Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines.

This paper focuses on terminal air traffic complexity indicators. By thorough analysis of previous research, the benefits and limitations of the existing terminal complexity models are identified. According to these findings, a new approach for determining terminal air traffic complexity indicators is proposed which assumes that terminal complexity could be determined based on approach air traffic controller (ATCO) tasks. The comprehensive list of general approach ATCO tasks was defined using a literature review and observation of training exercises, forming the basis for subsequent expert group workshops which enabled the acquisition of ATCOs’ knowledge data. Through these workshops, new approach ATCO tasks were additionally identified, and terminal complexity indicators were defined with airspace and traffic parameters. These new tasks and indicators present a novelty in this field of research since they incorporate ATCOs’ knowledge as the data input and consider various traffic scenarios, all types of traffic, weather conditions, and off-nominal situations.

Current methods to reduce air traffic complexity during convective weather conditions are sometimes not sufficient to meet the traffic demand in such situations. The most common strategy deployed by flow management personnel for complexity reduction during convective weather is to apply a limit to the number of aircraft entering the affected or surrounding airspace. Such complexity reduction measures globally affect the airspace capacity and can be inefficient. A better outcome in complexity reduction would be a complexity reduction measure that either restructures the airspace to rebalance complexity or a measure that cherry-picks flights with the highest impact on complexity. This paper proposes a method for complexity reduction in convective weather, which applies short-term air traffic flow management measures (STAMs) such as airspace restructuring and individual aircraft trajectory changes. Complexity reduction measures are applied to the most complex sector, thus reducing the global effect of a measure. The results of this paper showed that STAMs can reduce air traffic complexity by up to 19.63%. The benefit gained from STAMs comes with diminishing returns when more than four STAMs are applied to the most complex sector. When comparing traffic scenarios without convective weather to those with convective weather, it was shown that convective weather has a variable impact on air traffic complexity, ranging from 91% to 184% of the nominal complexity value.

Fuel burn during the actual route flown is an important indicator of aircraft operational efficiency. This study aims to assess and systematically evaluate the method for fuel consumed during flights using data from the automatic dependent surveillance–broadcast (ADS-B), European reanalysis (ERA5) meteorological dataset, and BADA 3 performance. A literature background and comprehensive methodology are provided for fuel estimation using track data. The airborne part of the trajectory was used to estimate the total trip fuel consumed during several flights of a commercial airliner. The calculated fuel burn is compared with measured fuel consumption from the flight data recorder (FDR). The results show that fuel consumption for the entire airborne part of the trajectory can be estimated with an average error of 1.2% and with a standard deviation of 1.3%. Detailed results of fuel burn for individual flight phases, from the initial climb to the approach, are also presented. In addition, this paper also discusses the sources of errors and the potential applications of the method for network operations and environmental monitoring.

Visual flight rules (VFR) traffic is routinely excluded from capacity models and workload assessments in terminal airspace, despite its potential to increase air traffic controller (ATCO) workload. This exploratory pilot study examined the association between a mixed instrument flight rules (IFR) and VFR terminal scenario and perceived approach-control workload in the simulated Zagreb Terminal Manoeuvring Area (TMA). Nine ATCO trainees completed three within-subject simulator exercises in a fixed order: a mixed IFR/VFR scenario (EXP1), an operationally comparable IFR-only baseline (EXP2), and a higher-density IFR-only scenario (EXP3). Workload was measured using the NASA Task Load Index (NASA-TLX). The mixed scenario was associated with a 9.26-point higher mean weighted NASA-TLX score than the IFR-only baseline (Cohen’s $d_z=0.67$; 95% CI $-1.33$ to $19.85$; two-sided $p=0.079$); an exploratory directional analysis, specified before data collection, yielded one-sided $p=0.039$. The omnibus analysis was not significant ($F(2,16)=2.56$, $p=0.109$). Frustration and Effort were the only subscales with Holm-adjusted p-values below 0.05; these results are interpreted as exploratory. The two IFR-only scenarios showed little observed difference. The fixed scenario order, small trainee sample, and scripted degraded communication preclude causal inference, motivating a larger counterbalanced confirmatory study with operational controllers.