We present a software prototype (SafeNcy) capable of automatically choosing and ranking landing sites for emergency situations and of generating four-dimensional (4D) trajectories towards these sites. We describe the modules composing this framework, together with their main capabilities, interactions and the workflow of the full integrated system. Different types of emergencies are firstly categorized. Then, for each type of emergency, landing sites—including off-airport locations—are ranked, and speed and vertical trajectory descent profiles are tailored accordingly. These algorithms take into account several data from different sources, such as terrain databases, weather forecasts and aircraft performance models. We outline a new concept of operations aiming to integrate SafeNcy into the current aircraft operations and air traffic management paradigms. Several scenarios, focusing on total engine flame-out situations, are described and used to validate the framework, as well as to show its main features. The scenarios were designed in cooperation with a group of expert pilots and engineers. SafeNcy is expected to be an additional function for advanced and extended flight management systems, alleviating flight crew’s workload and contributing to a more digital cockpit. It could also be a technical enabler for future unmanned or highly-automated aviation.

Aircraft trajectory is one of the most fundamental objects in air traffic management. Its optimization is essential to ensure efficient and sustainable aviation. This survey proposes to study all the phases of a flight, from its prediction several days before day of flight to the landing of the aircraft, including also the study of a possible emergency situation. Each phase of flight raises different issues and is subject to particular constraints. These guide the choice of potentially usable optimization methods. This study proposes, from the context, the issues, and existing studies, a methodology to identify the most appropriate solution algorithms for optimizing each phase of flight. This methodology is based on 5 evaluation criteria: optimality, computing time, adaptability, memory usage, and multi-trajectories. Finally, thanks to it, some methods are compared based on their consistency with solving problem associated to each phase of flight.

The air traffic control paradigm is shifting from sector-based operations to flow-centric approaches to overcome sectors’ geographical limits. Modeling and predicting intersecting air traffic flows can assist controllers in flow coordination under the flow-centric paradigm. This paper proposes a flow-centric framework – TEMPT: Text-Enriched air traffic flow Modeling and Prediction using Transformers – to identify, represent, and predict intersecting flows in the airspace. Firstly, nominal flow intersections (NFI) are identified through hierarchical clustering of flight trajectory intersections. A flow pattern consistency-based graph analytics approach is proposed to determine the number of NFIs. Secondly, in contrast to the traditional traffic flow feature representation, i.e., numerical time series of flights, this paper proposes a text-enriched flow feature representation to intuitively describe the “flow of flights” in the airspace. More specifically, air traffic flow features are described by a “text paragraph” composed of the time and flight sequences transiting through the NFIs. Finally, a transformer neural network model is adopted to learn the text-enriched flow features and predict the future traffic demand at the NFIs during future time windows. An experimental study was carried out in French airspace to validate the efficacy of TEMPT using one-month ADS-B data in December 2019. Prediction results show that TEMPT outperforms the competitive air traffic flow modeling and prediction approaches: time-series-based Transformers, Long Short-term Memory (LSTM), and Graph Convolutional Networks (GCN), as well as aerodynamic trajectory simulation-based prediction and the historical average.

The continuous increase in air traffic compels major airports to optimize their resources and enhance their Terminal Maneuvering Airspace (TMA) operations. The primary constraint to increasing airport capacity is the required separation minima between pairs of aircraft arriving through the same approach routes. RECAT-EU is a revised global wake separation minima scheme released by EUROCONTROL in 2018. This suggested a reduction in separation minima between certain aircraft pairs while maintaining safety levels. However, it increases in separation minima scheme complexity by doubling the number of non-minimum radar separation (MRS) values. In this work, a Meta-Heuristic based optimization model is proposed to sequence arrival flights based on the RECAT-EU separation scheme, which can provide an optimized vectoring to ensure flights absorb their assigned delays before reaching the final approach fix. Findings show that the proposed model is able to generate an optimized landing sequence for 50 arrival flights in a computation time of 16 seconds. It also suggests that the proposed algorithm's computational time increases linearly with increasing the number of flights. Furthermore, trajectory vectoring results demonstrate that 85% of the assigned delays could be absorbed by flying the proposed vectored trajectories.

