DATASET2050 presented at ACARE

Dataset-post

DATASET2050 participation in ACARE WG1 Meeting (Brussels, June 3, 2015)

Bauhaus Luftfahrt actively participated in the Advisory Council for Aviation Research and Innovation in Europe (ACARE) Working Group 1 Meeting on June 3, 2015 in Brussels on behalf of DATASET2050. ACARE’s main task is to provide a network for strategic research in aeronautics and air transport and to identifying strategic directions for aviation research and industry. The members of ACARE are organised in five working groups: (1) Mobility, (2) Competitiveness, (3) Environment and Energy, (4) Safety and Security, and (5) Resources. Within the different working groups experts from industry, science and politics meet regularly to discuss and define key topics and to monitor progress regarding mobility and air transportation related research. Three partners of DATASET2050 are part of ACARE: Bauhaus, EUROCONTROL and Innaxis. Working Group 1, in particular, is concerned with the societal and market needs and how these can be met in the future. Main objectives of the meeting in Brussels on June 3 were an update on the identification of bottlenecks within the current transport system and the respective formulation of research needs. Furthermore, three current projects within the scope of WG1 were presented: DATASET2050, DORA, and PASSME.

The DORA project starting in June 2015 (“Door to Door Information for Air Passengers”) focuses on passenger door-to-door travel and the provision of an integrated information system along the entire journey. The goal of the PASSME (“Personalised Airport Systems for Seamless Mobility and Experience”) project is to reduce passenger travel time by 60 minutes by introducing novel solutions (up to TRL6). These may include systems to provide predictive analysis of passenger flows or redesigned airport and aircraft processes that speed up the passenger journey and add to a hassle-free travel experience. Although these two projects have a different focus, both are concerned with enhancing the passenger journey and addressing door-to-door travel. This yields potential for valuable future exchange between the different partners and for the different work packages in DATASET2050.

The presentation of DATASET2050 at this particular meeting mainly focused on the work scope of work package 3, passenger demand profile. The intention of WP3 is to understand in a better way the current as well as the future transport system from the user perspective. For this purpose, travel behaviour is depicted by collecting a range of data on passenger needs and expectations for different time horizons. As depicted in step 1 in the figure, this demand is derived from a range of data sources: First, macro data, such as information on demographics or income, is collected to obtain a broad basis for the differentiation of population groups (as addressed in Blog Post #5). To introduce a transport-specific focus, passenger travel behaviour is analysed by looking at modal shares, traffic patterns and flows, e.g. travel times and destinations, as well as the availability and constitution of airport access modes. The characteristics derived from macro and transport-specific data are complemented by specific passenger attributes. High affinity for technology, for example, can be one feature that characterizes a certain type of passenger. Passengers might also place high emphasis on individualized products such as customized meals or entertainment programs during a flight.

21ag

Approach towards the derivation of distinct passenger clusters and requirements

The data is used to define specific demand profiles, i.e. to differentiate between a variety of passenger groups beyond the traditional classification of business, leisure, and visiting friends and relatives (step 2 in the figure). For example, one group may be characterized by travellers in the age group 20 to 25 with a high affinity for state-of-the-art technology solutions, travelling on a low budget to visit both friends and families across different European cities.

For each defined passenger demand profile (“passenger clusters”) requirements for the current as well as future transport system (2035 and 2050) are defined (step 3 in figure). A specification resulting from such a cluster can be the provision of aligned travel information along the entire journey. This means that all involved transport providers share information on schedules and expected delays to provide passengers with dynamic real-time information. These current and future requirements are then used to identify bottlenecks within passenger door-to-door transport and to design approaches how these can be addressed within future transport systems.

References:

ACARE: http://www.acare4europe.com/

PASSME: http://cordis.europa.eu/project/rcn/193396_en.html

DORA: http://cordis.europa.eu/project/rcn/193356_en.html

Innaxis brings together leading experts for Data Science in Aviation workshop

Complexworld-posts

Nearly 60 industry experts, academics and professionals from the fields of data science and aviation gathered in Madrid on October the 15th to attend the first ComplexWorld Network’s workshop, ‘Data Science in Aviation‘.  The workshop provided the opportunity for experts in the field to  discuss ways knowledge from aviation data could be extracted in order to enhance our understanding of the air transport system’s behaviour and the complex relation among its elements.

The workshop was motivated by the challenge of extracting ground breaking insights from the large quantities of data collected in the air transport network. The aviation sector gathers and stores a large amount of unstructured, heterogeneous data – safety data and reports, flight plans, navigation data, airport data, radar tracks – from multiple sources – airlines, ANSPs and airports. While the collection of information through different data sensors is growing exponentially, the application of data science to the data has not.  The workshop looked at how to capture the new opportunities offered by the data and close the large opportunity gap between the potential offered and the current outcomes of its analysis.

Innaxis as coordinator of the ComplexWorld Network, through which the workshop was supported, led the data science in aviation workshop initiative. Innaxis brought extensive IT expertise and experience in data-science analysis techniques to the workshop. Innaxis’s expertise in these areas has been developed through the various research programmes in which it works and through its exposure to different data science applications in a variety of fields.

The outcomes of the workshop will be made available shortly. It is our hope that these outcomes, which include new research ideas and discussions from this dynamic meeting of experts, will result in greater discussion and debate around the topic from the community as a whole. So please keep in touch and check back if you’d like to be involved in the ongoing development in this area.

Data Science and Complex Systems applied to Aviation – Innaxis Workshop

Businesses have entered a new era of decision-making and managing principles due to the pervasive availability of large amounts of data and the drastic growth, in the last decade, in the capacity to store and process data. Aviation is not an exception; Data Science principles have started to emerge through research programmes and practical applications in the field, albeit more slowly in some business functions than others.

Data Science, as a set of fundamental principles that support and guide the principled extraction of information and knowledge from data, leans on well-known data-mining techniques. However, it goes far beyond these techniques, with successful data-science paradigms that provide specific application guidelines. Data-driven decision making involves principles, processes and techniques for understanding phenomena via the automatic analysis of data.

A data-analytic thinking approach will help to envision opportunities for improving data-driven decision making in different contexts. There is strong evidence that aviation performance can be improved substantially via data-driven decision making and data-science techniques drawing on big data. Data-science will support data-driven decision making in the aviation field, where the underlying principles have yet to be established, in order to be able to realize its potential.

Innaxis participates in various research programmes and works on different applications in this field. We will be organizing a workshop on Data Science applied to Aviation in Madrid, Spain during October 2013. Please, write to us at innovation@innaxis.org if find this of interest and you would like to receive information on the workshop (please state “Data Science workshop” in the subject).

Connect with us!