The accelerating growth of global air traffic is widening the gap between traditional ATM tools and the realtime, data-intensive decisions modern control towers must make. We introduce AEROGRAM11AEROGRAM is available as open-source software here.22A Demo video can be found here., an open-source artefact that merges a combination of LSTM and GMM capacity predictor with a dashboard-driven MAPE-K adaption loop. Developed and calibrated for Amsterdam Schiphol Airport, AEROGRAM continuously ingests live ADS-B, A-SMGCS and METAR feeds, evaluates three interchangeable strategies (rule-based baseline, pattern-based GMM, deep LSTM) and surfaces rerouting advice, delay forecasts and uncertainty thresholds in an interactive GUI. Experimental results on Schiphol traffic scenarios show that the LSTM based adaptive strategy cuts average delay by 33 % and sustains 85-90% efficiency during peak hours, while the GMM alternative delivers moderate gains with half the compute footprint and the baseline remains lightweight but least effective.

Susanu, C., Raibulet, C., Gerostathopoulos, I. (2025). AEROGRAM: Adaptive Environment & Rerouting Optimiser with GMM-Augmented LSTM Airspace Model. In 2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (pp.239-244). Institute of Electrical and Electronics Engineers Inc. [10.1109/ACSOS-C66519.2025.00068].

AEROGRAM: Adaptive Environment & Rerouting Optimiser with GMM-Augmented LSTM Airspace Model

Raibulet C.
Co-primo
;
2025

Abstract

The accelerating growth of global air traffic is widening the gap between traditional ATM tools and the realtime, data-intensive decisions modern control towers must make. We introduce AEROGRAM11AEROGRAM is available as open-source software here.22A Demo video can be found here., an open-source artefact that merges a combination of LSTM and GMM capacity predictor with a dashboard-driven MAPE-K adaption loop. Developed and calibrated for Amsterdam Schiphol Airport, AEROGRAM continuously ingests live ADS-B, A-SMGCS and METAR feeds, evaluates three interchangeable strategies (rule-based baseline, pattern-based GMM, deep LSTM) and surfaces rerouting advice, delay forecasts and uncertainty thresholds in an interactive GUI. Experimental results on Schiphol traffic scenarios show that the LSTM based adaptive strategy cuts average delay by 33 % and sustains 85-90% efficiency during peak hours, while the GMM alternative delivers moderate gains with half the compute footprint and the baseline remains lightweight but least effective.
paper
AEROGRAM; Air Traffic Management; aviation safety; GMM; LSTM; machine learning; Schiphol Airport; selfadaptive systems; trajectory optimization;
English
6th IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2025 - 29 September 2025 - 03 October 2025
2025
2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
9798331502157
2025
239
244
open
Susanu, C., Raibulet, C., Gerostathopoulos, I. (2025). AEROGRAM: Adaptive Environment & Rerouting Optimiser with GMM-Augmented LSTM Airspace Model. In 2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (pp.239-244). Institute of Electrical and Electronics Engineers Inc. [10.1109/ACSOS-C66519.2025.00068].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/588551
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