Lost and found: Predicting airline baggage at-risk of being mishandled

Herbert van Leeuwen, Yingqian Zhang, Kalliopi Zervanou, Shantanu Mullick, Uzay Kaymak, Tom de Ruijter

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The number of bags mishandled while transferring to a connecting flight is high. Bags at-risk of missing their connections can be processed faster; however, identifying such bags at-risk is still done by simple business rules. This work researches a general model of baggage transfer process and proposes a Gradient Boosting Machine based prediction model for identifying the bags at-risk. Our prediction model is compared to the current rule based method and a benchmark using logistic regression. The results show that our model offers an increase in accuracy coupled with a marked increase in precision and recall when identifying bags that are transferred unsuccessfully.

Original languageEnglish
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages172-181
Number of pages10
ISBN (Electronic)9789897583957
DOIs
Publication statusPublished - 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: 22 Feb 202024 Feb 2020

Publication series

NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
Country/TerritoryMalta
CityValletta
Period22/02/2024/02/20

Keywords

  • Baggage At-risk Prediction
  • Baggage Transfer Process Model
  • Gradient Boosting Machine

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