Air traffic flow management (ATFM) is the key driver of efficient aviation. It aims at balancing traffic demand against airspace capacity by scheduling aircraft, which is critical for air navigation ...service providers in delivering secure and sustainable air transport. Nowadays, the scale of scheduled aircraft grows dramatically along with the sharp increase in air traffic demand, which brings heavy pressure to efficient scheduling. Regarding safety and efficiency as two fundamental objectives of air transport, this paper proposes a cooperative co-evolutionary algorithm to solve large-scale multi-objective ATFM problems. First, a new multi-objective co-evolution framework with an evolving external archive is devised, in which the subcomponents collaborate with each other via the knee solution of the archive. Second, a novel fuzzy decomposition method is specifically designed to split the large-scale ATFM problem into small-size subcomponents by utilizing the spatiotemporal correlations of aircraft. During optimization, the proposed algorithm can continuously receive feedback from the optimization process and make the decomposition more likely better suited to the problem. Third, a new contribution-based probabilistic resource allocation mechanism is developed to automatically assign the computing resources to the unbalanced subcomponents. Finally, a test suite with different scales extracted from real air traffic data is created. Extensive experimental results show that, given the same number of fitness evaluations, the proposed algorithm significantly outperforms the state-of-the-art baselines in terms of effectiveness on all the benchmark instances.
The innovative concept of multiple remote tower operation (MRTO) is where a single air traffic controller (ATCO) provides air traffic services to two or more different airports from a geographically ...separated virtual Tower. Effective visual scanning by the air traffic controller is the main safety concern for human-computer interaction, as the aim of MRTO is a single controller performing air traffic management tasks originally carried out by up to four ATCOs, comprehensively supported by innovative technology. Thirty-two scenarios were recorded and analyzed using an eye tracking device to investigate the above safety concern and the effectiveness of multiple remote tower operations. The results demonstrated that ATCOs' visual scan patterns showed significant task related variation while performing different tasks and interacting with various interfaces on the controller's working position (CWP). ATCOs were supported by new display systems equipped with pan tilt zoom (PTZ) cameras allowing enhanced visual checking of airport surfaces and aircraft positions. Therefore, one ATCO could monitor and provide services for two airports simultaneously. The factors influencing visual attention include how the information is presented, the complexity of that information, and the characteristics of the operating environment. ATCO's attention distribution among display systems is the key human-computer interaction issue in single ATCO performing multiple monitoring tasks.
•Innovative MRT technology permits a single ATCO to perform tasks previously done by up to four air traffic controllers.•Interface design and operational environment affect visual search patterns and SA.•Effective attention distribution is the main safety concern of HCI on MRTO.•MRT technology can improve aviation safety and generate cost savings.•MRTO can improve capacity, cost-efficiency, and human performance but can also increase ATCO perceived workload.
As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex ...meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.
Abstract
Considering similar air traffic control techniques for the present based on close historical dates is a good approach due to the unpredictability of weather and air traffic, as well as to ...increase controller efficiency. A K-prototype clustering technique and grey correlation analysis are proposed to discover similar days to address the problem of similar identification. Firstly, the weather and air traffic datasets are used to create a set of features broken down into numerical and categorical attributes. Secondly, the historical data are clustered using the K-prototype clustering, which is then paired with grey correlation analysis to identify days similar to the reference day and examine the traffic management initiatives employed on that day. Finally, the research uses actual weather information and aircraft schedules from Nanjing Lukou International Airport as examples. The outcomes demonstrate that the similar days picked by the model are representative and can serve as a foundation for airport controllers' decision-making.
In the air traffic management (ATM) environment, air traffic controllers (ATCos) and flight crews, (FCs) communicate via voice to exchange different types of data such as commands, readbacks ...(confirmation of reception of the command) and information related to the air traffic environment. Speech recognition can be used in these voice exchanges to support ATCos in their work; each time a flight identification or callsign is mentioned by the controller or the pilot, the flight is recognised through automatic speech recognition (ASR) and the callsign is highlighted on the ATCo screen to increase their situational awareness and safety. This paper presents the work that is being performed within SESAR2020-founded solution PJ.10-W2-96 ASR in callsign recognition via voice by Enaire, Indra, and Crida using ASR models developed jointly by EML Speech Technology GmbH (EML) and Crida. The paper describes the ATCo speech environment and presents the main requirements impacting the design, the implementation performed, and the outcomes obtained using real operation communications and real-time simulations. The findings indicate a way forward incorporating partial recognition of callsigns and enriching the phonetization of company names to improve the recognition rates, currently set at 84–87% for controllers and 49–67% for flight crew.