V2X holds enormous potential in augmenting road traffic safety by broadcasting information such as the position and velocity of the vehicle to others, rendering itself visible to the network even if it is occluded or still far away. However, regular broadcasting of information by many stations may heavily impact the V2X channel. This paper presents a novel algorithm and message format, Spherical Codec, to help compress trajectory data. The codec introduces new data transmission schemes, adaptable to different implementations. By leveraging inference on the functional domain, it is possible to reduce the transmission frequency while sacrificing a similar amount of accuracy to the current ITS-G5 redundancy mitigation standard. As a numerical experiment, the Gaussian-based version of the codec demonstrated a reduction of up to 2 times improvement in total bytes sent, halved average channel loads.

The number of vehicles on the road equipped with radars has been increasing for years and is estimated to reach 50% of cars by 2030. This rapid increase in radars will greatly increase the risk of harmful interference, especially since the radar waveform parameters are not heavily regulated within the 76-81 GHz band. Techniques for interference mitigation are thus becoming very important to ensure the correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. As presented in our previous study, by using a 4G, 5G or IEEE 802.11p/bd network, V2X technology can greatly help to mitigate interference by communicating radars physical properties and waveform parameters to surrounding vehicles. This data can be used to anticipate potential interferer and adapt FMCW radar parameters accordingly, but finding the perfect set of parameters can be difficult due to the lack of standardization of the bandwidth usage. To ease parameters selection, this article investigates the benefit of implementing a common channel access policy to split the bandwidth into orthogonal waveform and proposes a new strategy based on the radar orientation to mitigate interference.

This paper addresses the design of conflict free trajectories for sets of flights crossing areas subject to wind and meteorological hazards. After presenting the collaborative process based on first come first served principle, a dynamic Fast Marching Tree Star algorithm is proposed. The algorithm first computes initial avoidance trajectories and then updates them according to the new position of the hazard areas. Smart samplings are also proposed to reduce as much as possible the computing times. Indeed, the method generates in less than 1 second avoidance trajectories and guarantees safety. Then, a discussion on this method is conducted and shows its inequity. First simulations illustrate the proposed solution approach with promising results. However, to enhance equity between aircraft, the method is improved through a fairer collaborative process.

The COVID-19 pandemic has profoundly affected the air transportation system, its structure, its reliability, and its dynamics. Passengers have in turn significantly adapted their behavior. Through a case study at Paris-Charles de Gaulle airport, the present paper examines the new traffic network, the fact that delays remain high despite a drop in flight volume, the significant decrease in aircraft load factors and the change in passenger behavior at the airport.

Airport capacity has become a constraint in the air transportation networks due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum potential conflicts. In this framework, airspace together with ground airport operations is considered. The conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways and as capacity overloads for taxiway network and terminals. The methodology proposed in this paper consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution, while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, and this is supported by a case study of Paris Charles de Gaulle Airport.

International audience

Air traffic across the North Atlantic airspace has witnessed an incessant increase over the last decades. However, the efficiency of trans-Atlantic air traffic management is still low nowadays due to the limited radar coverage. Automated Dependent Surveillance-Broadcast systems represents an opportunity to enhance the strategic flight planning over the oceans by reducing separation standards between aircraft. Besides, the strong winds present a challenge for oceanic flights. Therefore, flying on the wind-optimal routes will save significantly both fuel and time. In this paper, we propose a new trans-Atlantic route structure that benefits from the jetstreams in order to construct wind-optimal flight trajectories. Then, we introduce an optimization model for detecting and resolving conflicts. The analysis is carried out on real traffic data to prove the efficiency of the proposed method. Experimental findings show an improvement in terms of conflict resolution and induced delays.

