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

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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 complex 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 publicationProceedings of the International Conference on Agents and Artificial Intelligence
Publication statusAccepted/In press - Dec 2019

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van Leeuwen, H., Zhang, Y., Zervanou, K., Mullick, S., Kaymak, U., & de Ruijter, T. (Accepted/In press). Lost and found: predicting airline baggage at-risk of being mishandled. In Proceedings of the International Conference on Agents and Artificial Intelligence