Deep reinforcement learning based airport departure metering
Airport taxi delays adversely affect airports and airlines around the world in terms of congestion, operational workload, and environmental emissions. Departure Metering (DM) is a promising approach to contain taxi delays by controlling departure pushback times. The key idea behind DM is to transfer...
Saved in:
Main Authors: | Ali, Hasnain, Pham, Duc Thinh, Alam, Sameer |
---|---|
Other Authors: | 24th IEEE International Conference on Intelligent Transportation - ITSC2021 |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/152013 |
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) -
Intelligent departure metering assistant tool (IDMAT) for airside congestion management
by: Ali, Hasnain, et al.
Published: (2023) -
Real-time departure slotting in mixed-mode operations using deep reinforcement learning : a case study of Zurich airport
by: Pham, Duc-Thinh, et al.
Published: (2021) -
Integrated airside landside framework to assess passenger missed connections with airport departure metering
by: Ali, Hasnain, et al.
Published: (2022) -
Reinforcement learning for two-aircraft conflict resolution in the presence of uncertainty
by: Pham, Duc-Thinh, et al.
Published: (2020)