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|Title:||Dynamic hot spot prediction by learning spatial-temporal utilization of taxiway intersections||Authors:||Ali, Hasnain
|Keywords:||Science::Mathematics::Probability theory||Issue Date:||2020||Source:||Ali, H., Delair, R., Pham, D.-T., Alam, S., & Schultz, M. (2020). Dynamic hot spot prediction by learning spatial-temporal utilization of taxiway intersections. Proceedings of International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT), 1-10. doi:10.1109/AIDA-AT48540.2020.9049186||Abstract:||Airports across the world are expanding by building multiple ground control towers and resorting to complex taxiway and runway system, in response to growing air trafﬁc. Current outcome- based ground safety management at the airside may impede our potential to learn from and adapt to evolving air trafﬁc scenarios, owing to the sparsity of accidents when compared with number of daily airside operations. To augment airside ground safety at Singapore Changi airport, in this study, we predict dynamic hot spots- areas where multiple aircraft may come in close vicinity on taxiways, as pre-cursor events to airside conﬂicts. We use airside infrastructure and A-SMGCS operations data of Changi airport to model aircraft arrival at different taxiway intersections both in temporal and spatial dimensions. The statistically learnt spatial-temporal model is then used to compute conﬂict probability at identiﬁed intersections, in order to evaluate conﬂict coefﬁcients or hotness values of hot spots. These hot spots are then visually displayed on the aerodrome diagram for heightened attention of ground ATCOs. In the Subjective opinion of Ground Movement Air Trafﬁc Controller, highlighted Hot Spots make sense and leads to better understanding of taxiway movements and increased situational awareness. Future research shall incorporate detailed human-in-the-loop validation of the dynamic hot spot model by ATCOs in 360 degree tower simulator.||URI:||https://hdl.handle.net/10356/146689||DOI:||10.1109/AIDA-AT48540.2020.9049186||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/AIDA-AT48540.2020.9049186||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ATMRI Conference Papers|
Updated on Jun 27, 2022
Updated on Jun 27, 2022
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