Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

A Quasi-Experimental Approach for Assessing Air Traffic Controller Workload

The objective of this thesis was to evaluate and determine the operational impacts to the Oceanic Air Traffic Controller (controller) from deficiencies of an Oceanic Data Link system. These deficiencies in the Oceanic Data Link system are in regards to the Computer Human Interface (CHI) and its effect on the cognitive effort and physical task requirements imposed on the controller. The various workload methodologies and techniques were reviewed for specific workload techniques applicable to the operational environment when resources, such as time and funding, are lacking for a laboratory design. Data was collected from a live oceanic control facility where the Oceanic Data Link system is currently being utilized at a single sector on the control room floor. Qualitative measures were used to assess controller workload associated with performing Air Traffic Control (ATC) tasks. The data collection activities utilized the analysis of data from the NASA-Task Load Index (TLX), observation, and questionnaires. Subjective workload analysis was used and collected from eleven oceanic controllers. Analysis of the NASA-TLX revealed that the use of the Oceanic Data Link system received the highest rating in mental demand and temporal demand followed closely by frustration and effort. The Oceanic Data Link system imposes higher workload in cognitive demand rather than physical demand, but does not affect their performance

Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).</p

Measuring mental workload of air traffic controller (ATC) by using dynamic density (DD) and nasa-tlx methods (case study: airnav Indonesia Surabaya branch office)

Today’s growth of Juanda Airport air transport service is very rapid. To ensure the service quality and safety, one of most significance aspects is relied on ATC performance under AirNav Juanda management. This performance depends on appropriate workload burdened towards the ATC. Therefore, an ATC workload measurement is important to be conducted. Especially for Approach Unit (APP) Controller who has so many cognitive tasks. The tasks are prevent collision between aircrafts, expedite and maintain an orderly of air traffic flow, provide advice and information for the safe and efficient flight, and notify relevant organizations regarding aircraft in need of rescue and assist the mentioned organization if it is required. Moreover, this unit does not only handle departing and arriving flights, but also the passing through flights through its sectors. One of objective methods which represent cognitive complexity factors that mainly trigger APP Controller mental workload is Dynamic Density (DD). In the application towards AirNav Juanda, DD is supported by a widely used subjective method, NASA-TLX. It is because there is still no objective mental workload which able to precisely represent the ATC level of workload. Result of AirNav Juanda APP Controller mental workload triggering factors measurement by using DD shows top 5 weighting of the factors are S5, NUMHORIZ, SCI, SV and C15 respectively. Moreover, based on NASA-TLX assessment, 9.1% of 22 observed APP Controllers experience ‘very high’ category of mental workload, 81.8% ‘high’ and 9.1% ‘medium high’. This workload is triggered by the complexity factors as measured by DD with result of R2 for 0.60497. ================================================================================================= Dewasa ini, permintaan pelayanan transportasi udara di Bandara Juanda bertumbuh sangat pesat. Untuk memastikan kualitas dan keamanan layanannya, salah satu aspek yang paling penting adalah kinerja Air Traffic Controller (ATC) di bawah manajemen AirNav Juanda. Kinerja tersebut bergantung pada keseimbangan beban kerja yang ATC rasakan. Oleh karenanya, pengukuran beban kerja ATC merupakan hal yang penting untuk dilakukan. Terutama untuk Approach Unit (APP) Controller yang memiliki banyak tuntutan tugas kognitif. Tugas tersebut antara lain mencegah tabrakan antar pesawat, melancarkan dan menjaga keteraturan arus lalu lintas udara, menyediakan saran serta informasi untuk penerbangan yang aman dan efisien, dan mengabari organisasi terkait pertolongan pada pesawat serta mendampinginya apabila dibutuhkan. Unit ini tidak hanya menangani pesawat yang menuju atau meninggalkan Bandara Juanda, namun juga pesawat yang melintas di sektor-sektor udaranya. Salah satu metode objektif yang dapat merepresentasikan faktor kerumitan kognitif sebagai pemicu utama beban kerja mental APP Controller adalah Dynamic Density (DD). Dalam pengaplikasiannya di AirNav Juanda, DD didukung oleh sebuah metode subjektif yang telah banyak dipakai, yaitu NASATLX. Hal ini karena belum adanya metode pengukuran beban kerja mental secara objektif yang benar-benar mampu merepresentasikan beban kerja tersebut. Hasil pengukuran faktor-faktor pemicu beban kerja mental APP Controller AirNav Juanda menggunakan DD didapatkan urutan 5 faktor kompleksitas lalu lintas udara tertinggi yaitu S5, NUMHORIZ, SCI, SV, dan disusul dengan C15. Kemudian berdasar pengukuran NASA-TLX, APP Controller yang merasa beban kerjanya „sangat tinggi‟ ada 9.1%, „tinggi‟ sebanyak 81.8%, dan „menengah tinggi‟ sejumlah 9.1% dari 22 responden. Beban kerja tersebut dipicu oleh faktorfaktor kerumitan sebagaimana yang telah diukur menggunakan DD dengan R2 sekitar 0.60497

