Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Air Traffic Management (ATM) for the Sustainability and Environmental Performance...
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Air Traffic Management (ATM) for the Sustainability and Environmental Performance of Aviation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 6141

Special Issue Editors

Dr. Tao Li

E-Mail Website
Guest Editor
Texas A&M Transportation Institute, College Station, TX 77843-3135, USA
Interests: air traffic management; large-scale aviation system; environmental impact of aviation activities
Prof. Dr. Yanjun Wang

E-Mail Website
Guest Editor
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: modeling and optimization for air transportation systems; network resilience; human factors in air traffic control
Dr. Max Z. Li

E-Mail Website
Guest Editor
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Interests: air transportation systems; networked systems; air traffic management; airline and airport operations; emerging air mobility systems

Special Issue Information

Dear Colleagues,

In October 2021, the International Air Transport Association (IATA) approved a resolution for the global air transport industry to achieve net-zero carbon emissions by 2050 (IATA, 2021). This is a momentous decision to ensure the sustainability of the global aviation industry so that our future generations can also enjoy the freedom to sustainably explore, learn, trade, and connect with the rest of the world as we do today. Achieving net-zero emissions while serving the growing demand will be a huge challenge. Addressing this challenge will require collaboration between industry and government through the use of, for example, policies, new technologies, and efficiency improvements.

Air traffic management (ATM) plays an important role in the sustainability of an aviation system as it assists pilots and aircraft to depart from an aerodrome, transit airspace, and land at a destination aerodrome. Therefore, improving ATM has been identified as a key opportunity to reduce the environmental impact of aviation activities while meeting the growing demand in the short to medium term.

The aim of this Special Issue is to investigate how ATM should be adapted to improve the sustainability and environmental performance of aviation. In this Special Issue, original research articles and reviews are welcome. Topics may include (but are not limited to) the following:

  • Studies that identify issues and challenges in the current ATM to achieve a sustainable and environment-friendly aviation system and propose potential solutions. 
  • Metrics and methods to evaluate the impact of components of an ATM system on the sustainability and environmental performance (e.g., fuel consumption, emission, noise) of aviation activities or to establish a quantitative relationship between the two.  
  • Application of new ATM technologies and equipment (e.g., new surveillance and communication technologies, automation, and digitalization) and/or innovative ATM procedures and processes (e.g., better flight planning and collaborative decision making)  to improve the sustainability and environmental performance of aviation
  • Design of ATM systems for new or future airspace users (e.g., advanced air mobility and unmanned aerial systems) with the consideration of sustainability and environmental performance.
  • While ATM is important, many other factors (e.g., ground vehicles, fuel, and aircraft engine technology) also play a crucial role in the sustainability and environmental performance of aviation. Studies that focus on the non-ATM factors that have a significant dependency on the ATM or can lead to significant changes to the ATM are also welcomed.

We look forward to receiving your contributions.

References:

International Air Transport Association, (Oct 2021), “Resolution on the industry’s commitment to reach net-zero carbon emissions by 2050” [Press release]. https://www.iata.org/en/pressroom/2021-releases/2021-10-04-03/

Dr. Tao Li
Prof. Dr. Yanjun Wang
Dr. Max Z. Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air traffic management
  • net-zero carbon emissions
  • aviation noise reduction
  • aviation emission reduction
  • aviation fuel consumption
  • aviation environmental impact
  • aviation sustainability
  • innovative ATM

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 5493 KiB  
Article
A Methodology for Predicting Ground Delay Program Incidence through Machine Learning
by Xiangning Dong, Xuhao Zhu, Minghua Hu and Jie Bao
Sustainability 2023, 15(8), 6883; https://doi.org/10.3390/su15086883 - 19 Apr 2023
Cited by 1 | Viewed by 1569
Abstract
Effective ground delay programs (GDP) are needed to intervene when there are bad weather or airport capacity issues. This paper proposes a new methodology for predicting the incidence of effective ground delay programs by utilizing machine learning techniques, which can improve the safety [...] Read more.
Effective ground delay programs (GDP) are needed to intervene when there are bad weather or airport capacity issues. This paper proposes a new methodology for predicting the incidence of effective ground delay programs by utilizing machine learning techniques, which can improve the safety and economic benefits of flights. We use the combination of local weather and flight operation data along with the ATM airport performance (ATMAP) algorithm to quantify the weather and to generate an ATMAP score. We then compared the accuracy of three machine learning models, Support Vector Machine, Random Forest, and XGBoost, to estimate the probability of GDPs. The results of the weather analysis, performed by the ATMAP algorithm, indicated that the ceiling was the most critical weather factor. Lastly, we used two linear regression models (ridge and LASSO) and a non-linear regression model (decision tree) to predict departure flight delays during GDP. The predictive accuracy of the regression models was enhanced by an increase in ATMAP scores, with the decision tree model outperforming the other models, resulting in an improvement of 8.8% in its correlation coefficient (R2). Full article
(This article belongs to the Special Issue Air Traffic Management (ATM) for the Sustainability and Environmental Performance of Aviation)
►▼ Show Figures

Figure 1

14 pages, 8631 KiB  
Article
Distribution Prediction of Strategic Flight Delays via Machine Learning Methods
by Ziming Wang, Chaohao Liao, Xu Hang, Lishuai Li, Daniel Delahaye and Mark Hansen
Sustainability 2022, 14(22), 15180; https://doi.org/10.3390/su142215180 - 16 Nov 2022
Cited by 7 | Viewed by 3153
Abstract
Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules [...] Read more.
Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules several months in advance; thus, predicting flight delays at the strategic stage is critical for airport slot allocation and airlines’ operation. However, less work has been dedicated to predicting flight delays at the strategic phase. This paper proposes machine learning methods to predict the distributions of delays. Three metrics are developed to evaluate the performance of the algorithms. Empirical data from Guangzhou Baiyun International Airport are used to validate the methods. Computational results show that the prediction accuracy of departure delay at the 0.65 confidence level and the arrival delay at the 0.50 confidence level can reach 0.80 without the input of ATFM delay. Our work provides an alternative tool for airports and airlines managers for estimating flight delays at the strategic phase. Full article
(This article belongs to the Special Issue Air Traffic Management (ATM) for the Sustainability and Environmental Performance of Aviation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop