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International audience; Air traffic management (ATM) systems around the world are being modernized to accommodate shifts towards performance-and trajectory-based operations. These shifts will require new indices for safety, efficiency and complexity. The authors have been developing an index for evaluating air traffic control (ATC) difficulty that utilizes the relative positions and velocity vectors of aircraft pairs as input data. Prior to practical application of the index, it is necessary to understand the effects of input data error, i.e. errors in the positions and velocities of a pair of aircraft, on the estimated difficulty value. Two sensitivity analyses were therefore performed for a pair of aircraft cruising at constant speeds on intersecting linear tracks at the same altitude. Sensitivity analysis examines how uncertainty in inputs relates to uncertainty in outputs. Firstly, an analysis of propagation error was carried out. The formula of the propagation error at a certain point was derived based on the assumed input error, and the distribution of propagation error was investigated for all possible situations and compared with the distribution of difficulty values to clarify its characteristics. Secondly, a sensitivity analysis based on variance was carried out that evaluated the effect of each input parameter using a conditional variance value called the Sobol indices. Using a Monte Carlo method, we investigated the effect of each input parameter on the calculated difficulty value for all possible situations of aircraft pairs on intersecting trajectories. As a result, it was found that the parameter that most affects the difficulty value is the intersection angle of the trajectories.

Air traffic controllers have to make quick decisions to keep air traffic safe. Their behaviors have a significant impact on the operation of the air traffic management (ATM) system. Automation tools have enhanced the ATM system’s capability by reducing the controller’s task-load. Much attention has been devoted to developing advanced automation in the last decade. However, less is known about the impact of automation on the behaviors of air traffic controllers. Here, we empirically tested the effects of three levels of automation—including manual, attention-guided, and automated—as well as varying traffic levels on eye movements, situation awareness and mental workload. The results showed that there are significant differences in the gaze and saccade behaviors between the attention-guided group and automated group. Traffic affected eye movements under the manual mode or under the attention-guided mode, but had no effect on eye movements under the automated mode. The results also supported the use of automation for enhancing situation awareness while reducing mental workload. Our work has potential implications for the design of automation and operation procedures.

International audience; n high-density Urban Air Mobility (UAM) operations, reducing congestion and structural constraints are key challenges. Pioneering urban airspace design projects expect the air vehicles to fit into structured UAM corridor networks. However, most existing air transport networks are not capable of handling the increasing traÿc demand, which is likely to cause congestion, traÿc complexity, and safety issues. To adapt the increasing demand to the current airspace capacity, a novel macroscopic traÿc assignment model is proposed to mitigate the congestion and organize the structure of air traÿc flow. Firstly, the UAM corridor is designed and fitted into graph representation. Then, a traÿc assignment problem based on Linear Dynamical System (LDS) is formalized to minimize the congestion factors and the intrinsic air traÿc complexity. A two-step resolution method based on Dafermos’ algorithm is introduced to eÿciently solve this optimization problem. A case study is carried out on a two-layer air transport network with intensive UAM operations. The results demonstrate that the proposed model can successfully mitigate urban airspace congestion and organize the UAM traÿc into a low-complexity flow pattern. This approach can be used as a tool to assist Air Navigation Service Provider (ANSP) in strategic planning for a given transportation network.

In this paper, a complete tool for real-time detection of atypical energy behaviors of airplanes is presented. The methodology extends in real time an existing offline process using Dubins trajectories as a predictor of the remaining distance to the runway threshold. Two major contributions are presented in this paper. First, a real-time measure of the aircraft energy behaviour is defined, indicating whether the aircraft is in good condition to intercept the extended runway centreline from its current position. Secondly, a 2D trajectory suggestion is given, allowing safe management of the approach path according to atypical criteria of historical data. Finally, this document proposes a comprehensive tool for air traffic controllers, which is a major step forward in understanding, becoming aware of and resolving critical situations that could lead to accidents.

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts, however, it reveals the need for additional runways.

International audience; 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 the potential conflicts. In this framework, airspace together with ground airport operations are considered. 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 work 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, this is supported by a case study of Paris Charles de Gaulle Airport.

