Moving sectors enable flow-centric management with enhanced airspace capacity, as controller workload is dynamically allocated according to traffic conditions.

This paper provides an extensive review of the emerging domain of Human-AI Hybrid in Air Traffic Management, defining and examining the fundamental pillars, including collaboration, adaptation, and trust.

Air transportation is frequently disrupted by factors such as weather and air traffic control. These disruptions challenge airport operations management, particularly in gate assignments, where potential conflicts and adjustments are often required.

Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a self-organized vertiport arrival system based on deep reinforcement learning.

This study establishes a foundation for determining the complexity of moving sectors, a key element of efficient flow-centric air traffic flow management. We apply complexity metrics for moving sectors derived from simulated air traffic of the Singapore FIR.

This study applies three developed complexity metrics to the Japanese sector-based airspace, which serves as a benchmark for the evaluation of moving sectors. Our results indicate that moving sectors could reduce peak loads by aggregating homogeneous traffic flows.

Delays deriving from airport ground operations at the aircraft stand possess the capacity to propagate and consequently escalate across the intricate air traffic network. This knock-on effect often exerts a significant detrimental influence on downstream flights and airports.

The growing use of spacecraft and drones is straining traditional airspace management and requiring new dynamic strategies, such as DDHC, to balance safety and minimize air traffic disruptions.

This paper delves into the rising use of location-aware radio communication systems to streamline operational processes. We propose a hybrid deterministic and stochastic simulation approach, incorporating model-based ray-tracing and empirical residual simulation.

We used different types of Data Envelopment Analysis to evaluate and compare the performance of European ANSPs and to develop and suggest an appropriate benchmarking approach.

A time-varying queuing network was modeled and applied to ASEAN free-route airspace. The model shows a 45% reduction in potential conflicts.

Moving sectors are introduced to improve airspace management by efficiently aligning traffic flows and grouping flights using realistic traffic patterns from the Singapore FIR.

Creating traffic flows that minimize congestion (hot spots) and enable flow-centric operations is important for future air traffic control.

The benefits and operational feasibility of the Free Route Airspace concept within the ASEAN airspace consisting of 12 Flight Information Regions (FIRs) are evaluated using fast-time simulation.

The proposed model-based framework for air traffic management optimizes arrival and departure flows, balancing capacity and demand. It includes time-varying queuing network models to reduce surface congestion and runway queuing.

Optimized boarding approach reduces time by 30%, minimizing virus risk compared to standard methods in various occupancy scenarios.

A prototype of a synthetic LiDAR sensor in a virtual environment of Singapore Changi Airport provides data to train machine learning models.

The implementation of optimized passenger group sequencing under COVID-19 conditions has the potential to reduce process times by up to 59% and transmission risk by up to 85%.

Integrated ground/airside airport management optimizes connecting flights for passengers.

The implementation of an E-AMAN at Singapore Airspace results in an estimated fuel saving of 1,524 kg along with a reduction in carbon dioxide emissions of 48,000 kg.

Evaluation of productivity or efficiency of air navigation services requires comprehensive economic modeling.

In the connected aircraft cabin, the use of connected sensors, devices, and passengers enables comprehensive status detection within the aircraft cabin.

Dynamic in-flight adjustments of a trajectory lead to improved use of airspace and airport capacity.