The North Atlantic Airspace (NAT) accommodates traffic between Europe and North America. This area is considered as the most congested oceanic airspace in the world. Radar-Based surveillance is not applied in the most of the oceanic area due to its limited coverage. So, aircraft become obliged to follow predefined routes called Organized Track System (OTS). These routes require very restrictive separation standards which limit the traffic of aircraft. Thus, a new kind of communication system, called Automated Dependence Surveillance Broadcast (ADS-B), has been introduced in order to afford the aircraft a reliable communication with both controllers and surrounding traffic. Hence, aircraft crossing the NAT will be able to follow more flexible routes, which will improve significantly the air traffic situation over this area. In this paper, we propose a strategic planning1model that overcomes the constraints of the OTS system in order to produce the closest routes to the direct ones of aircraft. This method is based on flocking boid model. It provides us with satisfying results on a portion of one day traffic over the NAT airspace.

In this paper, an algorithm to plan a continuous wind-optimal path is proposed, and simulations are made for aircraft trajectories. We consider a mobile which can move in a two dimensional space. The mobile is controlled only by the heading direction, the speed of the mobile is assumed to be constant. The objective is to plan the optimal path avoiding obstacles and taking into account wind currents. The algorithm is based on Ordered Upwind Method which gives an optimality proof for the solution. The algorithm is then extended to spherical coordinates in order to be able to handle long paths.

North Atlantic oceanic airspace accommodates air traffic between North America and Europe. Radar-based surveillance is not applicable in this vast and highly congested airspace. For conflict-free flight progress, the organized track system is established in the North Atlantic and flights are prescribed to follow predefined oceanic tracks. Rerouting of aircraft from one track to another is very rarely applied because of large separation standards. As a result, aircraft often follow routes that are not optimal in view of their departure and destination points. This leads to an increase in aircraft cruising time and congestion level in continental airspace at input and output. Implementing new technologies and airborne-based control procedures will enable a significant decrease in the present separation standards and improvement of the traffic situation in the North Atlantic. The aim of the present study is to show the benefits that can be expected from such a reduction of separation standards. Optimal conflict-free trajectories are constructed for several flight sets based on the new proposed separation standards, with respect to the flight input data and oceanic winds. This paper introduces a mathematical model, proposes an optimization formulation of the problem, constructs two test problems based on real air-traffic data, and presents very encouraging results of simulations for these data.

Accurate wind magnitude and direction estimation is essential for aircraft trajectory prediction. For instance, based on these data, one may compute entry and exit times from a sector or detect potential conflict between aircraft. Since the flight path has to be computed and updated on real time for such applications, wind information has to be available in real time too.The wind data which are currently available through meteorological service broadcast suffer from small measurement rate with respect to location and time. In this paper, a new wind estimation method based on radar track measures is proposed. When on board true air speed measures are available, a linear model is developed for which a Kalman filter is used to produce high quality wind estimate. When only aircraft position measures are available, an observability analysis shows that wind may be estimated only if trajectories have one or two turns depending of the number of aircraft located in a given area. Based on this observability conditions, closed forms of the wind has been developed for the one and two aircraft cases. By this mean, each aircraft can be seen as a wind sensor when it is turning. After performing evaluations in realistic frameworks, our approach is able to estimate the wind vectors accurately.

International audience

This paper presents a new air traffic route generator based on genetic algorithms. Due to traffic growth, direct (and near direct) routes are becoming increasingly congested and there is a real need for spreading traffic on new alternative routes. Those routes have to be different from several operational criteria and must not generate too much extra distance compared to the direct route. To reach this goal, a GA has been implemented with efficient sharing which automatically allows the emergence of different alternative routes. This algorithm has been tried on French air space and gives realistic operational results.

In the past, the first way to reduce the congestion of the air traffic control system was to modify the structure of the airspace in order to increase the capacity (increasing the number of runways, increasing the number of sectors by reducing their size). This method has a limit due to the cost involved by new runways and the way to manage traffic in too small sectors (a controller needs a minimum amount of airspace to solve conflicts). The other way to reduce congestion is to modify the flight plans in order to adapt the demand to the available capacity. So, to reduce congestion, demand has to be spread in spatial and time dimension (route-slot allocation). Our research addresses the general time-route assignment problem using a static and a dynamic approach. A state of the art of the existing methods shows that this general bi-allocation problem is usually partially treated and the whole problem remains unsolved due to the induced complexity. GAs are then adapted to the problem.