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

H. van Leeuwen; Yingqian Zhang; Kalliopi Zervanou; Shantanu Mullick; Uzay Kaymak; Tom de Ruijter

doi:10.5220/0008977801720181

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

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

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

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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 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 language	English
Title of host publication	ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Editors	Ana Rocha, Luc Steels, Jaap van den Herik
Publisher	SCITEPRESS-Science and Technology Publications, Lda.
Pages	172-181
Number of pages	10
ISBN (Electronic)	9789897583957
DOIs	https://doi.org/10.5220/0008977801720181
Publication status	Published - Feb 2020
Event	12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta Duration: 22 Feb 2020 → 24 Feb 2020

Conference

Conference	12th International Conference on Agents and Artificial Intelligence, ICAART 2020
Country/Territory	Malta
City	Valletta
Period	22/02/20 → 24/02/20

Keywords

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

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10.5220/0008977801720181

ICAART_2020_141_CR-2Final author version , 203 KB

Cite this

van Leeuwen, H., Zhang, Y., Zervanou, K., Mullick, S., Kaymak, U., & de Ruijter, T. (2020). Lost and found: predicting airline baggage at-risk of being mishandled. In A. Rocha, L. Steels, & J. van den Herik (Eds.), ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence (pp. 172-181). SCITEPRESS-Science and Technology Publications, Lda.. https://doi.org/10.5220/0008977801720181

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title = "Lost and found: predicting airline baggage at-risk of being mishandled",

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.",

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language = "English",

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booktitle = "ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence",

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note = "12th International Conference on Agents and Artificial Intelligence, ICAART 2020 ; Conference date: 22-02-2020 Through 24-02-2020",

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van Leeuwen, H, Zhang, Y, Zervanou, K, Mullick, S, Kaymak, U & de Ruijter, T 2020, Lost and found: predicting airline baggage at-risk of being mishandled. in A Rocha, L Steels & J van den Herik (eds), ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS-Science and Technology Publications, Lda., pp. 172-181, 12th International Conference on Agents and Artificial Intelligence, ICAART 2020, Valletta, Malta, 22/02/20. https://doi.org/10.5220/0008977801720181

Lost and found : predicting airline baggage at-risk of being mishandled. / van Leeuwen, H.; Zhang, Yingqian; Zervanou, Kalliopi et al.

ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence. ed. / Ana Rocha; Luc Steels; Jaap van den Herik. SCITEPRESS-Science and Technology Publications, Lda., 2020. p. 172-181.

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

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AU - de Ruijter, Tom

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AB - 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.

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van Leeuwen H, Zhang Y, Zervanou K, Mullick S, Kaymak U, de Ruijter T. Lost and found: predicting airline baggage at-risk of being mishandled. In Rocha A, Steels L, van den Herik J, editors, ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS-Science and Technology Publications, Lda. 2020. p. 172-181 https://doi.org/10.5220/0008977801720181

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

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