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