Dynamic hot spot prediction by learning spatial-temporal utilization of taxiway intersections
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 tr...
Saved in:
Main Authors: | Ali, Hasnain, Delair, Raphael, Pham, Duc-Thinh, Alam, Sameer, Schultz, Michael |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/146689 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
A deep reinforcement learning approach for airport departure metering under spatial-temporal airside interactions
by: Ali, Hasnain, et al.
Published: (2022) -
Integrated airside landside framework to assess passenger missed connections with airport departure metering
by: Ali, Hasnain, et al.
Published: (2022) -
A multi-task learning approach for facilitating dynamic airspace sectorization
by: Zhou, Wei, et al.
Published: (2022) -
Spatial and temporal distribution of reported dengue cases and hot spot identification in Quezon City, Philippines, 2010–2017
by: Medina J.R.C.
Published: (2023) -
Spatial Data Mining: Clustering of Hot Spots and Pattern Recognition
by: Tay, S.C., et al.
Published: (2013)