A methodology for identifying similar days in air traffic flow management initiative planning-Reference-Cited by-同舟云学术

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A methodology for identifying similar days in air traffic flow management initiative planning

Published:2016-08 Issue: Volume:69 Page:1-15
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Kuhn Kenneth D.

Funder

NASA NextGen

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference23 articles.

1. Ground delay program analytics with behavioral cloning and inverse reinforcement learning;Bloem,2014

2. Generating probabilistic capacity profiles from weather forecast: a design-of-experiment approach;Buxi,2011

3. Environmental impact of air traffic flow management delays;Carlier,2007

4. Flight delay propagation impact on strategic air traffic flow management;Churchill;Transport. Res. Rec.,2010

5. Architecture and capabilities of a data warehouse for ATM research;Eshow,2014

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1. Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM);Neural Computing and Applications;2024-05-07

2. Recent progress in air traffic flow management: A review;Journal of Air Transport Management;2024-04

3. Prediction of Hourly Airport Operational Throughput with a Multi-Branch Convolutional Neural Network;Aerospace;2024-01-15

4. Machine learning in aviation: Is it possible to predict when an ATC Sector or Air Traffic Flow will be regulated?;Journal of Physics: Conference Series;2023-06-01

5. A Methodology for Predicting Ground Delay Program Incidence through Machine Learning;Sustainability;2023-04-19

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