Large-scale airspace disruptions can reorganize civil aviation traffic far beyond the directly affected airspace, yet their operational consequences are difficult to characterize using traffic volume alone. This study examines the 28 February 2026 Middle East airspace disruption to understand the operational conditions within neighboring sectors by analyzing traffic observations in a period of about two weeks time around the event. It first establishes a multi-scale empirical picture of the disruption, from intercontinental route detours to corridor redistribution across the Middle East and traffic changes within the neighboring Jeddah Flight Information Region. Building on this evidence, a trajectory-computable framework is developed to identify temporal changes in regional traffic structure and characterize sector-level operational responses. The results show that the regional traffic structure departs rapidly from its baseline configuration during the initial disruption period and subsequently settles into a distinct and comparatively stable post-event regime with limited day-to-day variation. Within the Jeddah Flight Information Region, the redistribution is spatially uneven. Northeastern sectors largely lose their former transit function, while southeastern sectors increasingly absorb traffic associated with the southern alternative routing structure. Detailed analysis of a representative southeastern sector further shows that this changing role is not captured by traffic volume alone. Although instantaneous occupancy and weekly throughput remain broadly comparable with baseline levels, boundary-crossing activity becomes more spatially distributed, some crossing bands assume different downstream directional roles, trajectories become less direct and more maneuver-intensive, and locally proximate traffic interactions shift spatially.These findings show that major airspace disruptions are better understood as structural reorganizations of traffic rather than simple regional traffic losses, providing empirical insight into the operational challenges that air traffic management may face as traffic patterns reorganize after a disruption.
We study Innovative Air Mobility (IAM) as a dedicated hub feeder and dispersal service for Bologna Guglielmo Marconi Airport (BLQ), addressing short-range airport-to-airport connectivity where scheduled fixed-wing services are inefficient and surface access is time-consuming. Demand is generated directly from BLQ's flight timetable via a staggered rolling-window model: arriving passengers become IAM-eligible after a 20-minute buffer (a baseline 70
Terminal airspace congestion remains a major bottleneck in the global air traffic network. Although the Aircraft Sequencing and Scheduling Problem (ASSP) has been widely studied, many methods rely on simplified node-link abstractions that ignore the practical flight path, producing schedules that can be hard to execute under real airspace geometric constraints. This paper introduces a high-fidelity trajectory optimization framework for Terminal Arrival Sequencing and Scheduling (TASS) that explicitly models controller vectoring maneuvers. We formulate a single-stage nonlinear programming (NLP) model with a weighted objective function that optimizes Baseleg path extension and segment-wise speed profiles for arriving aircraft. The model enforces nonlinear geometric coupling between Baseleg extensions and the required Radius-to-Fix (RF) turn for Final Approach Fix (FAF) intercept through closed-form expressions. Under the First-Come-First-Served (FCFS) rule, it generates separated, operationally feasible trajectories aligned with controller practice. Monte Carlo simulations with ample runs on the A80 TRACON in Atlanta demonstrate minimal separation violations below the runway capacity threshold through tactical path stretching, with violations occurring only when arrival rates exceed the maximum tolerable rate.
BACKGROUND/OBJECTIVES:The human performance envelope (HPE) is a multidimensional model that represents the range in which an individual operator's performance is acceptable or begins to become dangerous. Although several alternative models have been proposed, HPE currently remains primarily a theoretical concept. The goal of the study was therefore to translate this theoretical concept into practical applications, seeking to characterize and measure how HPE manifests itself in real-world contexts. METHODS:Multivariate Autoregressive (MVAR) models and conditional transfer entropy (cTE) have been used in the analysis of complex systems in which processes are interdependent and mutually influence their dynamics over time. Professional Air Traffic Controllers were involved in the study and asked to deal with realistic traffic scenarios while their behavioural, subjective and neurophysiological data were collected. MVAR-cTE models were then employed to estimate the interactions among controller human factors and to identify the most appropriate characterization of the HPE. RESULTS:The results showed high and significant correlations among each controller's performance and the corresponding neurophysiological-based HPE values. Furthermore, high-performance conditions (best) were characterized by significantly higher HPE values and higher inter-human factor connections compared to the low-performance (worst) status. This evidence suggested that a densely interconnected network of Human Factors is a prerequisite for operational resilience. CONCLUSIONS:The study provided the first application of a neurophysiological framework to model the directed interactions between human factors, translating the theoretical HPE into a quantifiable model validated against operator performance.
