scholarly journals Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 103 ◽  
Author(s):  
Trevor Kistan ◽  
Alessandro Gardi ◽  
Roberto Sabatini
Keyword(s):  
Machine Learning ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Unmanned Aerial Systems ◽  
Research Attention ◽  
Cognitive Ergonomics ◽  
Traffic Flow Management ◽  
Recent Developments ◽  
Aerial Systems

Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human–machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.

scholarly journals Automation of Air Traffic Management Using Fuzzy Logic Algorithm to Integrate Unmanned Aerial Systems into the National Airspace

2018 ◽  
Vol 8 (5) ◽  
pp. 3169 ◽  
Author(s):  
Kouroush Jenab ◽  
Joseph Pineau
Keyword(s):  
Fuzzy Logic ◽  
Traffic Management ◽  
Current System ◽  
Significant Risk ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Unmanned Aerial Systems ◽  
Revolutionary Change ◽  
Near Misses ◽  
Weighted Values

Unmaned Aircraft Systems (UAS) have been increasing in popularity in personal, commercial, and military applications. The increase of the use of UAS poses a significant risk to general air travel, and will burden an already overburdened Air Traffic Control (ATC) network if the Air Traffic Management (ATM) system does not undergo a revolutionary change. Already there have been many near misses reported in the news with personal hobbyist UAS flying in controlled airspace near airports almost colliding with manned aircraft. The expected increase in the use of UAS over the upcoming years will exacerbate this problem, leading to a catastrophic incident involving substantial damage to property or loss of life. ATC professionals are already overwhelmed with the air traffic that exists today with only manned aircraft. With UAS expected to perform many tasks in the near future, the number of UAS will greatly outnumber the manned aircraft and overwhelm the ATC network in short order to the point where the current system will be rendered extremely dangerous, if not useless. This paper seeks to explore the possibility of using the artificial intelligence concept of fuzzy logic to automate the ATC system in order to handle the increased traffic due to UAS safely and efficiently. Automation would involve an algorithm to perform arbitration between aircraft based on signal input to ATC ground stations from aircraft, as well as signal output from the ATC ground stations to the aircraft. Fuzzy logic would be used to assign weights to the many different variables involved in ATM to find the best solution, which keeps aircraft on schedule while avoiding other aircraft, whether they are manned or unmanned. The fuzzy logic approach would find the weighted values for the available variables by running a simulation of air traffic patterns assigning different weights per simulation run, over many different runs of the simulation, until the best values are found that keep aircraft on schedule and maintain the required separation of aircraft

Democratizing business intelligence and machine learning for air traffic management safety

2022 ◽  
Vol 146 ◽  
pp. 105530
Author(s):  
R. Patriarca ◽  
G. Di Gravio ◽  
R. Cioponea ◽  
A. Licu
Keyword(s):  
Machine Learning ◽  
Business Intelligence ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic

The Airline's Perception of Air Traffic Management

1990 ◽  
Vol 43 (2) ◽  
pp. 204-208 ◽  
Author(s):  
Colin Hume
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Air Traffic Flow ◽  
Traffic Flow Management ◽  
Reverse Situation

The situation today can be described as very frustrating for a variety of reasons. Air traffic flow-management (ATFM) has dominated the scene for many years since its conception in 1980. At that time, the principles of ATFM were directed at ensuring that temporary or isolated sector overloads could be handled by ATC and only when broad, prolonged overloads were expected was ATFM activated. Today, we have the reverse situation, where ATFM is active throughout 16 h or more during each day. The system as such was never intended or planned to cope with such a burden and the results are seen in a variety of forms, including departure delays as shown in Fig. 1.

scholarly journals Insights of Models for Air Traffic Management System

2019 ◽  
Vol 8 (3S3) ◽  
pp. 459-467
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Traffic Flow Management ◽  
The Usa ◽  
Comprehensive Survey ◽  
Traffic Management System

