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

Cristian Augustin Susanu, Claudia Raibulet, Ilias Gerostathopoulos

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publication2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
Subtitle of host publication[Porceedings]
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages239-244
Number of pages6
ISBN (Electronic)9798331502157
DOIs
Publication statusPublished - 2025
Event6th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2025 - Tokyo, Japan
Duration: 29 Sept 20253 Oct 2025

Conference

Conference6th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2025
Country/TerritoryJapan
CityTokyo
Period29/09/253/10/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

  • AEROGRAM
  • Air Traffic Management
  • aviation safety
  • GMM
  • LSTM
  • machine learning
  • Schiphol Airport
  • selfadaptive systems
  • trajectory optimization

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