Big data analytics for a passenger-centric air traffic management system: A case study of door-to-door intermodal passenger journey inferred from mobile phone data

Pedro García, Ricardo Herranz, José Javier Ramasco, Gennady Andrienko, Nicole Adler, Carla Ciruelos

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

BigData4ATM is a SESAR 2020 Exploratory Research project that investigates how new sources of passenger-centric data coming from smart personal devices can be analysed to extract relevant information about passengers’ behaviour. In this paper, we introduce the project and present a case study focused on the analysis of door-to-door passenger journeys from mobile phone data. Anonymised call detail records (CDRs) for several million users in Spain are used to infer door-to-door trips for the Madrid-Barcelona corridor and identify the long-distance transportation mode (air, rail, road) chosen by each user. These door-to-door itineraries are upscaled to the total population using demographic data and used to estimate modal split, airport catchment areas and door-to-door travel times. Estimated modal shares are compared to official statistics to validate the results. We finish by outlining future research directions and discussing how the information extracted from mobile phone records can be exploited to inform decision making in air transport and ATM.

Original languageAmerican English
JournalSESAR Innovation Days
StatePublished - 2016
Event6th SESAR Innovation Days: Inspiring Long-Term Research in the Field of Air Traffic Management, SIDs 2016 - Delft, Netherlands
Duration: 8 Nov 201610 Nov 2016

Bibliographical note

Publisher Copyright:
© 2016, SESAR Joint Undertaking. All rights reserved.

Keywords

  • Big data
  • Data analytics
  • Door-to-door mobility
  • Mobile phone records
  • Passenger behaviour

Fingerprint

Dive into the research topics of 'Big data analytics for a passenger-centric air traffic management system: A case study of door-to-door intermodal passenger journey inferred from mobile phone data'. Together they form a unique fingerprint.

Cite this