This paper introduces a novel approach for synchronised demand-capacity balancing within a proposed Collaborative Air Traffic Flow Management framework. The approach is aimed to realise optimising traffic flow and scheduling airspace configuration in a more harmonised manner. Options such as delay assignment and alternative trajectories (generated and submitted by Airspace Users) are intended for regulating the traffic flow. Airspace reconfiguration involves, on the other side, adjusting the opening schemes of predefined configurations, or creating new ones (if needed) through dynamic sectorisation. Results suggest that, using the proposed approach, the required system delay can be reduced remarkably, whereas the number of opened sectors (and thus operating cost) and the total capacity provision (supplied by the Air Navigation Service Providers) decrease at the same time, due to the increased capacity utilisation per operating sector.

This paper presents a new challenge on the strategic 4D trajectory optimization problem with the evaluation of air traffic complexity by using the geometric-based intrinsic complexity measure called König metric. The demonstration of König metric shows the potential that the algorithm can capture the disorganized traffic which represents the difficulty of maintaining situational awareness as expected by the air traffic controller. We reformulate the optimization problem with two trajectory separation approaches including delaying flight departure time and allocating the new flight level subject to limited delay time of departure, limited changes of flight levels and fuel consumption constraints. We propose our solution to solve daily traffic demands in the regional French airspace. The resolution process uses the distributed metaheuristic algorithm to optimize aircraft trajectories in 4D environment with the objective of finding the optimal air traffic complexity. The experimental results shows the reduction of maximum complexity more than 95% with average delay of 2.69 minutes. The optimized trajectories can save fuel more than 80000 kg. The proposed algorithm not only reduces the air traffic complexity but also maintain its distribution in traffic. The research results represent further steps towards taking other trajectory separations methods and aircraft trajectory uncertainties into account, developing our approach at the continental scale as well as adapting it in the pre-tactical and tactical planning phase. Civil Aviation Authority of Singapore (CAAS) Accepted version This research is partially supported by NTU-CAAS Research Grant M4062429.052 by Air Traffic Management Research Institute, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.

The continued increase of air traffic, which doubles every 15 years, has produced economic benefits but poses environmental issues that put at risk the sustainable development of air transport. Factors such as jet fuel prices volatility, the introduction of new environmental regulations and in- tense competition in the airline industry incentive the research on ight e_ciency topics. Despite fuel costs in descent and approach phases are minor than those in cruise phase, flight crew high-workload during these phases often lead to energy mismanagement resulting in inefficient flight operations. This paper proposes an optimization algorithm that generates fuel-efficient arrival trajectories accounting for the current aircraft position. Results show 13% fuel savings compared with a best-in-class Flight Management System and, at the same time, descent time is reduced by 1%. A preliminary assessment has been conducted in the ight simulator with the aim of validating the operational feasibility of the computed trajectory. Data post-analysis suggest that the trajectory is flyable with current flight controls and guidance laws although idle margins shall be readjusted to ac- count for accelerations during descent. This paper may constitute a solid background for the likely development of flight efficiency functions for the future generation of avionic systems.

International audience; The increased number of daily flights in the last decades has led to the densification of major airports and the proliferation of air traffic delays. The approach phase is inherently complex and labor extensive for flight crews, whose mission is to follow a stabilized approach. Current flight management systems do not provide much help in situations of high energy, where crews apply challenging energy management techniques to land in the destination runway. Go-around procedures shall be initiated as soon as the aircraft is not at the correct energy condition at the stabilization gate, despite the increase of workload this type of operation induces for crews and air traffic controllers. The continuation of a nonstabilized approach may put at peril the safety of flight. This paper presents an algorithm that computes the trajectory that stabilizes the aircraft in the minimum distance, which is also known as the high-energy-limit trajectory. The provision of this trajectory contributes to raising flight crew awareness of the aircraft energy state, and it informs that stabilization is not possible by giving the minimum distance to follow a stabilized approach. Flight simulator results helped to assess the operational concept and improve the representativeness of real flight operations. The calculation of the trajectory on a real-time basis provides flight crews with useful information of the aircraft energy condition, which improves flight safety and may ultimately reduce the number of nonstabilized approaches and go-around procedures.

International audience; With the continuous air traffic growth and limits of resources, there is a need for reducing the congestion of the airspace systems. Nowadays, several projects are launched, aimed at modernizing the global air transportation system and air traffic management. In recent years, special interest has been paid to the solution of the dynamic airspace configuration problem. Airspace sector configurations need to be dynamically adjusted to provide maximum efficiency and flexibility in response to changing weather and traffic conditions. The main objective of this work is to automatically adapt the airspace configurations according to the evolution of traffic. In order to reach this objective, the airspace is considered to be divided into predefined 3D airspace blocks which have to be grouped or ungrouped depending on the traffic situation. The airspace structure is represented as a graph and each airspace configuration is created using a graph partitioning technique. We 2 optimize airspace configurations using a genetic algorithm. The developed algorithm generates a sequence of sector configurations for one day of operation with the minimized controller workload. The overall methodology is implemented and successfully tested with air traffic data taken for one day and for several different airspace control areas of Europe.

