Explanation of Machine-Learning Solutions in Air-Traffic Management

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.

Download Full-text

Ensuring the implementation of the modernized structure and automation of air traffic control processes in the area of responsibility of the Moscow district of air traffic control

Informacionno-technologicheskij vestnik ◽

10.21499/2409-1650-2018-3-46-54 ◽

2018 ◽

pp. 46-54

Author(s):

A. V. Strukova

Keyword(s):

Traffic Control ◽

Traffic Management ◽

Air Traffic Control ◽

Management System ◽

Air Traffic Management ◽

Air Traffic ◽

Security System ◽

System Description ◽

Functional Capabilities ◽

Traffic Management System

The article considers the new automated air traffic management system «Synthesis AR4», as well as a system description for ensuring the implementation of a modernized airspace structure, navigation and surveillance that provides technical capabilities. A number of functional capabilities and advantages of the airspace security system are presented.

Download Full-text

Real-time decision support for air traffic management, utilizing machine learning

Control Engineering Practice ◽

10.1016/0967-0661(96)00113-x ◽

1996 ◽

Vol 4 (8) ◽

pp. 1129-1141 ◽

Cited By ~ 6

Author(s):

J Nogami ◽

S Nakasuka ◽

T Tanabe

Keyword(s):

Machine Learning ◽

Decision Support ◽

Real Time ◽

Traffic Management ◽

Air Traffic Management ◽

Air Traffic

Download Full-text

Key Aspects of Occupational Health and Safety towards Efficiency and Performance in Air Traffic Management

10.5772/intechopen.101086 ◽

2021 ◽

Author(s):

Dimitrios Dimitriou ◽

Stylianos Zantanidis

Keyword(s):

Quality Management ◽

Occupational Health ◽

Traffic Control ◽

Traffic Management ◽

Air Traffic Control ◽

Health And Safety ◽

Occupational Health And Safety ◽

Air Traffic Management ◽

Air Traffic ◽

Key Aspects

This paper/chapter deals with the key drivers for adopting and developing an Occupational Health and Safety System (OHS) with a special focus on air traffic management and traffic controller’s workplace. A such system includes regulation and legal compliance procedures, actions and monitoring for ensuring workplace safety, incentives and motivation for the air traffic controller and associate personnel health and wellbeing. By a systemic approach, the key characteristics of OHS towards air traffic management are presented, highlighting the key aspects for implementing a quality management system in air traffic control, which is the cornerstone of airport operation efficiency and productivity on one hand; and the nature of job and the intensive working environment is well recognised. Based on air traffic providers functional analysis the key occupational aspects for air traffic control are taken into consideration, providing the benefits for implementing quality management systems (QMS) and OHS is real business. Conventional wisdom is to highlight the importance for establishing and incorporating a modern custom-made OHS system in accordance with the requirements addressed by OHSAS 18001 to develop and implement a QMS for air traffic services. Contribution of this paper is to highlight the key priorities for managers and decision makers in field of air traffic services providers, depicting ways and recommendation for adopting an efficient path for implementing OHS in a QMS environment.

Download Full-text

Selected Aspects of Using the Telemetry Method in Synthesis of RelNav System for Air Traffic Control

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17010213 ◽

2019 ◽

Vol 17 (1) ◽

pp. 213 ◽

Cited By ~ 4

Author(s):

Milan Džunda ◽

Natália Kotianová ◽

Peter Dzurovčin ◽

Stanislav Szabo ◽

Edina Jenčová ◽

...

Keyword(s):

Computer Simulation ◽

Traffic Control ◽

Navigation System ◽

Traffic Management ◽

Air Traffic Control ◽

Air Traffic Management ◽

Air Traffic ◽

Relative Navigation ◽

Positioning Accuracy ◽

Satellite Navigation System

Accuracy is an important factor in air traffic management which is why high requirements are necessary for each navigation system. The aim of this article is to describe the principles of the RelNav system and telemetry and their accuracy. We present the algorithms of the relative navigation system, which could be used for air traffic control in the case of the unavailability of satellite navigation system signals. This article sums up the different positioning methods, and deals with the accuracy of the relative navigation system (RelNav). Furthermore, the article considers the factors that influence the positioning accuracy. For this task, a computer simulation was created to evaluate the accuracy of the telemetric method. Next, we discuss the principles of telemetry and algorithms for calculating the position of the flying object (FO).

