Air Traffic Management Scenarios Research Articles

A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers

Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the “fight-freeze-flight” response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller’s stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator’s health. In this regard, sixteen controllers were asked to perform a realistic air traffic management (ATM) simulation during which subjective data (i.e. stress perception) and neurophysiological data (i.e. brain activity, heart rate, and galvanic skin response) were collected with the aim of accurately characterising the controller’s stress level experienced in the various experimental conditions. In addition, external supervisors regularly evaluated the controllers in terms of manifested stress, safety, and efficiency throughout the ATM scenario. The results demonstrated 1) how the stressful events caused both supervisors and controllers to underestimate the experienced stress level, 2) the advantage of taking into account both cognitive and hormonal processes in order to define a reliable stress index, and 3) the importance of the points in time at which stress is measured owing to the potential transient effect once the stressful events have ceased.

Geometric Approach Towards Airspace Assessment for Emerging Operations

Emerging urban air mobility operators propose to introduce extensive flight networks into metropolitan airspace. However, this airspace currently contains complex legacy airspace structure and flight operations that are perceived as safe, efficient, and generally acceptable to the overflown public. Hence, air traffic management concepts to support these emerging operators may be constrained to cause little to no interference with legacy operations. The identification of airspace that is noninterfering and potentially “available” to these new operators is therefore a critical first step toward their integration. This paper introduces a geometric airspace assessment approach that considers seven features of airspace that may preclude these new operations (that is, airspace constructs). Four hypothetical air traffic management scenarios are developed that prescribe different degrees of integration. An alpha-shape topological method is refined to process geometrically complex airspace construct polygons over an expansive geographic area and develop three-dimensional mappings of airspace availability. The approach is demonstrated in the San Francisco Bay Area and is readily extensible to other locations. It is envisioned to be useful in identification of viable takeoff and landing sites; evaluation of the sensitivity of airspace availability to separation or trajectory conformance requirements; and flight route design, throughput estimation, and risk analysis.

Chapter 15 - Monitoring performance of professional and occupational operators

The human capacity to simultaneously perform several tasks depends on the quantity and the mode of mentally processing the information imposed by the tasks. Since operational environments are highly dynamic, priorities across tasks will be expected to change as the mission evolves, thus the capability to reallocate the mental resources dynamically depending on such changes is very important. The resources required in very complex situations, such as air traffic management (ATM), can exceed the user's available resources leading to increased workload and performance impairments. In this regard, the availability of information concerning the workload experienced by the operators while dealing with tasks will be fundamental for both warning them when overload conditions are approaching and improving interactions with the system. The idea of our work was to use neurophysiologic data collected from professional air traffic controllers (ATCOs) to provide additional information to standard measures with which to assess the ATCOs' expertise and a machine learning electroencephalography-based index to evaluate their mental workload during the execution of ATC tasks. The results showed that the proposed method was able to track the workload alongside the execution of the realistic ATM scenario, and provide added values to objectively assess the expertise of the ATCOs.

A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects

The growth in demand for air transport has generated new challenges for capacity and safety. In response, manufacturers develop new types of aircraft while airlines open new routes and adapt their fleet. This excessive demand for air transport also leads to the need for further investments in airport expansion and ATM modernization. The current work was focused on the ATM problem with respect to new procedures, such as free flight, for addressing the air capacity issues in an environmental approach. The study was triggered by and aligned with the following performance objectives set by EUROCONTROL and the European Commission: (1) to improve ATM safety whilst accommodating air traffic growth; (2) to increase the ATM network efficiency; (3) to strengthen ATM’s contribution to aviation security and to environmental objectives; (4) to match capacity and air transport growth. The proposed mathematical model covers the aforementioned objectives by focusing on energy losses and costs of flights under the scenario of a controlled free flight and a unified airspace. The factors enhanced in the model were chosen based on their impact on the ATM energy efficiency, such as the airborne delays and flight duration, the delays due to ground holding, the flight cancellation, the flight speed deviations and the flight level alterations. Therefore, the presented mathematical model minimizes the energy costs due to the above terms under certain assumptions and constraints. Finally, simulation case studies, used as proof tests, have been conducted under different ATM scenarios to examine the complexity and the efficiency of the developed model.

Innovative time-based separation procedures for civil RPAS integration

Purpose This paper aims to suggest feasible solutions to overcome the problem of unmanned aerial vehicles integration within the existing airspace. Design/methodology/approach It envisages innovative time-based separation procedures that will enhance the integration in the future air traffic management (ATM) system of next generation of large remotely piloted aircraft system (RPAS). 4D navigation and dynamic mobile area concepts, both proposed in the framework of Single European Sky ATM Research program, are brought together to hypothesize innovative time-based separation procedures aiming at promoting integration of RPAS in the future ATM system. Findings Benefits of proposed procedures, mainly evaluated in terms of volume reduction of segregated airspace, are quantitatively analyzed on the basis of realistic operational scenarios focusing on monitoring activities in both nominal and emergency conditions. Eventually, the major limits of time-based separation for RPAS are investigated. Practical implications The implementation of the envisaged procedures will be a key enabler in RPAS integration in future ATM integration. Originality/value In the current ATM scenario, separation of RPAS from air traffic is ensured by segregating a large amount of airspace areas with fixed dimensions, dramatically limiting the activities of these vehicles.

Human Factors and Neurophysiological Metrics in Air Traffic Control: A Critical Review.