Best Student Paper Award; International audience; This paper aims at investigating further the use of the social media Twitter as a real-time estimator of the US Air Transportation system. Two different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested on the first two months of 2018 for the estimation of air traffic delays at departure and arrival at 34 different US airports. Using three different levels of content-related features created from the flow of social media posts led to the extraction of useful information about the current state of the air traffic system. The resulting methods yield higher estimation performances than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data for more than 28 airports. Moreover the features extracted can also be used to start a passenger-centric analysis of the Air Transportation system. This paper is the continuation of previous works focusing on estimating air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources as an estimator of the current state of the different actors of the air transportation system in real-time.

International audience; Aircraft approach flight path safety management provides procedures that guide the aircraft to intercept the final approach axis and runway slope before landing. In order to detect atypical behavior, this paper explores the use of data generative models to learn real approach flight path probability distributions and identify flights that do not follow these distributions. Through the use of Generative Adversarial Networks (GAN), a GAN is first trained to learn real flight paths, generating new flights from learned distributions. Experiments show that the new generated flights follow realistic patterns. Unlike trajectories generated by physical models, the proposed technique, only based on past flight data, is able to account for external factors such as Air Traffic Control (ATC) orders, pilot behavior or meteorological phenomena. Next, the trained GAN is used to identify abnormal trajectories and compare the results with a clustering technique combined with a functional principal component analysis. The results show that reported non compliant trajectories are relevant.

Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach, using a computational framework, where the two networks can inter- act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module (for capacity), Collision risk estimation module (for safety) and an air traffic simulation module (for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies (random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow (flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk. Keywords: Airport network, Airspace capacity, Airspace safety, Collision risk, Flow intensity

International audience; The North Atlantic oceanic airspace is considered the most congested oceanic airspace in the world. For many years, air traffic control in this airspace has experienced difficulties due to the limited radar coverage. To support conflict-free flight progress, a structure of routes, called the organized track system, is established in the North Atlantic airspace and very restrictive separation standards are applied. The development of the automated dependent surveillance-broadcast system provides an opportunity to improve the flight planning operations over the oceans by reducing separation norms. The aim of this study is to improve the traffic efficiency in the North Atlantic airspace by developing new approaches to organize the transatlantic traffic at the strategic level. The first considered approach proposes a new route structure, referred to as the wind-optimal track network, to replace the organized track system, whereas the second one is based on the wind-optimal free routes. The problem is modeled as an optimization problem and resolved further via simulated annealing combined with a sliding window algorithm. The results of the simulations performed on the real traffic data prove that about 76% of flights decrease their cruising times by more than half of an hour when flying wind-optimal tracks rather than using their great circle routes from the departure to the destination. Furthermore, by comparing the two proposed methods, it is concluded that using wind-optimal free routes implies lower cruising times, whereas the wind-optimal track network is much more robust under changing wind fields.

Air Traffic Management (ATM) is a complex decision-making process. Air traffic controllers’ decision on aircraft trajectory control actions directly leads to the efficiency of traffic flow management. In the Automated Point Merge Trajectory Planning (APMTP) problem, it aims to realize an automated routine trajectory management in Terminal Manoeuvring Area (TMA) with an intelligent decision-making agent.An Artificial Intelligence-based approach, mainly Reinforcement Learning (RL) algorithm, is applied to adaptively and smartly integrate four types of de-conflict actions for solving conflicts with fewer delays on the environment. In this paper, we will mainly discuss the policy optimization in APMTP, focus on improving the agent’s learning quality and exploration efficiency. Firstly,application of RL in adaptive trajectory planning is presented.APMTP problem is adaptively divided into several sub-problems.For each sub-problem, an online policy π is applied to guide the simulation and optimization modules to find out the conflict free and less-delay solution. The online policy π is a scale of weight distribution for choosing desirable actions. It follows the rule of Roulette-wheel selection with weighted probability. The highest desirable decision variable has the largest share of the roulette wheel, while the lowest desirable decision variable has the smallest share of the roulette wheel. The RL direct policy optimization algorithm is designed to update the online policy π.Finally, experiments are built up for validation of the proposed policy optimization algorithm for the intelligent decision-making in APMTP. The results in the test environment show that learning agent with different exploration and exploitation ability will result in different system performance in conflict resolution and delay Refereed/Peer-reviewed

