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

119 Downloads (Pure)

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	Malta
City	Valletta
Period	22/02/20 → 24/02/20

Keywords

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

Access to Document

10.5220/0008977801720181

ICAART_2020_141_CR-2Final author version , 203 KB

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Lost and found: predicting airline baggage at-risk of being mishandled'. Together they form a unique fingerprint.

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

van Leeuwen, H. ; Zhang, Yingqian ; Zervanou, Kalliopi ; Mullick, Shantanu ; Kaymak, Uzay ; de Ruijter, Tom. / Lost and found : predicting airline baggage at-risk of being mishandled. ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence. editor / Ana Rocha ; Luc Steels ; Jaap van den Herik. SCITEPRESS-Science and Technology Publications, Lda., 2020. pp. 172-181

@inproceedings{52cb54441c9442ab96fa7db70de140c2,

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.",

keywords = "Baggage At-risk Prediction, Baggage Transfer Process Model, Gradient Boosting Machine",

author = "{van Leeuwen}, H. and Yingqian Zhang and Kalliopi Zervanou and Shantanu Mullick and Uzay Kaymak and {de Ruijter}, Tom",

year = "2020",

month = feb,

doi = "10.5220/0008977801720181",

language = "English",

pages = "172--181",

editor = "Ana Rocha and Luc Steels and {van den Herik}, Jaap",

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

publisher = "SCITEPRESS-Science and Technology Publications, Lda.",

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

}

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; Mullick, Shantanu; Kaymak, Uzay; de Ruijter, Tom.

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

TY - GEN

T1 - Lost and found

T2 - 12th International Conference on Agents and Artificial Intelligence, ICAART 2020

AU - van Leeuwen, H.

AU - Zhang, Yingqian

AU - Zervanou, Kalliopi

AU - Mullick, Shantanu

AU - Kaymak, Uzay

AU - de Ruijter, Tom

PY - 2020/2

Y1 - 2020/2

N2 - 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.

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.

KW - Baggage At-risk Prediction

KW - Baggage Transfer Process Model

KW - Gradient Boosting Machine

UR - http://www.scopus.com/inward/record.url?scp=85083102378&partnerID=8YFLogxK

U2 - 10.5220/0008977801720181

DO - 10.5220/0008977801720181

M3 - Conference contribution

AN - SCOPUS:85083102378

SP - 172

EP - 181

BT - ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence

A2 - Rocha, Ana

A2 - Steels, Luc

A2 - van den Herik, Jaap

PB - SCITEPRESS-Science and Technology Publications, Lda.

Y2 - 22 February 2020 through 24 February 2020

ER -

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