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In this paper, we aim to address a relevant estimation problem that aviation professionals encounter in their daily operations. Specifically, aircraft load planners require information on the expected number of checked bags for a flight several hours prior to its scheduled departure to properly palletize and load the aircraft. However, the checked baggage prediction problem has not been sufficiently studied in the literature, particularly at the flight level. Existing prediction approaches have not properly accounted for the different impacts of overestimating and underestimating checked baggage volumes on airline operations. Therefore, we propose a custom loss function, in the form of a piecewise quadratic function, which aligns with airline operations practice and utilizes machine learning algorithms to optimize checked baggage predictions incorporating the new loss function. We consider multiple linear regression, LightGBM, and XGBoost, as supervised learning algorithms. We apply our proposed methods to baggage data from a major airline and additional data from various U.S. government agencies. We compare the performance of the three customized supervised learning algorithms. We find that the two gradient boosting methods (i.e., LightGBM and XGBoost) yield higher accuracy than the multiple linear regression; XGBoost outperforms LightGBM while LightGBM requires much less training time than XGBoost. We also investigate the performance of XGBoost on samples from different categories and provide insights for selecting an appropriate prediction algorithm to improve baggage prediction practices. Our modeling framework can be adapted to address other prediction challenges in aviation, such as predicting the number of standby passengers or no-shows.
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ZigZagCam: Pushing the Limits of Hand-held Millimeter-Wave Imaging
The ubiquity of millimeter-wave (mmWave) technology in 5G-and-beyond devices enable opportunities to bring through-obstruction imaging in hand-held, ad-hoc settings. This imaging technique will require manually scanning the scene to emulate a Synthetic Aperture Radar (SAR) [4] and measure back-scattered signals. Appropriate signal focusing can reveal hidden items and can be used to detect and classify shapes automatically. Such hidden object detection and classification could enable multiple applications, such as in-situ security check without pat-down search, baggage discrimination without opening the baggage, packaged inventory item counting without intrusions, etc.
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- Award ID(s):
- 1910853
- PAR ID:
- 10296781
- Date Published:
- Journal Name:
- Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