In many airports and air markets, congestion problems & weather are becoming more and more severe. To keep Air Traffic Control (ATC) against the overload of Air Traffic Flow Management (ATFM) activity, attempts to anticipate and prevent the resulting overload and limit the delays. A delay in the arrival of the flight (so-called congestion) occurs when the traffic expects to surpass the arrival and departure capacity of the airport or the airsector capacity. There is a very extensive over general reasoning to be considered in this area. Generally speaking, most of the references found in the literature published a few years ago refer to the simplest versions, those that do not take airsector into account. This happens because the research was first done in the USA only, where traffic issues basicallylimited to the airports congestion. In the paper we present a comprehensive survey of the key optimization models of literature.

scholarly journals Explanation of Machine-Learning Solutions in Air-Traffic Management

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 224
Author(s):  
Yibing Xie ◽  
Nichakorn Pongsakornsathien ◽  
Alessandro Gardi ◽  
Roberto Sabatini
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Traffic Control ◽  
Traffic Management ◽  
Operational Risk ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Viable Solution ◽  
Human Operators ◽  
Post Hoc

Advances in the trusted autonomy of air-traffic management (ATM) systems are currently being pursued to cope with the predicted growth in air-traffic densities in all classes of airspace. Highly automated ATM systems relying on artificial intelligence (AI) algorithms for anomaly detection, pattern identification, accurate inference, and optimal conflict resolution are technically feasible and demonstrably able to take on a wide variety of tasks currently accomplished by humans. However, the opaqueness and inexplicability of most intelligent algorithms restrict the usability of such technology. Consequently, AI-based ATM decision-support systems (DSS) are foreseen to integrate eXplainable AI (XAI) in order to increase interpretability and transparency of the system reasoning and, consequently, build the human operators’ trust in these systems. This research presents a viable solution to implement XAI in ATM DSS, providing explanations that can be appraised and analysed by the human air-traffic control operator (ATCO). The maturity of XAI approaches and their application in ATM operational risk prediction is investigated in this paper, which can support both existing ATM advisory services in uncontrolled airspace (Classes E and F) and also drive the inflation of avoidance volumes in emerging performance-driven autonomy concepts. In particular, aviation occurrences and meteorological databases are exploited to train a machine learning (ML)-based risk-prediction tool capable of real-time situation analysis and operational risk monitoring. The proposed approach is based on the XGBoost library, which is a gradient-boost decision tree algorithm for which post-hoc explanations are produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Results are presented and discussed, and considerations are made on the most promising strategies for evolving the human–machine interactions (HMI) to strengthen the mutual trust between ATCO and systems. The presented approach is not limited only to conventional applications but also suitable for UAS-traffic management (UTM) and other emerging applications.

Safety Culture and Safety-Relevant Behavior in Air Traffic Management

2015 ◽  
Vol 5 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Michaela Schwarz ◽  
K. Wolfgang Kallus
Keyword(s):  
Safety Culture ◽  
Traffic Management ◽  
Service Providers ◽  
Five Factor Model ◽  
Principal Component ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Psychometric Validation ◽  
Safety Attitudes ◽  
Culture Development

Since 2010, air navigation service providers have been mandated to implement a positive and proactive safety culture based on shared beliefs, assumptions, and values regarding safety. This mandate raised the need to develop and validate a concept and tools to assess the level of safety culture in organizations. An initial set of 40 safety culture questions based on eight themes underwent psychometric validation. Principal component analysis was applied to data from 282 air traffic management staff, producing a five-factor model of informed culture, reporting and learning culture, just culture, and flexible culture, as well as management’s safety attitudes. This five-factor solution was validated across two different occupational groups and assessment dates (construct validity). Criterion validity was partly achieved by predicting safety-relevant behavior on the job through three out of five safety culture scores. Results indicated a nonlinear relationship with safety culture scales. Overall the proposed concept proved reliable and valid with respect to safety culture development, providing a robust foundation for managers, safety experts, and operational and safety researchers to measure and further improve the level of safety culture within the air traffic management context.

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