A Complexity Indicator for 4D Flight Trajectories Based on Conflict Probability

In this paper, a complexity indicator for 4D flight trajectories is developed based on conflict probability. A 4D trajectory is modeled as piecewise linear segments connected by waypoints. The position of each aircraft is modeled as a 2D Gaussian random variable and an approximation of the conflict probability between two aircraft is deduced analytically over each segment. Based on such conflict probability, a complexity indicator is constructed for the whole trajectory. Numerical examples show that the proposed complexity indicator is able to reflect the complexity of 4D trajectories perceived by air traffic controllers

A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy

Functional Modelling of the Air Traffic Control and the Integration Perspectives of the Integrated Services

Aviation is the sector within transportation which manages the most data during operation. Air traffic control has a specific position, because it needs a significant amount of data in order to fulfil its tasks and its complex processes require close cooperation between the organizational units within the whole sector. These data are provided by the industry partners on mandatory base, in their own interest, in good quality. This article explores the structure and organization of air traffic control, the functions fulfilled during the operation of the organization and the managed data connected to these functions as well as the strategic possibilities of forming integrated solutions. Necessary information for the tasks can be identified by the functional modelling of air traffic control. The elaborated model provides the basis for the establishment of complex content provider systems which can manage information regarding air traffic control and even aviation related data jointly. The application of these systems contributes to the increase of the efficiency of traffic operations and the more economical operations even in case of air or ground based aviation organizations

Traffic flow prediction for UTM application: a deep learning approach

Over the past few years, the research community has focused greatly on predicting air traffic flows, yielding remarkable outcomes. We found that existing literature in the field mainly covers prediction of air traffic flows for conventional aircraft. However, there is limited research about prediction of air traffic flows for Uncrewed Aircraft Traffic Management (UTM). This research study proposes a deep learning-based approach to predict air traffic congestion in the context of UTM over a period of three minutes. The use of the model aims to address congestion considering air traffic uncertainties instead of addressing the conventional issues of trajectory prediction or conflict detection and resolution. Our model also considers the influence of recreational users who fly UAVs at random times, during the execution of the above essential missions. Further, the effects of airspace structure configurations like static No-Fly Zones (NFZ), airfields with variable availability for drone flights, recreational areas, emergency UTM operation and environmental factors such as weather conditions have also been studied. The proposed model shows better performance compared to other approaches such as the Shallow neural networks and regression models

Assessing fuel burn inefficiencies in oceanic airspace

Increasing the efficiency of aircraft operations offers a shorter term solution to decreasing aircraft fuel burn than fleet replacement. By estimating the current airspace inefficiency, we can get an idea of the upper limit of savings. Oceanic airspace presents a unique opportunity for savings due to increased separation differences vs. overland flight. We assess fuel burn inefficiency by comparing estimated fuel burn for real world flights with the estimated optimal fuel burn. For computing fuel burn, we use the Base of Aircraft Data (BADA) with corrections based on research by Yoder (2005). Our fuel burn results show general agreement with Yoder’s results. Optimal operation depends on flying 4-D trajectories that use the least amount of fuel. We decompose optimal 4-D trajectories into vertical and horizontal components and analyze the inefficiencies of each separately. We use the concept of Specific Ground Range [Jensen, 2011], to find optimal altitudes and speeds. We combine the optimal altitudes and speeds with an aircraft proximity algorithm to find pairs of aircraft in a vertical blocking situations. To find the fuel optimal horizontal track in a wind field, we use methods from the field of Optimal Control. The original problem formulation can be transformed into a Two Point Boundary Value problem which we solve using MATLAB’s bvp4c function. From our set of flights, we hypothesized a scenario where aircraft stack in such a way that they cannot climb to their optimal altitudes because of separations standards. Using aircraft positions we find when aircraft were within separation standards and were blocked from climbing or descending to their optimal altitude. We split our inefficiency results into a blocked and non-blocked set to see if blocking had an effect on mean inefficiency. Our set of flights consisted of real world flights that flew through WATRS and CEP airspace regions during the month of April 2016. Using the optimal altitude for actual flight Mach profiles, we compute a mean inefficiency of 4.75% in WATRS and 4.50% in CEP, both of which are roughly 2 to 2.5 percentage points higher than studies using proprietary performance models and data. BADA overestimates optimal altitudes, leading to an overestimate in inefficiency. Inefficiency due to off-optimal speed for WATRS is 2.18% vs. 1.86% in CEP. Blocking events result in a 2.59 percentage point increase in mean inefficiency due to off-optimal altitude in WATRS flights, and a 1.21 percentage point increase in mean inefficiency due to off-optimal altitude in CEP flights. Using wind-optimal horizontal tracks gave a 1.24% mean inefficiency in WATRS, and a 0.41% mean inefficiency in CEP. The results indicate that, in total, flights through WATRS and CEP have approximately the same inefficiency due to off-optimal altitudes, but that blocking effects are more prevalent in WATRS. In addition, flights through WATRS are farther from their wind-optimal horizontal tracks than flights in CEP

Traffic Network Identification Using Trajectory Intersection Clustering

The current airspace route system consists mainly of pre-defined routes with a low number of intersections to facilitate air traffic controllers to oversee the traffic. Our aim is a method to create an artificial and reliable route network based on planned or as-flown trajectories. The application possibilities of the resulting network are manifold, reaching from the assessment of new air traffic management (ATM) strategies or historical data to a basis for simulation systems. Trajectories are defined as sequences of common points at intersections with other trajectories. All common points of a traffic sample are clustered, and, after further optimization, the cluster centers are used as nodes in the new main-flow network. To build almost-realistic flight trajectories based on this network, additional parameters such as speed and altitude are added to the nodes and the possibility to take detours into account to avoid congested areas is introduced. As optimization criteria, the trajectory length and the structural complexity of the main-flow system are used. Based on these criteria, we develop a new cost function for the optimization process. In addition, we show how different traffic situations are covered by the network. To illustrate the capabilities of our approach, traffic is exemplarily divided into separate classes and class-dependent parameters are assigned. Applied to two real traffic scenarios, the approach was able to emulate the underlying route systems with a difference in median trajectory length of 0.2%, resp. 0.5% compared to the original routes

Toward air traffic complexity assessment in new generation air traffic management systems

International audienceThe characterization of complex air traffic situations is an important issue in air traffic management (ATM). Within the current ground-based ATM system, complexity metrics have been introduced with the goal of evaluating the difficulty experienced by air traffic controllers in guaranteeing the appropriate aircraft separation in a sector. The rapid increase in air travel demand calls for new generation ATM systems that can safely and efficiently handle higher levels of traffic. To this purpose, part of the responsibility for separation maintenance will be delegated to the aircraft, and trajectory management functions will be further automated and distributed. The evolution toward an autonomous aircraft framework envisages new tasks where assessing complexity may be valuable and requires a whole new perspective in the definition of suitable complexity metrics. This paper presents a critical analysis of the existing approaches for modeling and predicting air traffic complexity, examining their portability to autonomous ATM systems. Possible applications and related requirements will be discussed