Optimization-Based Autonomous Air Traffic Control for Airspace Capacity Improvement

The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic. To address this issue, the application of artificial intelligence (AI) in supporting ATCOs is a promising approach. In this work, we build an AI system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' preferences, and an AI agent based on reinforcement learning to suggest conflict resolutions capturing those preferences. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions, which could help increase the ATCOs' acceptance rate of the agent's suggested resolutions. Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.

With growing air traffic density, air-traffic controllers (ATCOs) are facing more challenges in interpreting and analyzing air traffic information. As one of the solutions to this problem, automation supports such as tactile human computer interface, interactive 3D radar displays, and conflict resolution aid (CRA) are proposed for the enhancement of the current air traffic control (ATC) systems. To evaluate the proposed ATC systems, questionnaires are commonly used to get the feedback from ATCOs. However, the questionnaires are usually administered upon completion of each ATC simulation task thus provide only overall ratings towards the new ATC systems. In this paper, we propose and implement a novel Electroencephalogram (EEG)-based neurocognitive tools for evaluation of ATC systems. The nature of EEG-based technique is that the brain states can be monitored in a high resolution time, fitting the nature of time-critical ATC tasks. Thus, such EEG-based human factors study allows for real-time monitoring of ATCOs' mental workload during the task performance in ATC systems. We designed and conducted an experiment to evaluate the costs and benefits of the CRA and tactile user interface in future ATC systems. Thirty six participants participated in the experiment and were assigned into three groups of different display modes: Non-Display, Display, and Trajectory Prediction. In each group, three CRA conditions were given: Manual, Reliable and Unreliable. The EEG data were recorded during the tasks, and the traditional workload evaluation method NASA Task Load Index (TLX) was administered at the end of each task. The result shows that the ratings obtained from NASA TLX and from the EEG labeling are highly correlated. Thus, the EEG-based system is reliable to recognize workload during the task performance. With the proposed EEG-based system, we found that 1) relatively high workload was observed at the beginning of the experiment in each group which could be due to participants' unfamiliarity with the interfaces; 2) participants in the trajectory prediction group had much higher workload as compared to vertical display and non-display groups, which could be attributed to its complexity. The changes of workload are due to interaction between the type of display modes and time (p<0.05); 3) workload varied slightly with different CRA setting. The results show that the proposed EEG-based system for human factors study can provide better understanding of real-time mental workload changes during the task performance in new ATC systems, therefore enables the evaluation of current and future ATC systems.

Air transportation is very important link in economic development of any country because it provides key interface between aviation sector and the rest of the transport network. Due to open sky policy (deregulation/ liberalization), availability of small and regional aircraft, the air transport contributes in improving the regional accessibility and overall economy of the nations. Often it has been observed that the airlines are over estimating or underestimating the passenger demand. Further, the airport authorities have believed that the air traffic estimation (both passenger and aircraft demand) needs to be done on scientific methods. In view of this, various available forecasting tools for arriving air transport demand (both passenger and aircraft demand) and network analysis and optimization are essentially discussed. This paper mainly focused on the issues that impact the air transport system and trends in analysis of such a system. The literature review indicates that there have been research efforts to address air transport but attempted each issue/ aspect/ theme/ cluster of it separately or in isolation and methods are developed. This research paper proposes an approach methodology to address the air transport in a holistic/ comprehensive manner(air transport perspectives and demand analysis and modelling which includes air passenger demand, determination of suitable aircraft size, estimation of fleet requirement and air network structure configuration) using the available data and could able to validate the analysis results obtained with the help of empirical formulae and heuristic approaches thus aimed at contribution to research and academic pursuits in the area of air transport and the challenges associated with it.

