- Project ID BigData4ATM
- Project duration 2016-05-09 > 2018-05-18
- Total EUR 599 732,50
- EU Contr. EUR 599 732,50
- Status Closed
Seeing the bigger picture
Air transport performance objectives and decision-making processes have often overlooked the passenger perspective, mainly due to the difficulties to collect accurate, updated and reliable data on passenger needs and behaviour. The BigData4ATM project investigated how new data sources coming from smart personal devices can be used to overcome this lack of information. The project objectives were to:
- Explore and characterise new emerging data sources potentially useful for ATM socioeconomic studies.
- Develop methodologies that integrate and analyse multiple sources of data to extract passengers’ behavioural patterns.
- Develop theoretical models translating these behavioural patterns into relevant and actionable indicators.
- Evaluate the potential applications of the new data sources through a number of relevant case studies.
The project successfully developed new approaches to characterise airport catchment areas, analyse the door-to-door passenger journey, and assess the impact of ATM disruptions on passenger behaviour. The insights gained from this information are expected to result in better integration of air transport with other transport modes, as well as in a more efficient coordination of airport landside and airside operations.
The project results show that new, unconventional data coming from smart personal devices open unprecedented opportunities for building a truly passenger-centric air transport system. Next steps will involve the application of the concepts and methodologies developed in BigData4ATM in one or more specific airports, which would work as a test environment to evaluate the benefits and practical implementation issues of the newly proposed solutions.
Richer information on passenger behaviour
Characterisation of door-to-door passenger journey
Improved air traffic forecasts
Better integration of air transport with other transport modes
Enhanced coordination of airport landside
More efficient use of airport resources
Project Members: Agencia estatal consejo superior de investigaciones scientificas (CSIC), Ingenieria de Sistemas para la Defensa de Espana (ISDEFE), Nommon Solutions and Technologies (Coordinator), The Hebrew University of Jerusalem, Universitat de les illes Balears, Fraunhofer Gesellschaft.
Final Project Results Report - BigData4ATM
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 699260