International audience; Aircraft are able to connect any pair of points on earth by using direct routing thanks to the flight management system (FMS) which is the on-board system in charge of the navigation. From an air traffic control point of view, such direct routing is only possible in low density areas. When demand is high, aircraft have to stay on a route network for which crossing points are well located and do not change with time. This location stability, helps controllers to manage efficiently the traffic and ensure a higher capacity compared with management of the same traffic with direct routes for which crossing points always change in space and in time dimensions. This instability reduces the overall capacity of sectors. During the night, when traffic demand decreases, air traffic controllers often deliver direct routes to aircraft because they know that such aircraft will not interfere with other traffic. During the day, such direct route assignments are not proposed anymore and the controller keeps the aircraft on the route network in order to structure the traffic and to reduce the associated complexity. In this research we propose an intermediate milestone for which direct routes could be proposed to aircraft during the day when it is possible and this in order to reduce also the congestion at some points in the airspace. We then consider a real crossing in the French airspace where aircraft have to merge at the same point and exit toward different directions. An optimization algorithm has been developed for this crossing in order to minimize the workload of the controller in charge of this airspace. Three decision variables are then assigned to each aircraft: speed regulation, flight level setting and direct route setting. The associated objectives of this problem are the minimization of the conflict between aircraft (has to reach zero), the maximization of the number of aircraft having a direct route (avoiding the crossing) and the minimization of the flight deviation (speed and FL). When a direct is given to an aircraft, we must ensure that such aircraft is separated from other aircraft by one when they share the same spatial location; this ensure that the controller in charge of such aircraft has few monitoring workload for this aircraft (because it has no interaction with other traffic). This optimization must satisfy some constraints: speed and FL have to stay in a given range. This algorithm has been applied successfully to a test case quite similar to one located in the west of France ACC (Brest) for which the capacity of the crossing point has been strongly increased thanks to many direct routes that have been given.

International audience; Air Traffic has evolved over the years with current commercial fleet expected to double in the near future, increasing airspace congestion. Jet fuel prices volatility, market competition and drones integrating the same airspace require a modernization of the Air Transportation System. In this context, Airbus proposes modern avionics and cockpit designs to fulfil these requirements. Flight Management System (FMS), in-service since the early 1980s, is one of these candidate avionic systems to be revisited. During high workload flight phases as descent and approach are, best-in-class FMSs compute a fixed vertical reference trajectory based on a lateral flight plan and applicable procedures. Then, flight guidance is responsible for tactical replanning so that deviations are absorbed. FMS reference profile is constructed through a series of hypotheses, which could be enhanced by taking into account other factors such as aircraft optimal energy repartition, ATC restrictions, surrounding traffic, wind errors and mass biases. This paper proposes and compares two methodologies based on a modified version of A* algorithm that solves the Optimal Control Problem in the vertical plane. Fixed and variable speed aircraft trajectories are compared in order to quantify the benefits with respect to current FMS design. The problem is formulated through a relaxed point-mass model with a real performance database for a modern commercial aircraft. It accounts for flight constraints as well as aircraft dynamics for trajectory generation. Fuel consumption is optimized without excessively penalizing flight time. These trajectories are compared with those generated by Airbus FMS simulator. Results show that, for modern arrival procedures such as those defined for Continuous Descent Operations, aircraft energy management (potential and kinetic) is enhanced, producing continuously idle trajectories that consume up to 30 % less fuel than current operations, as obtained for this particular procedure. In that case, flight time would be stretched by few minutes yielding to 15% extra time, whose trade-off still seems interesting from the airline perspective.

