We analyse the predictive power of each model in terms of its ability to forecast traffic volumes at the level of charging zones, proving significant potential to enhance pre-tactical traffic forecast. We conclude by discussing the limitations and room for improvement of the proposed approach, as well as the future developments required to produce reliable traffic forecasts at a higher spatial and temporal resolution.