Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the ...burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this article, we first form an aviation Big Data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long short-term memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.
Air traffic management (ATM) is facing a tremendous increase in the amount of available flight data, particularly four-dimensional (4D) trajectories. Computational requirements for analysis and ...storage of such bulk of data are steeply increasing. Compression is one key technology to address this challenge. In this paper we propose two techniques for compressing air traffic 4D trajectories. Our first technique analyzes a set of samples and computes a prediction for the most likely picked successor coordinate by a random walker. The second technique, i.e., referential compression, compresses a 4D trajectory as a collection of subtrajectory pointers into a reference trajectory. We evaluate our algorithms on trajectory data from the Demand Data Repository provided by EUROCONTROL. We show that a combination of our referential and statistical compression techniques compresses 4D trajectories of all air traffic over Europe in the year 2013 from 60 GB down to 0.78 GB, achieving a compression ratio of more than 75 : 1. The compression ratio for our techniques increases with the number of to-be-compressed flights, whereas standard compression techniques achieve a fixed compressed ratio for any number of flights. Our work contributes toward efficient handling of the increasing amount of traffic data in ATM.
•An innovative collaborative air traffic flow management framework is introduced in the scope of the future trajectory based operations paradigm.•An interactive trajectory design process is proposed ...between the airspace users and the air traffic flow management authority aligned with the collaborative decision-making mechanisms.•An integrated linear optimization model is presented, which incorporates all the possible options resulted from the previous interactive trajectory design process.•Real-world operational data are collected and processed in a case study to provide a realistic assessment of the framework for a typical operational day.
This paper proposes a collaborative air traffic flow management (ATFM) framework, in the scope of trajectory based operations, aiming to improve the cost-efficiency for airspace users (AUs) when facing ATFM regulations. The framework consists of four modules. The first one involves the AUs initially scheduling their preferred trajectories for all their flights. Based on this initial demand, the second module (assumed to be on the Network Manager -NM- side) detects time-varying hotspots (i.e. overloaded sectors along the day). In the third module, hotspot information is shared back to the AUs who plan alternative trajectory options to avoid crossing these congested airspace volumes (in the lateral and vertical domain); as well as providing to the NM different pre-tactical delay management preferences (including ground holding, linear holding, air holding and pre-tactical delay recovery); based on their internal cost breakdown structures. Incorporating all these potential combined options, the last module computes the best trajectory selections and the optimal distribution of delay assignments, such that the cost deviation from the initial status (all the user-preferred trajectories) is minimized. This model is formulated as mixed integer linear programming (MILP) and validated by a real-world case study focused on 24 h of traffic over the French airspace. Results using the proposed framework suggest a significant system delay reduction by nearly 97% over the existing method, whilst yielding an average of less than 100 kg extra fuel consumption and 50 Euro extra route charges for the 11% flights diverted to their alternative trajectories.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
One of the most important issues facing both the American and European air traffic management systems will be to provide enough capacity to meet increasing air traffic demand while maintaining a high ...level of safety. In this work, we focus on the final approach flight phase and, in particular, on the missed approach procedure, which is conducted when unexpected occurrences force the aircrew to abort the landing. The current missed approach consists in recommencing the approach from the initial approach fix and, in some cases, it is just an outbound heading to wait for air traffic control instructions. In this article, we propose and evaluate the performance of a new missed approach procedure supported by current state-of-the-art communication, navigation, and surveillance technology. Our proposal, referred to as aircraft reinjection system, basically consists in identifying a gap in the inbound traffic flow, in accordance with the International Civil Aviation Organization standards, to reinject the affected aircraft and providing a new route to redirect the aircraft to that mentioned gap. Its evaluation proves that this proposal can achieve important time savings, helping to reduce the use of active runways, with the corresponding positive impact on the environment, noise, and pollution. Furthermore, and more importantly, it may enhance safety levels when the aircrew performs an unstable approach on final since they will be more willing and trained to perform an up-to-date and time-sensitive procedure rather than current old-fashioned ones.
Today, aircraft demand is exceeding the capacity of the Air Traffic Control (ATC) system. As a result, airspace is becoming a very complex environment to control. The complexity of airspace is thus ...closely related to the workload of controllers and is a topic of great interest. The major concern is that variables that are related to complexity are currently recognised, but there is still a debate about how to define complexity. This paper attempts to define which variables determine airspace complexity. To do so, a novel methodology based on the use of machine learning models is used. In this way, it tries to overcome one of the main disadvantages of the current complexity models: the subjectivity of the models based on expert opinion. This study has determined that the main indicator that defines complexity is the number of aircraft in the sector, together with the occupancy of the traffic flows and the vertical distribution of aircraft. This research can help numerous studies on both air traffic complexity assessment and Air Traffic Controller (ATCO) workload studies. This model can also help to study the behaviour of air traffic and to verify that there is symmetry in structure and the origin of the complexity in the different ATC sectors. This would have a great benefit on ATM, as it would allow progress to be made in solving the existing capacity problem.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large ...quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.
Fast simulation technology is very important to explore the consequences of air traffic management decision. Although a variety of simulation tools have been developed to make decisions for air ...traffic controllers, the cognitive process of air traffic controllers is not effectively integrated into these tools. This article studies a probabilistic cellular automata (CA) model for air traffic flow simulation. By introducing the safety distance parameter into the CA model, the equilibrium property of air traffic system is studiedusing the statistical physics method. The simulation results show that the data are in good agreement with the measured results.
We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is ...prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions.
Full text
Available for:
CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
PDF
