Tutorials 2014

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ICRAT 2014-ComplexWorld- Tutorial Sessions

The 6th International Conference on Research in Air Transportation -ICRAT-, www.icrat.org was held the week of May 26-30, 2014 at the Istanbul Technical University in Istanbul, Turkey. ICRAT has now been established as a mainstream biennial event in Air Transport Research, alternating with the USA/Europe Air Traffic Management (ATM) Research and Development (R&D) Seminar. In this 2014 edition almost two hundreds of researchers and Air Transportation stakeholders attended ICRAT, which included several ComplexWorld activities: tutorials, key note speeches, and paper sessions on complexity&ATM.

Among them, we would like to highlight the remarkable interest arisen in the tutorial sessions brilliantly provided by Thomas Hauf (University of Hannover- Adverse weather and ATM performance) and Massimiliano Zanin (Innaxis) and Samuel Cristobal (Innaxis), on different areas within Data Science in Air Transportation. In fact it was the parallel session with the highest number of attendees, congrats!

Tutorial 1: How to apply data science in aviation: stationarity and metrics: Massimiliano Zanin and Samuel Cristobal (Innaxis)

Presentations: Massimiliano, Samuel, Recording of the tutorial

The reality is that the aviation sector gathers and stores a large amount of unstructured, heterogeneous data from different sources and of a diverse nature: safety data and reports, flight plans, navigation data, weather, airport data, radar tracks, etc. From airlines to ANSPs or airports, the ability to collect information through different data sensors is growing exponentially. Nevertheless, how the different stakeholders take advantage of these data has not evolved so rapidly and there is still a large gap for improvement.

In this talk, MZ reviewed a topics of utmost importance for the correct application of data analysis to air transport: stationarity.

Stationarity is the property of a system having coherent characteristics in time and space, being the latter both the physical space (i.e. the position of airports through the world) and the virtual space created by the parameters of the system. When stationarity cannot be guaranteed, the results obtained can be plagued with errors and inconsistencies. For instances, when analysing the time series representing some observables, causal relations may appear: yet they may just be the result of some constant trend, and not of a real cause-effect mechanism. This is especially relevant when trying to forecast the future behaviour of the system by means of historical data: relationships between the past and the future are an essential ingredient, such that the future cannot be forecasted from the past if the system changes its structural characteristics (i.e. if there is a non-stationarity in the parameters' space).

In the talk, the concept of stationarity has been reviewed through different simple exampled drawn from actual air transport problems; finally, a set of possible solutions has been discussed, including the use of detrending techniques.

The second topic covered metrics, with focus on representativeness and significance. We study how different metrics can be misleading when interpreting the results of an study and how providing a simple answer is not always possible. Practical examples from ongoing research projects will be provided, namely: passenger-centered metrics, delay propagation metrics and resilience metrics. Representativeness and significance of the metrics will also be discussed in the tutorial.

Tutorial 2: "Adverse Weather and ATM performance", Thomas Hauf

Presentation here

The various atmospheric aviation hazards such as icing, thunderstorms, lightning, wake vortices, turbulence,fog, reduced visibility, snow and ice at the ground etc. will be presented and their impact on aircraft safety and efficiency will be discussed. Delay statistics and accident and incident analysis supplement the discussion.Methodological problems for the latter will be addressed.The relation between safety, efficiency and punctuality will be highlighted. Methods to mitigate the impact will be presented. Mitigation strategies include increased situation awareness, eg by more weather information in the cockpit, specific adverse weather nowcasts and forecasts, weather expert systems, multi-observational and modeling systems such as ITWS, and also weather avoidance modeling.Basic linear versus nonlinear weather effects on ATM performance will be discussed.

Speakers short CV:

Massimiliano Zanin Researcher at Innaxis, graduated in Aeronautical Management at the Universidad Autónoma de Madrid. With more than 80 published peer-reviewed contributions in international conferences and journals, he has vast experience in complex systems research, both theoretical and applied, and in collaborating with scientists from all over the world. His main topics of interest are Complex Networks, Data Science and their application to several real-world problems, as with modelling and understanding the ATM system or mining complex data sets.

Samuel Cristóbal Samuel is currently a researcher at Innaxis, focused on stochastic modelling, as well as a PhD candidate at the Universität Wien. He holds a MSc in Advanced Mathematics and Applications (Universidad Autónoma of Madrid); a BCs (with honors) in Mathematics (Universidad Complutense de Madrid); and a BEng in Telecommunication Systems (Universidad Politécnica de Madrid). Samuel has a strong mathematical background and, as a researcher, he has vast experience in mathematical models, data management, simulation programming, and applied software. For the last five years, he has been conceiving mathematical models within several ETCL, SESAR, FP6 and FP7 funded projects.

Thomas Hauf Worked at the German Aerospace and Research Agency for 15 years in airborne turbulence and cloud measurements and also in cloud modeling. Since 1998 professor for meteorology at the Leibniz University Hannover. Research fields are traffic meteorology, shower modeling, aircraft icing, weather impact on aviation and weather avoidance modeling.

This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 783287.