Passenger-Centric Metrics for Air Transportation Leveraging Mobile Phone and Twitter Data

Abstract

This paper aims at presenting a detailed analysis of domestic air passengers behavior during a major airtraffic disturbance, from two complementary passenger-centric perspective: a passenger mobility perspective and a passenger social media perspective. By leveraging over 5 billion records of mobile phone location data per day from a major carrier in the United States, passenger mobility can be reliably analyzed, no matter which airline the passengers fly on or which airport they fly to and from. Such information is currently unavailable to the major aviation stakeholders at such scale and can be used to establish performance benchmarks from a passenger's perspective. Combining it with a Twitter analysis provides a more detailed and passenger-focused analysis than the traditional flight-centric measurements used to evaluate the overall system performance. More generally, these two passenger-centric analysis could be implemented in real-time for a daily evaluation of the Air Transportation System, enabling a faster analysis of the impact of major disruptions, whether due to meteorological conditions or system failures.

Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep

“…Data from WiFi hotspots and Bluetooth beacons, along with historical data, are used to analyze passenger behavior at airports ( Nikoue et al, 2015 ; Huang et al, 2019 ) and at transit stations ( Van den Heuvel et al, 2016 ). If available, data generated by passengers smartphone and collected by phone carriers can be processed to analyze the door-to-door behavior of passengers ( Marzuoli et al, 2019 , Marzuoli et al, 2018 ; García-Albertos et al, 2017 ), both under nominal and degraded conditions. However data gathered directly from smartphones are proprietary data and are not often publicly available for research.…”

Passengers on Social Media: A Real-Time Estimator of the State of the US Air Transportation System

“…A first step in topic analysis is to clean the documents analyzed, here the tweets. This cleaning process was already performed in [13] and [21] and consists of the following steps: Any reference to websites or pictures was replaced by a corresponding keyword. Every mention to another Twitter user within a tweet (@someone) as well as most emojis were similarly replaced.…”

“…Precursor work was made by Marzuoli et al in [12] and [13] using mobile phone data in order to analyze the performances of airports from the passengers' perspective. These studies validated the use of passengercentric data to better assess the overall health of the Air Transportation System.…”

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

“…This visualisation method can also be used to study the evolution of the reach of a city over time, which can be useful for analyzing the effect and reach of natural disasters. The same severe weather perturbation as in [13] was investigated here. On January 4th 2018, a winter storm nicknamed "Bomb Cyclone" hit the East Coast of the United States leading to the closure of some of the main Northeastern airports and the cancellation of a majority of flights servicing the area.…”

“…In the United States, Marzuoli et al [12] presented a method to detect domestic air passengers on a nationwide scale using mobile phone data, enabling a per leg analysis of the full door-to-door trip though the main focus was on passengers' behavior at airports. The passengers' experience in airports under major perturbations using this method and additional data from social media was further studied in [13]. In Europe, within the BigData4ATM project 1 , García-Albertos et al [14] presented a methodology for measuring the door-to-door travel time using mobile phone data and applied it between two Spanish cities, Madrid and Barcelona.…”