This paper was partially funded by grants from the CSC No 201506830050 and by the SESAR Joint Undertaking under grant agreement No 699338. —This paper proposes a method to synchronize traffic flow optimization and sector opening scheduling, with the aim of achieving flexible demand and capacity balancing (DCB). Delay assignment, trajectory options and sector collapsing are used to manage the traffic demand, while sector opening schemes are to affect the airspace capacity. Mixed Integer Programming (MIP) model is built to incorporate these initiatives. Three model variants are presented to illustrate the synchronization process, and their results in a real-world case study demonstrate some promising improvements for the DCB performances. Index Terms—air traffic flow management, trajectory options, dynamic sectorization, demand and capacity balancing

International audience; Functional airspace blocks (FAB) concept is adopted by the European airspace to allows cooperation between airspace users to manage the air trac ow, while ensuring eciency, safety, and fairness without the constraints of geographical boundaries. This integration of airspaces allow for exibility in airspace management and aircraft tra-jectory planning. This paper proposes a distributed air-trac ow management model to address four-dimensional (4D) trajectory planning over the European FAB. The proposed method is based on a meta-heuristic approach which uses a hybrid algorithm of simulated annealing and hill-climbing local search to separate a given set of aircraft trajectories in space and time domain (we term this as ight interaction), by allocating an alternative ight plan (route and departure time) to each ight. An innovative data structure, termed as FAB-Flight interaction matrix, captures the ight interaction in-1 Associate Professor, School of Mechanical and Aerospace Engineering, 1 formation between and within FABs. The proposed distributed model is implemented and tested with two air trac data sets comprising of 4,000 ights (3 hours trac) and 26,000 ights (one full day trac data over the European airspace). The performance of the model is then compared with a centralized air-trac ow management model on scalability and interaction minimization. Results indicates that, though both the approaches were able to achieve interaction-free trajectory planning within computational time acceptable for the operational context, distributed model converges faster to interaction-free solution as trac size increases; which shows the viability of the distributed model for eective FAB implementation.

International audience; To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each aircraft at a nation-wide scale.In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time.The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French airspace for which a conflict-free and robust 4D trajectory plan is produced.

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.

International audience; To sustain the continuously increasing air traffic demand, the future air traffic management system will rely on a so-called trajectory based operations concept that will increase air traffic capacity by reducing the controllers’ workload. This will be achieved by transferring tactical conflict detection and resolution tasks to the strategic planning phase. In this future air traffic management paradigm context, this paper presents a methodology to address such trajectory planning at nation-wide and continent scale. The pro-posed methodology aims at minimizing the global interaction between aircraft trajectories by allocating alternative departure times, alternative horizontal flight paths, and alternative flight levels to the trajectories involved in the interaction. To improve robustness of the strategic trajectory planning, un-certainty of aircraft position and aircraft arrival time to any given position on the trajectory are considered. This paper presents a mathematical formulation of this strategic trajectory planning problem leading to a mixed-integer optimization problem, whose objective function relies on the new concept of interaction between trajectories. A computationally efficient algorithm to compute interaction between trajectories for large-scale applications is presented and implemented. Resolution method based on hybrid-metaheuristic algorithm have been developed to solve the above large-scale optimization problems. Finally, the overall methodology is implemented and tested with real air traffic data taking into account uncertainty over the French and the European airspace, involving more than 30,000 trajectories. Conflict-free and robust 4D trajectory planning are produced within computational time acceptable for the operation context, which shows the viability of the approach.

This article won two awards : Best of Session (SE/TFM) Award Best Undergraduate Student Paper Award; International audience; Air Traffic Flow Management (ATFM) aims at structuring traffic in order to reduce congestion in airspace. Congestion being linked to aircraft located at the same position at the same time, ATFM organizes traffic in the spatial dimension (e.g. route network) and/or in the time dimension (sequencing and merging in TMA, Miles-in-Trail for en-route airspace). The objective of this paper is to develop a methodology that allows the traffic to self-organize in the time dimension when demand is high. This structure disappears when the demand diminishes. In order to reach this goal, a multi-agent system has been developed. In this system, aircraft agents regulate speed and delay departure time in order to reduce the number of conflicts, thus decreases overall traffic complexity, which becomes easier to manage by air traffic controllers. This algorithm was applied on realistic examples.

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 planning 1 model 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.

International audience; To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this paper presents a strategic 4D aircraft trajectory planning approach aiming at minimizing interaction between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each flight at a country and a continent scale. Uncertainties of aircraft position and arrival time on its curvilinear abscissa are taken into account in the trajectory planning process. The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the interaction between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French and the European airspace for which a conflict-free and robust 4D trajectory plan is produced.

