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

P. Monmousseau; A. Marzuoli; E. Feron; D. Delahaye

doi:10.1007/978-981-33-4669-7_11

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

P. Monmousseau^*, A. Marzuoli, E. Feron, D. Delahaye

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper aims at investigating further the use of the social media Twitter as a real-time estimator of the US Air Transportation system. Two different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested on the first two months of 2018 for the estimation of air traffic delays at departure and arrival at 34 different US airports. Using three different levels of content-related features created from the flow of social media posts led to the extraction of useful information about the current state of the air traffic system. The resulting methods yield higher estimation performances than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data for more than 28 airports. Moreover the features extracted can also be used to start a passenger-centric analysis of the Air Transportation system. This paper is the continuation of previous works focusing on estimating air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources as an estimator of the current state of the different actors of the air transportation system in real-time.

Original language	English (US)
Title of host publication	Air Traffic Management and Systems IV - Selected Papers of the 6th ENRI International Workshop on ATM/CNS, EIWAC 2019
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	189-205
Number of pages	17
ISBN (Print)	9789813346680
DOIs	https://doi.org/10.1007/978-981-33-4669-7_11
State	Published - 2021
Event	6th ENRI International Workshop on Air Traffic Management and communication, navigation and surveillance, ATM/CNS EIWAC2019 - Tokyo, Japan Duration: Oct 29 2019 → Oct 31 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	731 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th ENRI International Workshop on Air Traffic Management and communication, navigation and surveillance, ATM/CNS EIWAC2019
Country/Territory	Japan
City	Tokyo
Period	10/29/19 → 10/31/19

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.

Keywords

ATM performance measurement
Big data
Delay estimation
Machine learning

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-33-4669-7_11

Cite this

Monmousseau, P., Marzuoli, A., Feron, E., & Delahaye, D. (2021). Passengers on Social Media: A Real-Time Estimator of the State of the US Air Transportation System. In Air Traffic Management and Systems IV - Selected Papers of the 6th ENRI International Workshop on ATM/CNS, EIWAC 2019 (pp. 189-205). (Lecture Notes in Electrical Engineering; Vol. 731 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-4669-7_11

Monmousseau, P. ; Marzuoli, A. ; Feron, E. et al. / Passengers on Social Media : A Real-Time Estimator of the State of the US Air Transportation System. Air Traffic Management and Systems IV - Selected Papers of the 6th ENRI International Workshop on ATM/CNS, EIWAC 2019. Springer Science and Business Media Deutschland GmbH, 2021. pp. 189-205 (Lecture Notes in Electrical Engineering).

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

abstract = "This paper aims at investigating further the use of the social media Twitter as a real-time estimator of the US Air Transportation system. Two different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested on the first two months of 2018 for the estimation of air traffic delays at departure and arrival at 34 different US airports. Using three different levels of content-related features created from the flow of social media posts led to the extraction of useful information about the current state of the air traffic system. The resulting methods yield higher estimation performances than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data for more than 28 airports. Moreover the features extracted can also be used to start a passenger-centric analysis of the Air Transportation system. This paper is the continuation of previous works focusing on estimating air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources as an estimator of the current state of the different actors of the air transportation system in real-time.",

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Monmousseau, P, Marzuoli, A, Feron, E & Delahaye, D 2021, Passengers on Social Media: A Real-Time Estimator of the State of the US Air Transportation System. in Air Traffic Management and Systems IV - Selected Papers of the 6th ENRI International Workshop on ATM/CNS, EIWAC 2019. Lecture Notes in Electrical Engineering, vol. 731 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 189-205, 6th ENRI International Workshop on Air Traffic Management and communication, navigation and surveillance, ATM/CNS EIWAC2019, Tokyo, Japan, 10/29/19. https://doi.org/10.1007/978-981-33-4669-7_11

Passengers on Social Media: A Real-Time Estimator of the State of the US Air Transportation System. / Monmousseau, P.; Marzuoli, A.; Feron, E. et al.
Air Traffic Management and Systems IV - Selected Papers of the 6th ENRI International Workshop on ATM/CNS, EIWAC 2019. Springer Science and Business Media Deutschland GmbH, 2021. p. 189-205 (Lecture Notes in Electrical Engineering; Vol. 731 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper aims at investigating further the use of the social media Twitter as a real-time estimator of the US Air Transportation system. Two different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested on the first two months of 2018 for the estimation of air traffic delays at departure and arrival at 34 different US airports. Using three different levels of content-related features created from the flow of social media posts led to the extraction of useful information about the current state of the air traffic system. The resulting methods yield higher estimation performances than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data for more than 28 airports. Moreover the features extracted can also be used to start a passenger-centric analysis of the Air Transportation system. This paper is the continuation of previous works focusing on estimating air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources as an estimator of the current state of the different actors of the air transportation system in real-time.

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Monmousseau P, Marzuoli A, Feron E, Delahaye D. Passengers on Social Media: A Real-Time Estimator of the State of the US Air Transportation System. In Air Traffic Management and Systems IV - Selected Papers of the 6th ENRI International Workshop on ATM/CNS, EIWAC 2019. Springer Science and Business Media Deutschland GmbH. 2021. p. 189-205. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-33-4669-7_11