Download Full-text

Using machine learning to analyze air traffic management actions: Ground delay program case study

Transportation Research Part E Logistics and Transportation Review ◽

10.1016/j.tre.2019.09.012 ◽

2019 ◽

Vol 131 ◽

pp. 80-95 ◽

Cited By ~ 4

Author(s):

Yulin Liu ◽

Yi Liu ◽

Mark Hansen ◽

Alexey Pozdnukhov ◽

Danqing Zhang

Keyword(s):

Machine Learning ◽

Traffic Management ◽

Air Traffic Management ◽

Air Traffic ◽

Management Actions ◽

Ground Delay Program ◽

Ground Delay

Download Full-text

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

Aerospace ◽

10.3390/aerospace5040103 ◽

2018 ◽

Vol 5 (4) ◽

pp. 103 ◽

Cited By ~ 12

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.

Download Full-text

Air traffic management for commercial and military systems integrating human factors in air traffic control research and acquisition

Proceedings. The 21st Digital Avionics Systems Conference ◽

10.1109/dasc.2002.1067927 ◽

2003 ◽

Cited By ~ 1

Author(s):

P. Krois ◽

J.T. Rehmann

Keyword(s):

Human Factors ◽

Traffic Control ◽

Traffic Management ◽

Air Traffic Control ◽

Air Traffic Management ◽

Air Traffic ◽

Control Research ◽

Military Systems

Download Full-text

A Method for Predicting Air Traffic Network Flow by Short-Term Probability

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.1500 ◽

2015 ◽

Vol 713-715 ◽

pp. 1500-1503

Author(s):

Jin Feng Kong ◽

Yu Wang

Keyword(s):

Traffic Flow ◽

Traffic Control ◽

Network Flow ◽

Traffic Management ◽

Air Traffic Management ◽

Air Traffic ◽

Main Task ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Probability Prediction

The main task of air traffic management is making adjustments when traffic flow reaches to the maximum capacity of air traffic control. It aims to increase the use ratio of airport and airspace. Air traffic flow prediction is one important part of air traffic management. Usually, air traffic prediction is divided into deterministic flow prediction and probability prediction .We research on the probability prediction on the basis of deterministic flow prediction. By analyzing aircrafts’ experienced flight time distribution, giving experienced distribution function and computing method. The data analysis shows that the accuracy rate increased by 22%.

Download Full-text

Operating in the North Atlantic MNPS Airspace

Journal of Navigation ◽

10.1017/s0373463398008091 ◽

1999 ◽

Vol 52 (1) ◽

pp. 11-27

Author(s):

Conor Whelan

Keyword(s):

North Atlantic ◽

Traffic Control ◽

Traffic Management ◽

Air Traffic Management ◽

Air Traffic ◽

Performance Specification ◽

The North ◽

Fuel Burn ◽

The North Atlantic ◽

Reporting Procedures

This paper considers the issue of operating aircraft through the North Atlantic's Minimum Navigation Performance Specification (MNPS) airspace. Noting that aircraft constantly strive for reduced fuel burn and uplift, it describes how flight operators and pilots conduct safe, efficient flights through the region. Reference is made to mechanisms of the North Atlantic MNPS airspace in terms of its Organized Track Structure and other routes that exist. These different structures emphasize the level of flexibility available. Flight planning procedures and requirements necessary to obtain oceanic Air Traffic Control (ATC) clearances are mentioned, as is an account of how communication and position reporting procedures operate to apply the Mach Number technique. Other aspects of MNPS operations such as ETOPS operational restrictions, meteorological effects, the employment of Reduced Vertical Separation Minima and planned regional changes aim to provide an overview of the MNPS system's current and future air traffic management.

Download Full-text