A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area-Reference-Cited by-同舟云学术

A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

Published:2023-01 Issue:1 Volume:15 Page:285-299
ISSN:1939-1390
Container-title:IEEE Intelligent Transportation Systems Magazine
language:
Short-container-title:IEEE Intell. Transport. Syst. Mag.

Author:

Ma Yan¹^ORCID,Du Wenbo¹^ORCID,Chen Jun²^ORCID,Zhang Yu³^ORCID,Lv Yisheng⁴^ORCID,Cao Xianbin¹^ORCID

Affiliation:

1. Key Laboratory of Advanced Technology of Near-Space Information Systems, School of Electronic and Information Engineering, Beihang University, Beijing, China

2. School of Engineering and Materials Science, Queen Mary University of London, London, U.K.

3. Department of Civil and Environmental Engineering, University of South Florida, Tampa, Florida, USA

4. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/5117645/10015073/09670444.pdf?arnumber=9670444

Reference49 articles.

1. Target Tracking and Estimated Time of Arrival (ETA) Prediction for Arrival Aircraft

2. Modeling Time and Space Metering of Flights in the National Airspace System

3. Evaluating the Impact of Estimated Time of Arrival Accuracy on Interval Management Performance

4. Estimated Time of Arrival Prediction based on State-Dependent Transition Hybrid Estimation Algorithm

5. Airline Disruption Management: A Review of Models and Solution Methods

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Applying masked language model for transport mode choice behavior prediction;Transportation Research Part A: Policy and Practice;2024-06

2. A deep learning-based approach for predicting in-flight estimated time of arrival;The Journal of Supercomputing;2024-04-24

3. MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction;Transportation Research Part C: Emerging Technologies;2024-03

4. Prediction of Flight Delay through Intelligent Algorithms and Big Data Technology;2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2023-05-04

5. Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism;Transportation Research Part C: Emerging Technologies;2023-04