The effectiveness of DVB-T based passive radar (PR) in counter drones operations is investigated in this study aiming at monitoring airport terminal areas. In particular, the authors demonstrate that ...such sensors could be effectively employed to provide simultaneous short-range surveillance against drones and long-range monitoring of aircraft from civil air traffic. To this purpose, several experimental tests have been performed with the DVB-T based AULOS® passive sensor developed by Leonardo S.p.A. using very small RCS drones as cooperative targets along with conventional air traffic as targets of opportunity. An appropriate signal processing architecture is proposed for the two search tasks to be accomplished simultaneously. This is extensively applied against the collected datasets, based on the algorithmic solutions devised by the research group at Sapienza University. The reported results clearly prove the capability of a DVB-T based PR of simultaneously detecting and localising drones flying around the airport area as well as the typical civil aircraft at longer distances.
Abstract
Introduction
The simplicity of wrist actigraphy for sleep-wake monitoring in the field contributes to its ubiquity in shift-work research. However, devices based solely on recording activity ...levels are generally not suitable to quantify sleep architecture. This is a limitation as quantifying changes in sleep stages caused by circadian misalignment is important to better assess the consequences of sleep-wake disruption in shift-working populations. This pilot study was conducted to evaluate whether sleep stages vary with respect to different shift types.
Methods
Six male air traffic controllers aged 48.5±8.4 years (mean±SD) completed the protocol which entailed two ~9-day periods, each with up to 6 workdays. Schedules comprised 1 or 2 early night shifts (19:30–03:30h), followed by an evening shift (15:00–23:00h), day shift (09:00–17:00h), morning shift (06:30–14:30h), and 1 or 2 full night shifts (23:00–7:00h). A portable sleep-staging device (Somno-Art, Paris, France) that monitored activity levels and heart rate was worn on the non-dominant forearm during bedtime and produced estimates of REM and NREM sleep stages with a proprietary algorithm. Total sleep time (TST) and sleep stages were assessed per shift type with mixed-effects models.
Results
Final analyses were based on 70 sleep periods preceding workdays, standardized to 24 h to account for the different intervals between consecutive shifts. Analyses revealed significant effects of shift type for TST (p=.016), stages N1 (p=.010) and N2 (p=.043), but none for N3 (p=.055) or REM (p=.117) sleep. TST and stage N1 sleep prior to night shifts was shorter than for day, evening, or early night shifts (all p<.05). Participants obtained less stage N2 sleep prior to night shifts than days shifts (p=.049).
Conclusion
This pilot study suggests variations in TST across shifts were predominantly due to differences in light sleep stages, whereas no significant differences in N3 and REM sleep were observed. Thus, while TST was reduced for night shifts, participants obtained similar durations of the most recuperative stages. These findings highlight the importance of refined monitoring of sleep in field research involving shift-work.
Support (if any)
Project funded by NAV Canada. Devices lent by the Somno-Art company. A.K. received a postdoctoral fellowship from the Fonds de Recherche en Santé du Québec (FRQS).
In order to address the continuing growth of demands on airspace capacity, various navigation methods have been developed such as Area Navigation (RNAV), which allows pilots and air traffic ...controllers to have a higher degree of freedom in the airspace, but at the same time, the airspace becomes more complex than ever, and maintaining the safety and efficiency becomes challenging. To develop assistant tools for such situation, this paper proposes (i) a trajectory pattern identification framework that can identify complex and diverse trajectory patterns in the RNAV terminal airspace, and (ii) a recurrent neural network-based real-time trajectory pattern classification framework that is necessary for real-time air traffic applications. The proposed frameworks are tested with the real air traffic data recorded at Incheon International Airport, South Korea, in 2019, and evaluated by predicting estimated time of arrival in a real-time manner.
•Area navigation terminal airspace has complex operations.•Trajectory patterns in the area navigation terminal airspace can now be identified.•Real-time trajectory pattern classification is achieved.•Automated air traffic assistant tools can be better developed and implemented.
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