  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.
