BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools

Ka Ho Wong; Jiannong Cao; Yu Yang; Wengen Li; Jia Wang; Zhongyu Yao; Suyan Xu; Esther Ahn Chian Ku; Chun On Wong; David Leung

doi:10.1007/978-3-030-59419-0_48

BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools

Ka Ho Wong, Jiannong Cao, Yu Yang^*, Wengen Li, Jia Wang, Zhongyu Yao, Suyan Xu, Esther Ahn Chian Ku, Chun On Wong, David Leung

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

Resource management becomes a critical issue in airport operation since passenger throughput grows rapidly but the fixed resources such as baggage carousels hardly increase. We propose a Big-data-driven Airport Resource Management (BigARM) engine and develop a suite of application tools for efficient resource utilization and achieving customer service excellence. Specifically, we apply BigARM to manage baggage carousels, which balances the overload carousels and reduces the planning and rescheduling workload for operators. With big data analytic techniques, BigARM accurately predicts the flight arrival time with features extracted from cross-domain data. Together with a multi-variable reinforcement learning allocation algorithm, BigARM makes intelligent allocation decisions for achieving baggage load balance. We demonstrate BigARM in generating full-day initial allocation plans and recommendations for the dynamic allocation adjustments and verify its effectiveness.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
Editors	Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	741-744
Number of pages	4
ISBN (Print)	9783030594183
DOIs	https://doi.org/10.1007/978-3-030-59419-0_48
Publication status	Published - 2020
Externally published	Yes
Event	25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 - Jeju, Korea, Republic of Duration: 24 Sept 2020 → 27 Sept 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12114 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Country/Territory	Korea, Republic of
City	Jeju
Period	24/09/20 → 27/09/20

Keywords

Airport resource management
Big data analytics
Inbound baggage handling
Load balance

Access to Document

10.1007/978-3-030-59419-0_48

Cite this

Wong, K. H., Cao, J., Yang, Y., Li, W., Wang, J., Yao, Z., Xu, S., Ku, E. A. C., Wong, C. O., & Leung, D. (2020). BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools. In Y. Nah, B. Cui, S.-W. Lee, J. X. Yu, Y.-S. Moon, & S. E. Whang (Eds.), Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings (pp. 741-744). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12114 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59419-0_48

Wong, Ka Ho ; Cao, Jiannong ; Yang, Yu et al. / BigARM : A Big-Data-Driven Airport Resource Management Engine and Application Tools. Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. editor / Yunmook Nah ; Bin Cui ; Sang-Won Lee ; Jeffrey Xu Yu ; Yang-Sae Moon ; Steven Euijong Whang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 741-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Resource management becomes a critical issue in airport operation since passenger throughput grows rapidly but the fixed resources such as baggage carousels hardly increase. We propose a Big-data-driven Airport Resource Management (BigARM) engine and develop a suite of application tools for efficient resource utilization and achieving customer service excellence. Specifically, we apply BigARM to manage baggage carousels, which balances the overload carousels and reduces the planning and rescheduling workload for operators. With big data analytic techniques, BigARM accurately predicts the flight arrival time with features extracted from cross-domain data. Together with a multi-variable reinforcement learning allocation algorithm, BigARM makes intelligent allocation decisions for achieving baggage load balance. We demonstrate BigARM in generating full-day initial allocation plans and recommendations for the dynamic allocation adjustments and verify its effectiveness.",

keywords = "Airport resource management, Big data analytics, Inbound baggage handling, Load balance",

author = "Wong, {Ka Ho} and Jiannong Cao and Yu Yang and Wengen Li and Jia Wang and Zhongyu Yao and Suyan Xu and Ku, {Esther Ahn Chian} and Wong, {Chun On} and David Leung",

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Wong, KH, Cao, J, Yang, Y, Li, W, Wang, J, Yao, Z, Xu, S, Ku, EAC, Wong, CO & Leung, D 2020, BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools. in Y Nah, B Cui, S-W Lee, JX Yu, Y-S Moon & SE Whang (eds), Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12114 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 741-744, 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020, Jeju, Korea, Republic of, 24/09/20. https://doi.org/10.1007/978-3-030-59419-0_48

BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools. / Wong, Ka Ho; Cao, Jiannong; Yang, Yu et al.
Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. ed. / Yunmook Nah; Bin Cui; Sang-Won Lee; Jeffrey Xu Yu; Yang-Sae Moon; Steven Euijong Whang. Springer Science and Business Media Deutschland GmbH, 2020. p. 741-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12114 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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Wong KH, Cao J, Yang Y, Li W, Wang J, Yao Z et al. BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools. In Nah Y, Cui B, Lee SW, Yu JX, Moon YS, Whang SE, editors, Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 741-744. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59419-0_48