Door-to-door Air Travel Time Analysis in the United States using Uber Data

Philippe Monmousseau (1) , Daniel Delahaye (1) , Aude Marzuoli (2) , Eric Féron (2)

1 ENAC - Ecole Nationale de l'Aviation Civile
2 Georgia Institute of Technology [Atlanta]

NextGen and ACARE Flightpath 2050 set some ambitious goals for air travel, including improving the passenger travel experience using door-to-door travel times as a possible metric. Using recently released Uber data along with other online databases, a reliable estimation of door-to-door travel times is possible, which then enables a comparison of cities performance regarding the good integration of their airports as well as a per segment analysis of the full trip. This model can also be used to better evaluate where progress should and can be made with respect to air passenger travel experience.

Mots clés: Multi-modal travel; Air Transportation System; Big Data; Door-to-door travel times

Domaines: Intelligence artificielle [cs.AI]; Base de données [cs.DB]; Modélisation et simulation

Philippe Monmousseau, Daniel Delahaye, Aude Marzuoli, Eric Féron. Door-to-door Air Travel Time Analysis in the United States using Uber Data. AIDA-AT 2020, 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation, Feb 2020, Singapore, Singapore. ⟨10.1109/AIDA-AT48540.2020.9049179⟩. ⟨hal-02506640⟩