TY - GEN
T1 - BigARM: A Big-Data-Driven Airport Resource Management Engine and Application Tools
AU - Wong, Ka Ho
AU - Cao, Jiannong
AU - Yang, Yu
AU - Li, Wengen
AU - Wang, Jia
AU - Yao, Zhongyu
AU - Xu, Suyan
AU - Ku, Esther Ahn Chian
AU - Wong, Chun On
AU - Leung, David
N1 - Funding Information:
Acknowledgement. The work has been supported by the Innvoation and Technology Fund (ITP/024/18LP) and RGC General Research Fund (PolyU152199/17E). Thank Alan Lee, Patrick Yau, Gavin Lee, and Jiandong Li’s effort in this work.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Airport resource management
KW - Big data analytics
KW - Inbound baggage handling
KW - Load balance
UR - http://www.scopus.com/inward/record.url?scp=85092114932&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59419-0_48
DO - 10.1007/978-3-030-59419-0_48
M3 - Conference article published in proceeding or book
AN - SCOPUS:85092114932
SN - 9783030594183
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 741
EP - 744
BT - Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
A2 - Nah, Yunmook
A2 - Cui, Bin
A2 - Lee, Sang-Won
A2 - Yu, Jeffrey Xu
A2 - Moon, Yang-Sae
A2 - Whang, Steven Euijong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Y2 - 24 September 2020 through 27 September 2020
ER -