4D Trajectory Management Research Articles

Hybrid AI-based 4D trajectory management system for dense low altitude operations and Urban Air Mobility

Urban Air Mobility (UAM) has emerged as a promising solution to address some of the challenges of transportation congestion and associated pollution, especially in large cities. However, the development of drone transportation and UAM services is limited by the capacity of the low altitude airspace where these new vehicles will operate. Without suitable regulatory advancements and associated traffic management systems, air traffic in the densest low-altitude sectors may incur congestion, which, in addition to affecting operational efficiency, can increase systemic risk and fuel emergency occurrences, thereby affecting the safety of people and property in the air and on the ground. To address these challenges, this study aims to develop an intelligent Uncrewed Aircraft Traffic Management (UTM) system that leverages the complementary strengths of metaheuristic and machine learning algorithms for an effective management of dense low altitude airspace. The UTM system determines time-based three-dimensional airspace Demand-Capacity Balancing (DCB) solutions by processing real-time data updates and dynamically replanning flight paths and DCB decisions in any given context, while also providing UAM operators with relevant inputs for autonomous decision-making. Simulation-based verification activities in representative conditions show that the proposed UTM system has the ability to effectively resolve overload instances and minimize potential conflicts in low-altitude airspace, with an operationally acceptable running time. We conclude that the proposed hybrid algorithm can support a successful implementation of UAM services in and around cities, and it has high potential to address critical airspace resource constraints also in traditional Air Traffic Flow Management (ATFM).

4D-trajectory time windows: definition and uncertainty management

PurposeThe use of the 4D trajectory operational concept in the future air traffic management (ATM) system will require the aircraft to meet very accurately an arrival time over a designated checkpoint. To do this, time intervals known as time windows (TW) are defined. The purpose of this paper is to develop a methodology to characterise these TWs and to manage the uncertainty associated with the evolution of 4D trajectories.Design/methodology/approach4D trajectories are modelled using a point mass model and EUROCONTROL’s BADA methodology. The authors stochastically evaluate the variability of the parameters that influence 4D trajectories using Monte Carlo simulation. This enables the authors to delimit TWs for several checkpoints. Finally, the authors set out a causal model, based on a Bayesian network approach, to evaluate the impact of variations in fundamental parameters at the chosen checkpoints.FindingsThe initial results show that the proposed TW model limits the deviation in time to less than 27 s at the checkpoints of an en-route segment (300 NM).Practical implicationsThe objective of new trajectory-based operations is to efficiently and strategically manage the expected increase in air traffic volumes and to apply tactical interventions as a last resort only. We need new tools to support 4D trajectory management functions such as strategic and collaborative planning. The authors propose a novel approach for to ensure aircraft punctuality.Originality/valueThe main contribution of the paper is the development of a model to deal with uncertainty and to increase predictability in 4D trajectories, which are key elements of the future airspace operational environment.

Interdependent Uncertainty Handling in Trajectory Prediction

The concept of 4D trajectory management relies on the prediction of aircraft trajectories in time and space. Due to changes in atmospheric conditions and complexity of the air traffic itself, the reliable prediction of system states is an ongoing challenge. The emerging uncertainties have to be modeled properly and considered in decision support tools for efficient air traffic flow management. Therefore, the subjacent causes for uncertainties, their effects on the aircraft trajectory and their dependencies to each other must be understood in detail. Besides the atmospheric conditions as the main external cause, the aircraft itself induces uncertainties to its trajectory. In this study, a cause-and-effect model is introduced, which deals with multiple interdependent uncertainties with different stochastic behavior and their impact on trajectory prediction. The approach is applied to typical uncertainties in trajectory prediction, such as the actual take-off mass, non-constant true air speeds, and uncertain weather conditions. The continuous climb profiles of those disturbed trajectories are successfully predicted. In general, our approach is applicable to all sources of quantifiable interdependent uncertainties. Therewith, ground-based trajectory prediction can be improved and a successful implementation of trajectory-based operations in the European air traffic system can be advanced.

Adaptive prediction of flight time uncertainty for ground-based 4D trajectory management

An adaptive prediction model of level flight time uncertainty is derived as a function of flight and meteorological conditions, and its effectiveness for ground-based 4D trajectory management is discussed. Flight time uncertainty inevitably increases because of fluctuations in meteorological conditions, even though the Mach number, flight altitude and direction are controlled constant. Actual flight data collected using the secondary surveillance radar Mode S and numerical weather forecasts are processed to obtain a large collection of flight time error and flight and meteorological conditions. Through the law of uncertainty propagation, an adaptive prediction model of flight time uncertainty is derived as a function of the Mach number, flight distance, wind, and temperature. The coefficients of the adaptive prediction model is determined through cluster analysis and linear regression analysis. It is clearly demonstrated that the proposed adaptive prediction model can estimate the flight time uncertainty without underestimation or overestimation, even under moderate or severe weather conditions. The proposed adaptive prediction is able to improve both safety and efficiency of 4D trajectory management simultaneously.

Arrival Flow Integration Technique of the Point Merge

This paper show some simulation about the Point Merge in this paper. Some new methods of Integrating arrival flows with existing technology. The paper shows that even under high traffic load, Its advantages in the typical terminal area configurations These studies indicate the inhere benefits of Point Merge in flexibility and scalability. The paper also shows that Point Merge could be a basis of 4D trajectory management.

