Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center

Objective: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Background: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. Method: A multilevel workload model was developed in Study 1 with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters. The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Results: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. Conclusion: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Application: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs. Tactical uses include the dynamic reallocation of resources to meet changes in demand.

Assessing the Impact of Sensor Accuracy and Sensor Choice on Air Traffic Conflict Risk Estimation using Risk-Ratio Concepts

One of the primary desired capabilities of any future air traffic separation management system is the ability to provide early conflict detection and resolution effectively and efficiently. In this paper, we consider the risk of conflict as a primary measurement to be used for early conflict detection. This paper focuses on developing a novel approach to assess the impact of different measurement uncertainty models on the estimated risk of conflict. The measurement uncertainty model can be used to represent different sensor accuracy and sensor choices. Our study demonstrates the value of modelling measurement uncertainty in the conflict risk estimation problem and presents techniques providing a means of assessing sensor requirements to achieve desired conflict detection performance.

Test architecture for prototyping automated dynamic airspace control

The automation of various aspects of air traffic management has many wide-reaching benefits including: reducing the workload for Air Traffic Controllers; increasing the flexibility of operations (both civil and military) within the airspace system through facilitating automated dynamic changes to en-route flight plans; ensuring safe aircraft separation for a complex mix of airspace users within a highly complex and dynamic airspace management system architecture. These benefits accumulate to increase the efficiency and flexibility of airspace use(1). Such functions are critical for the anticipated increase in volume of manned and unmanned aircraft traffic. One significant challenge facing the advancement of airspace automation lies in convincing air traffic regulatory authorities that the level of safety achievable through the use of automation concepts is comparable to, or exceeds, the accepted safety performance of the current system.

Flight testing of an augmented separation management system providing separation protection during failure of the central communication network

Future air traffic management concepts often involve the proposal of automated separation management algorithms that replaces human air traffic controllers. This paper proposes a new type of automated separation management algorithm (based on the satisficing approach) that utilizes inter-aircraft communication and a track file manager (or bank of Kalman filters) that is capable of resolving conflicts during periods of communication failure. The proposed separation management algorithm is tested in a range of flight scenarios involving during periods of communication failure, in both simulation and flight test (flight tests were conducted as part of the Smart Skies project). The intention of the conducted flight tests was to investigate the benefits of using inter-aircraft communication to provide an extra layer of safety protection in support air traffic management during periods of failure of the communication network. These benefits were confirmed.

Filter and control performance bounds in the presence of model uncertainties with aerospace applications

This thesis establishes performance properties for approximate filters and controllers that are designed on the basis of approximate dynamic system representations. These performance properties provide a theoretical justification for the widespread application of approximate filters and controllers in the common situation where system models are not known with complete certainty. This research also provides useful tools for approximate filter designs, which are applied to hybrid filtering of uncertain nonlinear systems. As a contribution towards applications, this thesis also investigates air traffic separation control in the presence of measurement uncertainties.

Evaluation of separation management algorithms in Class G airspace

Use of Unmanned Aerial Vehicles (UAVs) in support of government applications has already seen significant growth and the potential for use of UAVs in commercial applications is expected to rapidly expand in the near future. However, the issue remains on how such automated or operator-controlled aircraft can be safely integrated into current airspace. If the goal of integration is to be realized, issues regarding safe separation in densely populated airspace must be investigated. This paper investigates automated separation management concepts in uncontrolled airspace that may help prepare for an expected growth of UAVs in Class G airspace. Not only are such investigations helpful for the UAV integration issue, the automated separation management concepts investigated by the authors can also be useful for the development of new or improved Air Traffic Control services in remote regions without any existing infrastructure. The paper will also provide an overview of the Smart Skies program and discuss the corresponding Smart Skies research and development effort to evaluate aircraft separation management algorithms using simulations involving realworld data communication channels, and verified against actual flight trials. This paper presents results from a unique flight test concept that uses real-time flight test data from Australia over existing commercial communication channels to a control center in Seattle for real-time separation management of actual and simulated aircraft. The paper also assesses the performance of an automated aircraft separation manager.

Separation management approaches during periods of communication failure

New air traffic automated separation management concepts are constantly under investigation. Yet most of the automated separation management algorithms proposed over the last few decades have assumed either perfect communication or exact knowledge of all aircraft locations. In realistic environments, these idealized assumptions are not valid and any communication failure can potentially lead to disastrous outcomes. This paper examines the separation performance behavior of several popular algorithms during periods of information loss. This comparison is done through simulation studies. These simulation studies suggest that communication failure can cause the performance of these separation management algorithms to degrade significantly. This paper also describes some preliminary flight tests.

Automated centralised separation management with onboard decision support

This paper proposes a novel automated separation management concept in which onboard decision support is integrated within a centralised air traffic separation management system. The onboard decision support system involves a decentralised separation manager that can overrule air traffic management instructions under certain circumstances. This approach allows the advantages of both centralised and decentralised concepts to be combined (and disadvantages of each separation management approach to be mitigated). Simulation studies are used to illustrate the potential benefits of the combined separation management concept.

Aircraft automation tasks requiring switching control

A key feature in future aircraft operations will be automation of various aircraft processes, such as air traffic separation management and the management of forced landing events. Automated versions of these processes will often involve consideration of multiple modes of operations and hence require consideration of automated decision processes able to switch between various available modes of operations. This paper proposes a switching algorithm on the basis of max-min decision theory. This algorithm is particularly suitable in situations where each operational mode has access to different set of partial information. We apply our proposed algorithm to the air traffic separation management problem. A simulation study is presented that illustrates the performance of the proposed switching algorithm.

