Resumen
Nowadays, in view of the growing traffic volume, an appropriate aircraft sequencing in the arrival sector is needed to maintain safety levels and improve the performance of the runway system and flight times. This paper presents a digital assistant supporting the air traffic controller in aircraft sequencing by providing suggestions for next waypoints, speed adjustments and altitude holdings. On the one hand, the suggested paths are such to preserve safety by ensuring the prescribed minimum separation, while also promoting environmental benefits through continuous descent operations (CDO). On the other hand, the suggestions aim to reduce landing times, improving the runway throughput. The proposed tool exploits multipath planning, for which a global optimization technique is used in conjunction with the dynamic time warping distance metric and a reinforcement learning approach to resolve conflicts through speed modulation and/or altitude holding. The performances of the assistant are assessed by means of a multi-agent simulator tailoring its reasoning on the procedures of Olbia airport (Italy). The analysis of a stream of many random aircraft has revealed its effectiveness in terms of arrival time reduction against a standard first-come-first-served strategy, usually adopted by controllers, and strong conflict reduction while considering a CDO-like adherence. Additionally, the man/machine interaction is investigated through an analysis of the overall latency from the suggestions provided by the digital assistant up to the actual aircraft maneuvers.