Designing for shared cognition in air traffic management

It is to be expected that the task of an air traffic controller will change with the introduction of four-dimensional (space and time) trajectories for aircraft, as can be seen in ongoing developments in ATM systems in Europe (SESAR) and the US (NextGen). It is clear that higher levels of automation will need to be developed to support the management of four-dimensional trajectories, but a definite concept on a distribution of the roles of automation and human users has not yet been well defined. This paper presents one approach to the design of a shared representation for 4D trajectory management. The design is based on the Cognitive Systems Engineering framework and by using a formative approach in the analysis of the work domain, a step-wise refinement in the planning and execution of 4D trajectories is proposed. The design is described in three Abstraction Hierarchies, one for each phase in the refinement. The ultimate goal is to design a shared representation that underlies both the design of the human-machine interface and the rationale that guides the automation. It is foreseen that such a shared representation will greatly benefit the shared cognition in ATM and allows shifting back and forth across various levels of automation. A preliminary version of a joint cognitive system for 4D trajectory management has been developed and will be introduced in this paper. Further work will focus on the refinement of the shared representation by means of human-in-the-loop experiments.

4D trajectory management using contract of objectives

Contract of Objectives (CoO) has been designed in the context of trajectory-based Air Traffic Management (ATM), using mutually agreed objectives between Air Traffic Control (ATC), airlines and airports. This paper provides an overview of the assessment of CoO and discusses the results of the three human-in-the-loop (HIL) evaluations of the concept of operations. Measurements were collected and analysed on system performance (i.e. safety, efficiency and capacity) and human performance (i.e. workload, situation awareness and acceptability) for the human actors involved. Findings indicate that controllers and pilots are positive about the concept of operations, and that they agree on the principle of flying what was “planned, agreed and negotiated” during the planning phase, as opposed to the current “first come, first served” approach. The renegotiation of CoO was assessed as manageable, even with a 2020 traffic load, without any impact on safety. They all recognise that implementation of CoO increases the collaboration between crew and ground, as they share not only the same data but also the same robust objective all through the flight, i.e. the objective determined at strategic level through a collaborative decision-making process (CDM). Some improvements are still needed regarding the HMI and the generation of Target Windows (TWs). The airport and airline operators, as well as the network manager, considered CoO renegotiation to be feasible and acceptable. They were interested in the CDM process proposed. They found the principle of sharing the operational data of other actors to be a great improvement in the way choices are made and validated.

Special issue: Selected papers from the Innovative Research Workshop 2010

Over nearly 10 years the Innovative Research Workshop and Exhibition held annually in early December at the EUROCONTROL Experimental Centre in Bretigny-sur-Orge, France, has brought together researchers and practitioners from air traffic management and related areas. The workshop focused specifically on innovative research and has continuously attracted inspiring new ideas, studies and concepts. The 2010 edition of the INO Workshop was a notable event (not least because snow kept the attendants locked into the centre one evening) and we have decided to dedicate a special edition of the Journal of Aerospace Operations to it. We have selected what we believe are the most interesting and innovative amongst the papers submitted for presentation at the conference and invited the authors to prepare an extended version of their original manuscript. There are seven articles in this edition which we are sure the reader will find stimulating and rewarding. “A quantitative exploration of flight prioritisation principles, using new delay costs” by Andrew Cook and Graham Tanner proposes a systematic approach to assessing the cost of delays for airlines and how these findings should be applied to flight prioritisation procedures instead of the simple application of delay minutes for optimisation purposes. “Analysis of the ecological and economic impact of a Single European Sky by simulating Freeflight trajectories with Lido/Flight” by Andreas Henn, Urban Weishaar and Philipp Bock proposes a method for quantifying the fuel saving potential of user preferred trajectories as compared to the present trajectories in the European airspace. The application of the flight planning software presently used by Lufthansa and several other airlines to a historic data set shows a fuel saving potential of more than 3% which translates to economic and ecological benefits alike. “4D trajectory management using Contract of Objectives” by Sandrine Guibert and Laurent Guichard describes a series of experiments carried out in order to assess the benefits and usability of the

A CD&CR causal model based on path shortening/path stretching techniques

Some attempts have been done to alleviate the airspace congestion in Terminal Manoeuvring Area (TMA): reduced vertical separation minima, negotiation of voluntary reductions in scheduled service, construction of additional runways at major airports. However, there is still a pending matter to be solved regarding the efficiency of the airspace use, by avoiding non-efficient procedures such as holdings. In line with the requirements of the future air traffic management (ATM) concepts proposed by SESAR and NextGen, the air-traffic flow needs to be more predictable to offer the possibility of more effective use of airspace and airport capacity. In the context of SESAR, 4D trajectory management is expected to improve air traffic operations, in particular to increase the overall predictability of traffic, with benefit to airlines and air traffic management. A deep knowledge concerning the events that take place in the air traffic management of 4D trajectories and their interactions in a TMA is essential to remove non-effective operations, avoid delay propagation between arrivals and optimise the occupancy of the runway. The present paper describes a discrete event model, based on Coloured Petri-Nets formalism, useful to specify a conflict detection and conflict resolution algorithm focusing on the arrival phase of flight. The causal model developed considers different alternative pre-defined turning points for each flight by evaluating path shortening/path stretching of all trajectories upwards the assigned merging points in a TMA.