Fatigue is an inevitable hazard in the provision of air traffic services and it has the potential to degrade human performance leading to occurrences. The International Civil Aviation Organization ...(ICAO) requires air navigation services which providers establish fatigue risk management systems (FRMS) based on scientific principles for the purpose of managing fatigue. To develop effective FRMSs, it is important to investigate the relationship between traffic volume, air traffic management occurrences, and fatigue. Fifty‐seven qualified ATCOs from a European Air Navigation Services provider participated in this research by providing data indicating their alertness levels over the course of a 24‐hour period. ATCOs’ fatigue data were compared against the total of 153 occurrences and 962,328 air traffic volumes from the Eurocontrol TOKAI incident database in 2019. The result demonstrated that ATCO fatigue levels are not the main contributory factor associated with air traffic management occurrences, although fatigue did impact ATCOs’ performance. High traffic volume increases ATCO cognitive task load that can surpass available attention resources leading to occurrences. Furthermore, human resilience drives ATCOs to maintain operational safety though they suffer from circadian fatigue. Consequently, FRMS appropriately implemented can be used to mitigate the effects of fatigue. First‐line countermeasure strategies should focus on enough rest breaks and roster schedule optimization; secondary strategies should focus on monitoring ATCOs’ task loads that may induce fatigue. It is vital to consider traffic volume and ATCOs’ alertness levels when implementing effective fatigue risk management protocols.
Abstract The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and ...Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagram-plus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves.
Air traffic control systems play a critical role in ensuring the sustainable and resilient flow of air traffic. The air traffic sector serves as a fundamental topological unit and is responsible for ...overseeing and maintaining the system’s sustainable operation. Examining the structural characteristics of the air traffic sector network is a useful approach to gaining an intuitive understanding of the system’s sustainability and resilience. In this paper, an air traffic sector network (ATSN) was established in mainland China using the complex network theory, and its motif characteristics were analyzed from a microscopic perspective. Additionally, subgraph resilience was defined in order to describe the network topology by analyzing changes in subgraph motif concentration and subgraph residual concentration. Our empirical findings indicated that motifs exhibit high connectivity, while anti-motifs are found in subgraph structures with low connectivity. The motif concentration of subgraphs can efficiently reflect the distribution of heterogeneous subgraph structures within a network. During the process of resilience evaluation, the subgraph motif concentration remains relatively stable but is sensitive to the transition state of the network from disturbance to recovery. The resilience of the system at the macroscopic scale is aligned with the resilience of each heterogeneous subgraph structure to some extent. Topological indicators have a more significant impact on the resilience of the ATSN than air traffic flow characteristics. This study has the outcome of uncovering the preference for connection among nodes and the rationality of sector structure delineation in ATSNs. Additionally, this research addresses the fundamental mechanism behind the network disturbance recovery process, and identifies the connection between network macro- and microstructure in the resilience process.
•We develop two strategies to increase the resilience of air traffic networks.•We show an ‘adaptive’ resilience strategy is superior over a ‘permanent’ strategy.•This strategy is also able to ensure ...that network maintains reasonable connectivity and efficiency.•Air traffic networks should form dynamic contingency plans to minimize disruption.•These plans should adaptively utilize spare capacity at neighbouring airports.
Air traffic networks are essential to today’s global society. They are the fastest means of transporting physical goods and people and are a major contributor to the globalisation of the world’s economy. This increasing reliance requires these networks to have high resilience; however, previous events show that they can be susceptible to natural hazards. We assess two strategies to improve the resilience of air traffic networks and show an adaptive reconfiguration strategy is superior to a permanent re-routing solution. We find that, if traffic networks have fixed air routes, the geographical location of airports leaves them vulnerable to spatial hazard.
•A chance-constrained model is introduced to address capacity uncertainty in air traffic flow management.•A novel polynomial approximation-based approach is presented to solve the large-scale ...chance-constrained optimization problem.•A distributed computing framework is designed to improve the computational efficiency.
In order to efficiently balance traffic demand and capacity, optimization of Air Traffic Flow Management (ATFM) relies on accurate predictions of future capacity states. However, these predictions are inherently uncertain due to factors, such as weather. This paper presents a novel computationally efficient algorithm to address uncertainty in ATFM by using a chance-constrained optimization method. First, a chance-constrained model is developed based on a previous deterministic Integer Programming optimization model of ATFM to include probabilistic sector capacity constraints. Then, to efficiently solve such a large-scale chance-constrained optimization problem, a polynomial approximation-based approach is applied. The approximation is based on the numerical properties of the Bernstein polynomial, which is capable of effectively controlling the approximation error for both the function value and gradient. Thus, a first-order algorithm is adopted to obtain a satisfactory solution, which is expected to be optimal. Numerical results are reported in order to evaluate the polynomial approximation-based approach by comparing it with the brute-force method. Moreover, since there are massive independent approximation processes in the polynomial approximation-based approach, a distributed computing framework is designed to carry out the computation for this method. This chance-constrained optimization method and its computation platform are potentially helpful in their application to several other domains in air transportation, such as airport surface operations and airline management under uncertainties.