Air Traffic Complexity as a Source of Risk in ATM

In this chapter the connection between air traffic complexity and risks in air traffic management system will be explored. Air traffic complexity is often defined as difficulty of controlling a traffic situation, and it is therefore one of the drivers for air traffic controller’s workload. With more workload, the probability of air traffic controller committing an error increases, so it is necessary to be able to assess and manage air traffic complexity. Here, we will give a brief overview of air traffic complexity assessment methods, and we will put the traffic complexity assessment problem into a broader context of decision complexity. Human reliability assessment methods relevant to air traffic management will be presented and used to assess the risk of loss of separation in traffic situations with different levels of complexity. To determine the validity of the human reliability assessment method, an analysis of conflict risk will be made based on the real-time human-in-the-loop (HITL) simulations

Air Traffic Control

Improving air traffic control and air traffic management is currently one of the top priorities of the global research and development agenda. Massive, multi-billion euro programs like SESAR (Single European Sky ATM Research) in Europe and NextGen (Next Generation Air Transportation System) in the United States are on their way to create an air transportation system that meets the demands of the future. Air traffic control is a multi-disciplinary field that attracts the attention of many researchers, ranging from pure mathematicians to human factors specialists, and even in the legal and financial domains the optimization and control of air transport is extensively studied. This book, by no means intended to be a basic, formal introduction to the field, for which other textbooks are available, includes nine chapters that demonstrate the multi-disciplinary character of the air traffic control domain

Mental Workload in the Explanation of Automation Effects on ATC Performance

Automation has been introduced more and more into the role of air traffic control (ATC). As with many other areas of human activity, automation has the objective of reducing the complexity of the task so that performance is optimised and safer. However, automation can also have negative effects on cognitive processing and the performance of the controllers. In this paper, we present the progress made at AUTOPACE, a European project in which research is carried out to discover what these negative effects are and to propose measures to mitigate them. The fundamental proposal of the project is to analyse, predict, and mitigate these negative effects by assessing the complexity of ATC in relation to the mental workload experienced by the controller. Hence, a highly complex situation will be one with a high mental workload and a low complex situation will be one in which the mental workload is low

Determining Air Traffic Complexity – Challenges and Future Development

Air traffic complexity is one of the main drivers of the air traffic controllers’ workload. With the forecasted increase of air traffic, the impact of complexity on the controllers\u27 workload will be even more pronounced in the coming years. The existing models and methods for determining air traffic complexity have drawbacks and issues which are still an unsolved challenge. In this paper, an overview is given of the most relevant literature on air traffic complexity and improvements that can be done in this field. The existing issues have been tackled and new solutions have been given on how to improve the determination of air traffic complexity. A preliminary communication is given on the future development of a novel method for determining air traffic complexity with the aim of designing a new air traffic complexity model based on air traffic controller tasks. The novel method uses new solutions, such as air traffic controller tasks defined on pre-conflict resolution parameters, experiment design, static images of traffic situations and generic airspace to improve the existing air traffic complexity models.</p