A path object is a set of instructions plus the values of associated parameters that would be used by an aircraft’s flight management system (FMS) or area navigation (RNAV) computer to construct a flight trajectory on the basis of the values of the parameters provided by the pilot or air traffic control (ATC) system. The concept of path objects and its applicability for development of FMS- or RNAV-based flight paths for aircraft routes are described. The use of path objects requires only a small number of parameters for specification of an RNAV route, and the computer then calculates the resulting flight path. Because most RNAV routes are just variations of the same basic shapes, many RNAV routes can be generated from a single path object by changing just one or two parameters. This offers the capability to reduce the size of the navigation database by a significant amount. This also allows the dynamic alteration of three-dimensional FMS-RNAV routes instead of radar vectoring, which represents a significant improvement in ATC procedures. Because of the compact expressions for the path objects, these procedures could be used in a voice communications environment as well as a data link environment. A path object can be thought of as a high-level language with which aircraft and ATC systems communicate flight path intentions. The concept of FMS- or RNAV-stored path objects is a change in thinking about the role of avionics technology. It offers a means of including the precision of the FMS-RNAV technology in the ATC system during the transition from today’s system to the future’s fully automated control system. This is an enabling technology that improves the ability to use and maintain FMS-RNAV in its role as a vital component of the ATC system. It has the potential to enhance future air traffic management-communication navigation surveillance concepts and applications.

Liberalization, regional economic development and the geography of demand for air transport in the European Union

Aviation performance depends on education and training of pilots and air traffic controllers (ATCOs) on technical, procedural, crisis management, decision making, leadership and communication skills. Recent incidents and studies, however, show crew members may be caught unaware and errors in human judgement can still occur in air traffic control (ATC) systems even more so in the presence of cyberattacks. This paper focuses on raising situation awareness and decision making of ATCOs with cyberattacks impacting operations in next-generation ATC systems. Our goal is to investigate crew-based cyber security as a layer in making ATC systems resilient to cyber and cyber-physical attacks. Automatic Dependent Surveillance Broadcast (ADS-B) technology is used as the basis of our preliminary investigation due to its widely known security concerns and central role in ATC systems. We present experimental study considerations for assessing cyber readiness and improving training of ATCOs. We propose a Kalman filter based cyber alarm solution approach for a machine learning based aid for ATCOs towards early cyberattack detection and incident response in ATC systems.

Preliminary analysis has indicated that a digital data broadcast system (DDBS) concept could be applied as a potential solution to the problems of cockpit workload, pilot blunders, and airborne data storage when considered in terms of the utilization of area navigation (RNAV) within our National Airspace System (NAS). The basic philosophy of the program described in this paper was to evaluate concurrently both the operational impact of the DDBS concept under a set of flight evaluations and the technical feasibility of a DDBS engineering model. The principal conclusion of this program substantiates and amplifies the original goals of the DDBS development effort, namely, a reduction of cockpit workload, pilot blunders, and steering errors. Statement of the Problem O NE of the identifiable elements of the Upgraded Third Generation (UG3RD) Air Traffic Control (ATC) system currently under development by the Federal Aviation Administration is the concept of area navigation (RNAV). Previous reports13 have shown that the implementation of RNAV into the National Airspace System (NAS) would result in a major operational and economic improvement in both the air carrier and general aviation sectors, as well as to the ATC system itself. Airspace capacity and controller productivity would be demonstrably increased, and significant fuel and

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

The identification of backup radio sites, to increase the availability of the future air/ground (A/G) air traffic control (ATC) systems, is investigated, When there is an A/G communications failure, due to a radio site or an ATC controller access failure, another site (or sites) provides the service. The backup site should be capable of providing full coverage of the desired airspace known as the service volume. Meeting the full coverage conditions by one site or a group of sites is analyzed. It is concluded that identification of one site providing full coverage can be determined easily. The identification of a group of sites, each with partial coverage and providing full coverage is analyzed in detail. An algorithm involving both analytical and numerical approaches is described. It determines if two radio sites with given geographical coordinates, and each with partial coverage of a given cylindrical service volume, can provide full coverage. This algorithm can be implemented as a computer program to process a database of radio sites.

