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Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification

Published in ICANN (2). Hrsg. von Alessandra Lintas u. a. Bd. 10614. Lecture Notes in Computer Science. Springer, 2017

The performance analysis of complex systems like Air Traffic Management (ATM) is a challenging task. To overcome statistical complexities through analysing non-linear time series we approach the problem with machine learning methods. Therefore we understand ATM (and its identified system model) as a system of coupled and interdependent sub-systems working in time-continous processes, measurable through time-discrete time series.

Recommended citation: S. Reitmann und K. Nachtigall. “Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification” In: ICANN (2). Hrsg. von Alessandra Lintas u. a. Bd. 10614. Lecture Notes in Computer Science. Springer, 2017, S. 528–536. ISBN: 978-3-319-68612-7. https://doi.org/10.1007/978-3-319-68612-7_60

Computation of Air Traffic Flow Management Performance with Long Short-Term Memories Considering Weather Impact

Published in ICANN 2018. Bd. 11140. Lecture Notes in Computer Science. Springer, 2018

In this paper we compute the impact of weather events to airport performance, which is measured as deviation of actual and scheduled timestamps (delay). Weather phenomena are categorized by the Air Traffic Management Airport Performance weather algorithm, which aims to quantify weather conditions at European airports. A comprehensive dataset of flights of 2013 for example airport Hamburg and accompanied weather data result in both a quantification of the individual airport performance and an aggregated weather-performance metric.

Recommended citation: S. Reitmann und M. Schultz. “Computation of Air Traffic Flow Management Performance with Long Short-Term Memories Considering Weather Impact”. In: Artificial Neural Networks and Machine Learning – ICANN 2018. Bd. 11140. Lecture Notes in Computer Science. Springer, 2018, S. 532–541. ISBN: 978-3-030-01421-6. DOI: 10.1007/978-3-030-01421-6_51. http://dx.doi.org/10.1007/978-3-030-01421-6_51

Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Tim

Published in Aerospace 2018, 5(4), 101, 2018

In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric.

Recommended citation: M. Schultz und S. Reitmann. “Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time”. In: Aerospace 5.4 (Sep. 2018), S. 101. https://doi.org/10.3390/aerospace5040101

Machine learning approach to predict aircraft boarding

Published in Transportation Research Part C: Emerging Technologies, Volume 98, January 2019, Pages 391-408, 2019

Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. We used a recurrent neural network approach to predict the progress of a running boarding event. In particular, we implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, we used a reliable, validated boarding simulation environment to provide data about the aircraft boarding events. First predictions show that uni-variate input (seat load progress) produces insufficient results, so we consider expected passenger interactions in the aircraft cabin as well. These interactions are aggregated to a prior-developed complexity metric and allow an efficient evaluation of the current boarding progress. With this multi-variate input, our Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.

Recommended citation: M. Schultz und S. Reitmann. “Machine learning approach to predict aircraft boarding”. In: Journal of Transportation Research Part C: Emerging Technologies (2018). https://doi.org/10.1016/j.trc.2018.09.007

talks

Multivariate Time Series Prediction with Long Short-Term Memory (LSTM)

Published: October 14, 2016

S. Reitmann und K. Nachtigall. “Multivariate Time Series Prediction with Long Short-Term Memory (LSTM)”. In: International Science & Progress Conference. St. Petersburg, Russland, 2016.

Performance Benchmarking in Interdependent ATM Systems

Published: June 21, 2017

S. Reitmann, A. Gillissen und M. Schultz. “Performance Benchmarking in Interdependent ATM Systems”. In: International Conference on Research in Air Transportation (ICRAT). Philadelphia, USA, 2016.

Ableitung eines mathematischen Wirkungsmodells durch systematische Analyse von Leistungsindikatoren

Published: September 07, 2017

S. Reitmann. “Ableitung eines mathematischen Wirkungsmodells durch systematische Analyse von Leistungsindikatoren”. In: Deutscher Luft- und Raumfahrtkongress (DLRK). München, Deutschland, 2017.

Stability Analysis of Recurrent Neural Networks for Time Series based System Control in Aviation

Published: November 14, 2017

S. Reitmann und M. Schultz. “Stability Analysis of Recurrent Neural Networks for Time Series based System Control in Aviation”. In: International Science & Progress Conference. St. Petersburg, Russland, 2017.

Recurrent Neural Network based Aircraft Boarding Prediction

Published: July 03, 2018

S. Reitmann und M. Schultz. “Recurrent Neural Network based Aircraft Boarding Prediction”. In: Air Transport Research Society (ATRS). Seoul, Süd Korea, 2018.

Real-Time Prediction of Aircraft Boarding

Published: September 27, 2018

S. Reitmann und M. Schultz. “Real-Time Prediction of Aircraft Boarding”. In: Digital Avionics Systems Conference (DASC). London, UK, 2018.

Machine Learning in modern Data-Based Air Traffic Management

Published: May 16, 2019

Advanced Quantification of Weather Impact on Air Traffic Management - Intelligent Weather Categorization with Machine Learning

Published: June 19, 2019

S. Reitmann und M. Schultz. “Advanced Quantification of Weather Impact on Air Traffic Management - Intelligent Weather Categorization with Machine Learning”. In: 13th USA/Europe ATM Research and Development Seminar (ATM2019). Vienna, Austria, 2019.

Stefan Reitmann