Design of flight avoidance manoeuvre alphabets for mixed centralised-decentralised separation management

This paper investigates a mixed centralised-decentralised air traffic separation management system, which combines the best features of the centralised and decentralised systems whilst ensuring the reliability of the air traffic management system during degraded conditions. To overcome communication band limits, we propose a mixed separation manager on the basis of a robust decision (or min-max) problem that is posed on a reduced set of admissible flight avoidance manoeuvres (or a FAM alphabet). We also present a design method for selecting an appropriate FAM alphabet for use in the mixed separation management system. Simulation studies are presented to illustrate the benefits of our proposed FAM alphabet based mixed separation manager.

The smart skies project : enabling technologies for future airspace environments

This paper summarises the achievements of the Smart Skies Project, a three-year, multi-award winning international project that researched, developed and extensively flight tested four enabling aviation technologies: an electrooptical mid-air collision avoidance system, a static obstacle avoidance system, a mobile ground-based air traffic surveillance system, and a global automated airspace separation management system. The project included the development of manned and unmanned flight test aircraft, which were used to characterise the performance of the prototype systems for a range of realistic scenarios under a variety of environmental conditions. In addition to the collection of invaluable flight data, the project achieved world-firsts in the demonstration of future automated collision avoidance and separation management concepts. This paper summarises these outcomes, the overall objectives of the project, the research and the development of the prototype systems, the engineering of the flight test systems, and the results obtained from flight-testing.

Understanding worker perceptions of common incidents at roadworks in Queensland

The process of building safer roads and roadsides needs to be managed to minimise risks to both the road using public and roadworkers. However, detailed and accurate data on fatalities and injuries at roadworks across Australia are not available. The lack of reliable safety records and consequent poor understanding of the hazards at roadworks motivated this research to examine the common trends in incidents and to understand workers' perceptions of the causes of incidents at roadworks. To achieve these aims, 66 roadworks personnel were interviewed in Queensland including road construction workers, traffic controllers, engineers, and managers. Qualitative analyses identified several major issues and themes. Vehicles driving into work areas, traffic controllers hit by vehicles, rear end crashes at roadwork approaches, and reversing incidents involving work vehicles and machinery were the most common types of incidents. Roadworkers perceived driver errors, such as violation of speed limits, distracted driving, and ignoring signage and traffic controllers' instructions as the main causes of the incidents.

A Tobit model for analyzing speed limit compliance in work zones

Poor compliance with speed limits is a serious safety concern in work zones. Most studies of work zone speeds have focused on descriptive analyses and statistical testing without systematically capturing the effects of vehicle and traffic characteristics. Consequently, little is known about how the characteristics of surrounding traffic and platoons influence speeds. This paper develops a Tobit regression technique for innovatively modeling the probability and the magnitude of non-compliance with speed limits at various locations in work zones. Speed data is transformed into two groups—continuous for non-compliant and left-censored for compliant drivers—to model in a Tobit model framework. The modeling technique is illustrated using speed data from three long-term highway work zones in Queensland, Australia. Consistent and plausible model estimates across the three work zones support the appropriateness and validity of the technique. The results show that the probability and magnitude of speeding was higher for leaders of platoons with larger front gaps, during late afternoon and early morning, when traffic volumes were higher, and when higher proportions of surrounding vehicles were non-compliant. Light vehicles and their followers were also more likely to speed than others. Speeding was more common and greater in magnitude upstream than in the activity area, with higher compliance rates close to the end of the activity area and close to stop/slow traffic controllers. The modeling technique and results have great potential to assist in deployment of appropriate countermeasures by better identifying the traffic characteristics associated with speeding and the locations of lower compliance.

Threat of resource loss: The role of self-regulation in adaptive task performance

Changes at work are often accompanied with the threat of, or actual, resource loss. Through an experiment, we investigated the detrimental effect of the threat of resource loss on adaptive task performance. Self-regulation (i.e., task focus and emotion control) was hypothesized to buffer the negative relationship between the threat of resource loss and adaptive task performance. Adaptation was conceptualized as relearning after a change in task execution rules. Threat of resource loss was manipulated for 100 participants undertaking an air traffic control task. Using discontinuous growth curve modeling, 2 kinds of adaptation—transition adaptation and reacquisition adaptation—were differentiated. The results showed that individuals who experienced the threat of resource loss had a stronger drop in performance (less transition adaptation) and a subsequent slower recovery (less reacquisition adaptation) compared with the control group who experienced no threat. Emotion control (but not task focus) moderated the relationship between the threat of resource loss and transition adaptation. In this respect, individuals who felt threatened but regulated their emotions performed better immediately after the task change (but not later on) compared with those individuals who felt threatened and did not regulate their emotions as well. However, later on, relearning (reacquisition adaptation) under the threat of resource loss was facilitated when individuals concentrated on the task at hand.

Aircraft trajectory clustering techniques using circular statistics

This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic patterns. First, a resampled version of each trajectory is modelled using a mixture of Von Mises distributions (circular statistics). Second, the remodelled trajectories are globally aligned using tools from bioinformatics. Third, the alignment scores are used to cluster the trajectories using an iterative k-medoids approach and an appropriate distance function. The approach is then evaluated using synthetically generated unmanned aircraft flights combined with real air traffic position reports taken over a sector of Northern Queensland, Australia. Results suggest that the technique is useful in distinguishing between expected unmanned and manned aircraft traffic behaviour, as well as identifying some common conventional air traffic patterns.