During the 13th Five-Year Plan period, the rapid growth of air traffic flow, so that the air traffic control industry is facing great pressure. The contradiction between flight flow support capability and airspace capacity is increasingly prominent, North China is under increasing pressure to secure flights. The existing air traffic control service as the core of the operation management mode is no longer competent. A relatively independent, professional and systematic flow management system covering all aspects of flight operation is an inevitable requirement to ensure the safe, orderly and efficient operation of large-scale air transport system. From the perspective of flow management system construction, this paper expounds the construction background, construction content and technical innovation direction of the project. Air traffic control departments make full use of new technologies, new equipment and new control means to continuously tap the control potential and improve the comprehensive support capacity of air traffic control through scientific methods.

Air traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.

The increasing volume of traffic in the air transportation is leading to excessive workload on air traffic controllers. Developing automated air traffic management (ATM) tools is a critical technology in reducing the workload of air traffic controllers and hence increasing the airspace capacity. The existing approaches to automated ATM either use overly-simplified air traffic and aircraft dynamics models to reduce computational complexity or end up being computationally intractable for large-scale ATM scenarios. This paper presents a new hybrid system description of modeling the decision process of the air traffic controllers in en-route and approach operations. The model is based on the domain expertise provided by the State Airport Authority and Air Navigation Service Provider (ANSP) of Turkey. The emulation of air traffic controller decision process in the hybrid model provides realistic conflict resolution maneuvers and separation assurance in 3D, while being computationally tractable. The algorithm has polynomial iteration complexity in the number of waypoints of the aircraft, which makes it scalable to large-scale ATM scenarios with more than 100 aircraft. The algorithm is validated on the real air traffic data over the Istanbul region extracted from the ALLFT+ dataset provided by EUROCONTROL, which includes over 7000 flights in a 96-hour period. The developed algorithm is also integrated into a Boeing 737-800 flight deck simulator with a custom radar display to demonstrate the applicability to existing avionics systems.

Based on a long-term prediction by the International Civil Aviation Organization indicating steady increases in air traffic demand throughout the world, the workloads of air traffic controllers are expected to continuously increase. Air traffic control and management (ATC/M) includes the processing of various unstructured composite data along with the real-time visualization of aircraft data. To prepare for future air traffic, research and development intended to effectively present various complex navigation data to air traffic controllers is necessary. This paper presents a mixed reality-based air traffic control system for the improvement of and support for air traffic controllers’ workflow using mixed reality technology that is effective for the delivery of information such as complex navigation data. The existing control systems involve difficulties in information access and interpretation. Therefore, taking notice of the necessity for the integration of air traffic control systems, this study presents the mixed reality (MR) system, which is a new approach, that enables the control of air traffic in interactive environments. This system is provided in a form usable in actual operational environments with a head-mounted see-through display installed with a controller to enable more structured work support. In addition, since this system can be controlled first-hand by air traffic controllers, it provides a new experience through improved work efficiency and productivity.

The main objective of Air Traffic Control (ATC) systems is to maintain safe separation between aircrafts by providing proper instructions to pilots. ATC automation is a component of ATC systems, which involves with the processing and display of operational information. In order to improve performance of ATC, operational condition evaluation is important. A critical index for evaluating ATC automation system is reliability. In this paper, a safety evaluation index system for ATC automation system considering reliability index is proposed on the basis of the survey of failure records from several airports in recent years. The final evaluation index is calculated using analytic hierarchy process (AHP).

In anticipation of a projected rise in demand for air transportation, NASA and the FAA are researching new air-traffic-management (ATM) concepts that fall under the paradigm known broadly as :free flight. This paper documents the software development and engineering efforts in progress by Seagull Technology, to develop a free-flight simulation (FFSIM) that is intended to help NASA researchers test mature-state concepts for free flight, otherwise referred to in this paper as distributed air / ground traffic management (DAG TM). Under development is a distributed, human-in-the-loop simulation tool that is comprehensive in its consideration of current and envisioned communication, navigation and surveillance (CNS) components, and will allow evaluation of critical air and ground traffic management technologies from an overall systems perspective. The FFSIM infrastructure is designed to incorporate all three major components of the ATM triad: aircraft flight decks, air traffic control (ATC), and (eventually) airline operational control (AOC) centers.