Advanced computing methods and related technologies are changing the way engineers interact with the information infrastructure. Explicit knowledge representation formalisms and new reasoning techniques are no longer the sole territory of computer science. For knowledge-intensive tasks in engineering, a new philosophy and body of knowledge called Engineering Informatics is emerging.

Advanced Engineering Informatics solicits research papers with particular emphases both on 'knowledge' and 'engineering applications'. As an international Journal, original papers typically:

• Report progress in the engineering discipline of applying methods of engineering informatics.
• Have engineering relevance and help provide the scientific base to make engineering decision-making more reliable, spontaneous and creative.
• Contain novel research that demonstrates the science of supporting knowledge-intensive engineering tasks.
• Validate the generality, power and scalability of new methods through vigorous evaluation, preferably both qualitatively and quantitatively.

In addition, the Journal welcomes high quality review articles that summarise, compare, and evaluate methodologies and representations that are proposed for the field of engineering informatics. Similarly, summaries and comparisons of full-scale applications are welcomed, particularly those where scientific shortcomings have hindered success. Typically, such papers have expanded literature reviews and discussion of findings that reflect mastery of the current body of knowledge and propose novel additions to contemporary research.

Papers missing explicit representation and use of knowledge, such as those describing soft computing techniques, mathematical optimization methods, pattern recognition techniques, and numerical computation methods, do not normally qualify for publication in the Journal. Papers must illustrate contributions using examples of automating and supporting knowledge intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering. Papers that report application of an established method to a new engineering subdomain will qualify only if they convincingly demonstrate noteworthy new power, generality or scalability in comparison with previously reported validation results. Finally, papers that discuss software engineering issues only are not in the scope of this journal.

Special Issue on Airspace Optimization and Intelligent Air Traffic Models for Sustainable Air Transportation
Submission Date: 2024-08-01

The forecasted traffic growth in the aviation industry is expected to increase significantly over the coming years. According to the International Air Transport Association (IATA), pre-COVID-19 estimates, the number of air passengers is projected to double by 2037, with the Asia-Pacific region being the most significant contributor to this growth. Additionally, the growth of unmanned aerial vehicles (UAVs) and commercial space transportation is also expected to add to the traffic in the airspace. This creates a significant challenge for the ATM industry, which must adapt and handle growing traffic safely, efficiently, and at an economically acceptable cost.

The current airspace design and procedures have evolved over the years. They are constrained by limited capacity, poor scalability, fixed routes, fixed national airspace structures, limited automation, low level of information sharing, and fragmented Air Traffic Management (ATM) infrastructure. Although it has served its purpose well, it has now reached its operational limits, where it will be challenging for air navigation service providers (ANSPs) and airlines to accommodate future air traffic growth.

Future air traffic demand and challenges cannot be met with incremental changes in the ATM system and an automation plug-in approach. A holistic strategy is needed to achieve a resilient airspace that seamlessly integrates various ATM sub-systems, including en-route, terminal, and airside. Such an approach will accommodate forecasted growth in air traffic, reduce air traffic delays, and minimize fuel consumption through efficient demand-capacity balancing. Integrating sub-systems will enable advanced air traffic services, including time-based separation, trajectory-based operations, smart sequencing of traffic, and conformal automation support tools. The emergent challenges of coordinating air traffic across airspace sub-systems lead to the requirement for developing novel concepts of operations and artificial intelligence (AI) tools to support air traffic controllers (ATCO) in handling the growing traffic demand safely and efficiently.

Furthermore, the emergence of AI and advanced Communication, Navigation, and Surveillance (CNS) technologies is driving a profound transformation in ATM research and is part of ATM strategic planning worldwide. To achieve a green, safe, efficient, and seamless gate-to-gate experience for passengers, it is essential to leverage these emerging technologies to optimize and control air traffic planning, operations, and control.

This special issue aims to bring together the latest advancements in concepts of operations and AI models for integrated airspace management in the context of future air transportation systems. We seek to showcase innovative methodologies for strategic planning, tactical management, and operational control, leveraging emerging technologies such as data-driven decision support, AI-based machine learning, and large-scale simulation and optimization. Additionally, this proposal aims to explore the integration of various ATM sub-systems, including en-route, terminal, and airside, to enhance coordination and improve the efficiency and sustainability of the air transportation system.

The research topics of interest include but are not limited to:

Intelligent airspace design for seamless air traffic operations,
Novel representation and intelligent algorithms for dynamic airspace management and trajectory-based operations,
Human-machine collaboration in air traffic management,
Personalized automation models and Human-Machine Interface for supporting ATCOs,
Trust of humans in automation/recommendation systems,
Simulation platform for training and evaluation of the AI algorithms and novel concepts of operations.

Guest editors:
Assoc. Prof. Sameer Alam
Nanyang Technological University, Singapore City, Singapore
Prof. Eri Itoh
The University of Tokyo, Tokyo, Japan
Assist. Prof. Max Li
University of Michigan, Ann Arbor, Ann Arbor, United States
Dr. Duc-Thinh Pham
Nanyang Technological University, Singapore City, Singapore
Prof. Michael Schultz
Universität der Bundeswehr München, Neubiberg, Germany
Assoc. Prof. Yanjun Wang
Nanjing University of Aeronautics and Astronautics, Nanjing, China

Manuscript submission information:

Open for Submission: from 25-Mar-2024 to 01-Aug-2024