the air traffic management (ATM) system is a comprehensive information-based intelligent system that provides seamless services and dynamic integrated management of air traffic and airspace through ...the cooperation of all relevant parties in civil aviation. The ATM adopts an integrated "space-air-ground" network structure to provide reliable services for the security and efficiency of civil aviation flights, which is an important infrastructure to support and guarantee civil aviation transportation and an important support point for the development of civil aviation as a whole. The composition of the ATM is wide and complex, and contains many business systems and user types. In the face of the increasingly serious information security threats, the business collaboration of ATM and the shared use of ATM data are subject to certain restrictions and challenges. According to the Aviation Network Security Strategy released by International Civil Aviation Organization (ICAO) in 2019, there is an urgent need to research the basic theories, core methods and key technologies for ATM information security assurance that are compatible with the characteristics of ATM composition and information security assurance needs. From the core research objective of ATM information security assurance, this paper designs a future ATM security architecture based on blockchain technology, referred to as ATMChain, to meet the real operational needs of ATM trustworthiness, security and availability. ATMChain takes ATM trustworthy services as the core and builds an ATM information security base with "endogenous security" features. Then, three security function modules, namely, trusted authentication, data sharing, and access control, are designed to realize the 4A (Authentication, Account, Audit, Authorization) security functions of ATM. Finally, this paper provides a comprehensive analysis and performance evaluation of ATMChain security architecture. The results show that the research in this paper will help solve the current bottlenecks in ATM information security assurance, promote technological innovation, and ultimately facilitate the realization of the vision of global ATM interoperability proposed by ICAO.
The arrival-departure (AD) window is actually an area that controls takeoff, where departing aircraft on the side runway are not allowed to take off when there are approaching aircraft in the AD ...window. To solve the operation conflict of the lateral runway system, the model and method of AD window based on collision risk theory are proposed for the first time. Firstly, the paper takes the operation process of the landing and departing aircraft under the simultaneous operation of the lateral runway as the research object, and gives the risk prediction based on the flight program protection area combining the runway configuration and the operation characteristics of the aircraft. Secondly, combined with the relative position change characteristics of aircraft pairs on the lateral runway, the real-time dynamic interval calculation model of two aircraft is built. The lateral runway collision risk calculation model based on position error theory is further established. Then, the solution process of AD window is designed based on the dichotomy method, so as to determine the final AD window boundary. Finally, taking Beijing Daxing International Airport, China, as an example, the location of the AD window is calculated, and the boundary of the AD window is determined to be 8,012 m before the entrance of Dongyi runway, and the maximum critical collision risk value is 4.9371 × 10−9 times/flight hour. The results have been verified by the control simulator and applied to the actual operation of Beijing Daxing International Airport from 2019 to the present. The practical operation results show that the proposed method and model can quantitatively determine the position of the AD window and effectively control the operation risk of lateral runway systems.
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•A method for finding similar days in air traffic flow management initiative planning is proposed.•Features describing conditions on calendar days are defined.•The importance of these ...features is determined by modeling initiative implementation.•Cluster analysis reveals minimal structure in feature data.
This article describes a methodology for selecting days that are comparable in terms of the conditions faced during air traffic flow management initiative planning. This methodology includes the use of specific data sources, specific features of calendar days defined using these data sources, and the application of a specific form of classification and then cluster analysis. The application of this methodology will produce results that enable historical analysis of the use of initiatives and evaluation of the relative success of different courses of action. Several challenges are overcome here including the need to identify the appropriate machine learning algorithms to apply, to quantify the differences between calendar days, to select features describing days, to obtain appropriate raw data, and to evaluate results in a meaningful way. These challenges are overcome via a review of relevant literature, the identification and trial of several useful models and data sets, and careful application of methods. For example, the cluster analysis that ultimately selects sets of similar days uses a distance metric based on variable importance measures from a separate classification model of observed initiatives. The methodology defined here is applied to the New York area, although it could be applied by other researchers to other areas.
•We propose a methodology to determine minimum distance separation for safe airspace.•Algorithms for sector/traffic design and applying separation values are proposed.•Multiobjective optimization is ...used to find the minimum separation values.•We show the trade-off between separation values and aircraft pairs violating safety.•We illustrate how sector/traffic properties affect the minimum separation values.
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A precursor question to increase the capacity of an airspace is to determine the minimum distance separation required to make this airspace safe. A methodology to answer this question is proposed in this paper. The methodology takes sector volume, number of crossings and crossing angles of routes, and the number of aircraft as input, and generate air traffic scenarios which satisfy the input values. A stochastic multi-objective optimization algorithm is then used to optimize separation values. The algorithm outputs the set of non-dominated solutions representing the trade-off between separation values and the best attainable target level of safety. The results show that the proposed methodology is successful in determining the minimum distance separation values required to make an air traffic scenario safe from a collision risk perspective, and in illustrating how minimum separation values are affected by different sector/traffic characteristics.
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