A Deep Reinforcement Learning Approach for Airport Departure Metering Under Spatial–Temporal Airside Interactions-Reference-Cited by-同舟云学术

A Deep Reinforcement Learning Approach for Airport Departure Metering Under Spatial–Temporal Airside Interactions

Published:2022-12 Issue:12 Volume:23 Page:23933-23950
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ali Hasnain¹^ORCID,Pham Duc-Thinh¹^ORCID,Alam Sameer¹^ORCID,Schultz Michael²^ORCID

Affiliation:

1. School of Mechanical and Aerospace Engineering, Air Traffic Management Research Institute, Nanyang Technological University, Jurong West, Singapore

2. Institute of Flight Systems, Bundeswehr University Munich, Neubiberg, Germany

Funder

National Research Foundation, Singapore

Civil Aviation Authority of Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/9972869/09906869.pdf?arnumber=9906869

Reference52 articles.

1. A Map-Matching Algorithm for Ground Movement Trajectory Representation using A-SMGCS Data

2. Analysing the effect of uncertainty in airport surface operations

3. Deep Reinforcement Learning Based Airport Departure Metering

4. A forward method for optimal stochastic nonlinear and adaptive control

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