Implementing and Validating Air Passenger–Centric Metrics Using Mobile Phone Data

Transportation Research Interdisciplinary Perspectives

Féron

et al. 2020

Self Cite

The COVID-19 pandemic has had a significant impact on the air transportation system worldwide. This paper aims at analyzing the effect of the travel restriction measures implemented during the COVID-19 pandemic from a passenger perspective on the US air transportation system. Four metrics based on data generated by passengers and airlines on social media are proposed to measure how the travel restriction measures impacted the relation between passengers and airlines in close to real-time. The proposed metrics indicate that each airline has reacted differently to the COVID-19 travel restriction measures from a passenger perspective, therefore they can be used by airlines and passengers to improve their decision making process. This report comes ahead of official data related to the same sequence of events, thereby showing the value of passenger-borne data in an industry where corporate priorities, institutional prudence, and passenger satisfaction come close together.

Section: Motivationmentioning

confidence: 99%

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

Transportation Research Interdisciplinary Perspectives

Féron

et al. 2020

Self Cite

“…It can be split into two component: a processing time t sec necessary to get through security and through the airport to the desired gate and an extra wait time t wait due to flight delays. The processing times are based on the average wait times at airports presented in the study of [12]. They are summarized in the following table: The extra wait times are based on the data published by the Bureau of Transportation Statistics (BTS) [20] and were calculated only for departure.…”

Section: B Dwell Time At Airportsmentioning

confidence: 99%

“…Larger scale studies with a focus on air transportation was later possible thanks to the increasing use of mobile phone devices as datasources. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Delahaye

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-At)

et al. 2020

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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.

“…In this section, the method of passenger selection validated in [27] is implemented and analyzed for the time period covering the bomb cyclone.…”

Section: Bomb Cyclone From Mobile Location Datamentioning

confidence: 99%

“…These tools are implemented and tested a posteriori in the case of the bomb cyclone that hit the Northeast part of the United States in January 2018, causing the closure of Kennedy International Airport (JFK) and severe capacity decreases at Logan International Airport (BOS), Newark Liberty International Airport (EWR) and LaGuardia Airport (LGA). The passenger mobility perspective is based on a previous experimental proof [27] that mobile phone data enables the identification of domestic air passengers and supports the analysis of their behavior, under both nominal and degraded conditions. Combined with a passenger social media behavior perspective, these two analysis yield a better understanding of the impact of this bomb cyclone than the traditional flight-centric data coming from the BTS database.…”

Section: Introductionmentioning

confidence: 98%

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

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

Féron

2018

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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.