This paper provides a focused and organized review of the research progress on neurophysiological indicators, also called "neurometrics," to show how they can effectively address some of the most important human factors (HFs) needs in the air traffic management (ATM) field. In order to better understand and highlight available opportunities of such neuroscientific applications, state of the art on the most involved HFs and related cognitive processes (e.g., mental workload and cognitive training) are presented together with examples of possible applications in current and future ATM scenarios. Furthermore, this paper will discuss the potential enhancements that further research and development activities could bring to the efficiency and safety of the ATM service.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

Uncertainty management in enroute air traffic control: a field study exploring controller strategies and requirements for automation

The management of uncertainty is a critical aspect of current as well as future air traffic control operations. This study investigated: (1) sources of uncertainty in enroute air traffic control, (2) strategies that air traffic controllers adopt to cope with uncertainty, (3) the trade-offs and contingencies that influences the adoption of these uncertainties, and (4) the requirements for system design that support controllers in following these strategies. The data were collected using a field study in two enroute air traffic control centres, involving "over the shoulder" observation sessions, discussions with air traffic controllers, and document analysis. Three types of uncertainty coping strategies were identified: reducing uncertainty, acknowledging uncertainty, and increasing uncertainty. The RAWFS heuristic (Lipshitz and Strauss in Organ Behav Hum Decis Process 69:149---163, 1997) and anticipatory thinking (Klein et al. in Anticipatory thinking, Proceedings of the eighth international NDM conference, Pacific Grove, CA, 2007) were used to identify reduction and acknowledgement strategies. Recent suggestions by Grote (Saf Sci 71:71---79, 2015) were used to further explore strategies that increase uncertainty. The study presents a new framework for the classification of uncertainties in enroute air traffic control and identified the uncertainty management strategies and underlying tactics, in context of contingencies and trade-offs between operational goals. The results showed that controllers, in addition to reducing and acknowledging uncertainty, may deliberately increase uncertainty in order to increase flexibility for other actors in the system to meet their operational goals. The study describes new tactics for acknowledging and increasing uncertainty. The findings were summarized in the air traffic controller complexity and uncertainty management model. Additionally, the results bring to light system design recommendations that allow controllers to follow these different coping strategies, including (1) the design of alerts, (2) the transparency of prediction tools, and (3) system flexibility as a requirement for acknowledging and increasing uncertainty. The results are particularly important as uncertainty is likely to increase in future operations of enroute air traffic control, requiring automation support for controllers. Implications for future air traffic management scenarios as envisioned within the SESAR Joint Undertaking (SESAR JU in European ATM Master Plan, 2 eds, 2012) and NextGen (FAA in FAA's NextGen implementation plan, 2014) operational concepts are discussed.

A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.

In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.

Using Multisensory Cues to Facilitate Air Traffic Management

In the present study, we sought to investigate whether auditory and tactile cuing could be used to facilitate a complex, real-world air traffic management scenario. Auditory and tactile cuing provides an effective means of improving both the speed and accuracy of participants' performance in a variety of laboratory-based visual target detection and identification tasks. A low-fidelity air traffic simulation task was used in which participants monitored and controlled aircraft.The participants had to ensure that the aircraft landed or exited at the correct altitude, speed, and direction and that they maintained a safe separation from all other aircraft and boundaries. The performance measures recorded included en route time, handoff delay, and conflict resolution delay (the performance measure of interest). In a baseline condition, the aircraft in conflict was highlighted in red (visual cue), and in the experimental conditions, this standard visual cue was accompanied by a simultaneously presented auditory, vibrotactile, or audiotactile cue. Participants responded significantly more rapidly, but no less accurately, to conflicts when presented with an additional auditory or audiotactile cue than with either a vibrotactile or visual cue alone. Auditory and audiotactile cues have the potential for improving operator performance by reducing the time it takes to detect and respond to potential visual target events. These results have important implications for the design and use of multisensory cues in air traffic management.

Safety criticality analysis of complex Air Traffic Management systems via compositional bisimulation

The inherent complexity of Air Traffic Management (ATM) systems makes formal analysis a difficult task. Complexity of ATM systems is mainly due to their heterogeneity and to the large number of their sub–components. While heterogeneity in the diverse components of ATM systems has been effectively approached by resorting to compositional hybrid-system formalism, methods for the formal analysis of realistic large scale ATM systems are few at present. In this paper we provide an approach to deal with the analysis of safety criticality for complex ATM systems. The approach proposed is centered on a mathematical representation of ATM systems, termed arenas of finite state machines, which are effective in modeling the behavior of ATM agents both in nominal and non–nominal modes of operation, as well as their interaction. Complexity reduction techniques are then proposed as a tool to analyze realistic ATM scenarios.

On Bayesian sample size determination

Three Bayesian methods are considered for the determination of sample sizes for sampling from the Laplace distribution – the distribution of time between rare events – with a normal prior. These methods are applied to the sizing of aircraft mid-air collisions in a navigation system or large flight path deviations of aircraft in air traffic management scenarios. A computer program handles all computations and gives a good insight about the best suggested method.

On a Combination of Gamma and Generalized Error Distributions with Application to Aircraft Flight Path Deviations

The probability distribution of certain rare events, such as large flight path deviations of aircraft in air traffic management (ATM) scenarios, can be modeled by a generalized error distribution with parameter k = 0.5, intermediate between the Laplace k = 1.0 and Gaussian k = 2.0 distribution. To model not only the “tail” shape of the distribution for rare events, but also the “body” shape for frequent events, a further extension is introduced—namely, the combined Gamma and generalized error distribution. It includes as particular cases the Gamma, Laplace, and Gauss distributions. Its general properties are obtained, including the normalization constant, the variance and all other central and noncentral moments of any order, as well as the cumulative probability distribution, and its series expansion for small variable and its asymptotic expansion for large variable. As an example, a mixture of generalized error distribution and combined Gamma and generalized error distribution reasonably models the whole range of small to large flight path deviations for aircraft flying in an ATM scenario.