• Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM
  • Consortium: Nommon (Project Coordinator), IFISC, Fraunhofer-IAIS, the Hebrew University of Jerusalem, and ISDEFE.
  • 2016 – 2018

The project

BigData4ATM (Passenger-centric Big Data Sources for Socio-economic and Behavioural Research in ATM) is a research project within SESAR 2020 Exploratory Research which aims to investigate how new sources of passenger-centric data coming from smart personal devices can be analysed to extract relevant information about passengers’ behaviour and how this information can be used to inform ATM decision making processes.

The project is conducted by a consortium composed by Nommon (Project Coordinator), a research-intensive company specialised in the analysis of spatio-temporal data, two world-leading research centres in the fields of complexity science and data science, IFISC and Fraunhofer-IAIS, one top university in the area of transport economics, the Hebrew University of Jerusalem, and ISDEFE, a consulting company with a broad knowledge of the ATM sector.


The paramount goal of the European transport policy, as defined in the European Commission’s 2011 White Paper on Transport, is to “establish a system that underpins European economic progress, enhances competitiveness and offers high quality mobility services while using resources more efficiently”. In line with these objectives, the long-term vision for the European aviation sector outlined in the report ‘Flightpath 2050 – Europe’s Vision for Aviation’ envisages a passenger-centric air transport system thoroughly integrated with other transport modes, with the ultimate goal of taking travellers and their baggage from door to door predictably and efficiently while enhancing passenger experience and rendering the transport system more resilient against disruptive events.

In contrast with this high-level vision, ATM operations have so far lacked a passenger-oriented perspective, with performance objectives and decision criteria (e.g., flight prioritisation rules) not necessarily taking into account the ultimate consequences for the passenger. Further research is needed to provide new insights on the interactions between the ATM system and passengers’ needs, choices and behaviour. However, current methods used to collect data on passengers’ activities are limited in accuracy and validity: traditional methods based on observations and surveys present intrinsic limitations (e.g., incorrect and imprecise answers, dependence on the availability and willingness to answer of the interviewed persons, etc.), and they are also expensive and time-consuming; useful data can also be collected from other sources such as air traffic databases, travel reservation systems or market intelligence data services, but these data typically fail to capture important information, such as door-to-door origin-destination pairs and travel times. The generalised use of geolocated devices in our daily activities opens new opportunities to collect rich data and overcome many of the limitations of traditional methods. The very same ICT tools that are enabling new forms of bidirectional communication with the passenger are also making it possible to gather permanently updated information on passengers’ activity and mobility patterns, with an unprecedented level of detail.

BigData4ATM’s goals

The goal of BigData4ATM is to investigate how different passenger-centric geolocated data can be analysed and combined with more traditional demographic, economic and air transport databases to extract relevant information about passengers’ behaviour, and to study how this information can be used to inform ATM decision making processes. The specific objectives of the project are the following:

  1. To develop a set of methodologies and algorithms to acquire, integrate and analyse multiple distributed sources of non-conventional ICT-based spatio-temporal data — including mobile phone records, data from indoor geolocation technologies, credit card records and data from Internet social networks, among others — with the aim of characterising passengers’ behavioural patterns;
  2. To develop new theoretical models translating these behavioural patterns into relevant and actionable indicators for the planning and management of the ATM system;
  3. To evaluate the potential applications of the new data sources, data analytics techniques and theoretical models through a number of case studies relevant for the European ATM system, including the development of passenger-centric door-to-door delay metrics, the improvement of air traffic forecasting models, the analysis of intra-airport passenger behaviour and its impact on ATM, and the assessment of the socio-economic impact of ATM disruptions.

To find more about the project, download the ​​​BigData4ATM Position Paper​.

Public deliverables

Scientific papers

This project has received funding from the SESAR JU under grant agreement Nº 699260 under European Union’s Horizon 2020 research and innovation programme. © 2016 BigData4ATM Consortium.