Vol.:(0123456789) 1 3 CEAS Aeronautical Journal (2020) 11:345–354 https://doi.org/10.1007/s13272-019-00432-y ORIGINAL PAPER Analyzing door‑to‑door travel times through mobile phone data A case study of Spanish airports Pedro García‑Albertos 1  · Oliva G. Cantú Ros 1  · Ricardo Herranz 1 Received: 26 November 2018 / Revised: 13 September 2019 / Accepted: 21 November 2019 / Published online: 27 November 2019 © Deutsches Zentrum für Luft- und Raumfahrt e.V. 2019 Abstract A strategic objective of the European transport policy is the so-called 4-h door-to-door target, according to which, by 2050, 90% of travelers within Europe should be able to complete their journey, door-to-door, within 4 h. However, information on door-to-door travel times is scarce and difcult to obtain, which make it difcult to assess the level of accomplishment of this ambitious target. In this paper, we extract door-to-door travel times based on the analysis of opportunistically collected data generated by mobile phones. Anonymized mobile phone records are combined with data from the Google Maps Directions API to reconstruct the diferent legs of the trip and estimate the travel times for the door-to-kerb, kerb-to-gate, gate-to-gate, gate-to-kerb and kerb-to-door segments. The travel times of these legs have been extracted for diferent scenarios, all of them focusing on the Adolfo Suárez Madrid-Barajas airport, with the aim of exploring their infuence on total door-to-door travel time. Results show that the methodology presented is able to measure door-to-door travel times and that these travel times measured are far from the 4-h door-to-door target. We fnish by outlining future research directions. Keywords Big data · Air travel · Mobile phone records · Passenger behavior · Door-to-door mobility 1 Introduction The high-level vision for transport outlined in the European Commission’s 2011 White Paper on Transport [1] focuses on the need for an optimized multimodal travel system in which passengers are transported from door-to-door in a seamless, efcient and environmentally friendly way. In line with this, European aviation policy has the aim to ensure that air trans- port is smoothly integrated with the rest of the European transport network, taking passengers and their baggage from door-to-door predictably and efciently. The fnal goal is to provide an enhanced air transport experience and, at the same time, to improve the resilience of the air transport sys- tem against disruptive events [2]. However, this high-level vision has not always been taken into account in air transport in general, and Air Trafc Management (ATM) in particular: often performance objec- tives and decision-making processes lack this multimodal perspective, not always taking into account the ultimate impact on passengers. One of the reasons for this lack of passenger-centric perspective in the ATM system is the difculty to collect accurate, updated and reliable data on passenger behavior, needs, choices and sentiments. Tradi- tionally, these data have been collected through observa- tions and surveys, which provide rich travel and sociodemo- graphic information. However, surveys have some important drawbacks: they are expensive and time consuming; data acquisition needs to be planned in advance, which prevents the study of unpredicted events and they depend on users’ availability and willingness to answer the truth. The pervasive use of personal mobile devices in our daily lives (smartphones, apps, public transport smart cards, credit cards, etc.) opens new opportunities to collect a vast amount of passenger spatiotemporal data, which complement tra- ditional data acquisition methods. This paper explores the potential of mobile phone records and Google Maps API data to extract relevant insights about door-to-door mobil- ity. In particular, we present a methodology to detect the origin and the destination beyond the airport, measure the experienced travel times during each segment of the trip and extract airport accessibility indicators. * Pedro García-Albertos pedro.garcia@nommon.es 1 Nommon Solutions and Technologies, Madrid, Spain