Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91948
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Title: BigARM : a big-data-driven airport resource management engine and application tools
Authors: Wong, KH 
Cao, J 
Yang, Y 
Li, W 
Wang, J 
Yao, Z 
Xu, S 
Ku, EAC 
Wong, CO
Leung, D
Issue Date: 2020
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2020, v. 12114, p. 741-744
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
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-030-59419-0_48
Description: International Conference on Database Systems for Advanced Applications, DASFAA 2020, Jeju, Korea (Republic of), 24-27 September 2020
Rights: © Springer Nature Switzerland AG 2020
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-59419-0_48
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