International audience; .The sharp increase in air traffic flow causes traffic congestion in airspaces near airports, called Terminal Maneuvering Areas (TMA). The departure and arrival traffic of airports follow pre-designed routes named Standard Instrument Departure (SID) routes and Standard Terminal Arrival Routes (STAR). Optimizing these routes is crucial to regulate air traffic. Currently, SIDs and STARs are designed manually, based on the airport layout and nearby constraints. The objective of this research is to propose a methodology for designing an arrival/departure route in TMA, taking into account some constraints including obstacle avoidance. The shape of a route in horizontal plan is a succession of arcs of circles and segments. The originality of our study is, on the one hand, that the horizontal route is associated with a cone in vertical plan enveloping all ascent (or descent) aircraft profiles, and on the other hand, a branching strategy in a Branch and Bound (B\&B) framework tailored on the problem is proposed.

International audience; This paper describes a parallel approach for computing an air traffic complexity metric that is based on dynamical systems. It is important in order to quantify the complexity and geometric features of traffic. Lyapunov exponents are used for understanding the geometry of current air traffic in the airspace. It is computed at different points on a spatial grid. Those values can indicate areas where air traffic controllers must pay more attention because the complexity is higher at those points. A parallel implementation of the air traffic complexity metric computation is introduced in this paper. Serial implementation of this problem is too slow for real world problems. This process is parallelizable because Lyapunov exponent values can be computed simultaneously for each grid point in the airspace.

International audience; Pricing policies could encourage airline companies to modify departure times and routes of their flights in order to reach a route-slot allocation target that minimizes the en-route congestion. The problem of restricting the number of price levels and the assignment of one price level to each sector at each time period is studied using a simulation based on a Logit discrete choice model. In this model, an option is defined as the combination of a departure time and a route. For each flight, a utility is associated to each option and takes into account the flying cost, the cost of ground delay and the prices of crossed sectors. The optimization of the pricing policy considers average flows and minimizes the quadratic difference between desired and expected flows on each option. A heuristic algorithm that involves successive iterations of simulated annealing and gradient methods performs this optimization. The simulated annealing assigns a price level to each sector at each time period, and the gradient algorithm computes new values of price levels. The test of the method on constructed examples indicates that the use of only four price levels does not significantly deteriorate the performance of the system with respect to the use of independent prices.

Field observations and focused interviews of Air Traffic Controllers have been used to generate a list of key complexity factors in Air Traffic Control. The underlying structure of the airspace was identified as relevant in many of the factors. A preliminary investigation has revealed that the structure appears to form the basis for

International audience; 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.

International audience; This paper considers the problem of short to mid-term aircraft trajectory prediction, that is, the estimation of where an aircraft will be located over a 10-30 min time horizon. Such a problem is central in decision support tools, especially in conflict detection and resolution algorithms. It also appears when an air traffic controller observes traffic on the radar screen and tries to identify convergent aircraft, which may be in conflict in the near future. An innovative approach for aircraft trajectory prediction is presented in this paper. This approach is based on local linear functional regression that considers data preprocessing, localizing and solving linear regression using wavelet decomposition. This algorithm takes into account only past radar tracks, and does not use any physical or aeronautical parameters. This approach has been successfully applied to aircraft trajectories between several airports on the data set that is one year air traffic over France. The method is intrinsic and independent from airspace structure.

The growth of the air traffic demand leads to congestion that can be reduced by adapting the flight plans to the available capacity. A pricing policy could encourage the airline companies to modify departure times and routes. A model of the relation between taxes charged to aircraft and the choice of routes and take-off time is proposed. The difference, in terms of take-off time and route, between a target assignment and the assignment resulting from taxes is minimized. Two optimization methods are adapted to the problem and tested. The results show that the optimum can be obtained by a simple gradient method and that the pricing policy significantly reduces the difference between target and actual assignment.

International audience; Air traffic management ensures the safety of flight by optimizing flows and maintaining separation between aircraft. After giving some definitions, some typical feature of aircraft trajectories are presented. Trajectories are objects belonging to spaces with infinite dimensions. The naive way to address such problem is to sample trajectories at some regular points and to create a big vector of positions (and or speeds). In order to manipulate such objects with algorithms, one must reduce the dimension of the search space by using more efficient representations. Some dimension reduction tricks are then presented for which advantages and drawbacks are presented. Then, front propagation approaches are introduced with a focus on Fast Marching Algorithms and Ordered upwind algorithms. An example of application of such algorithm to a real instance of air traffic control problem is also given. When aircraft dynamics have to be included in the model, optimal control approaches are really efficient. We present also some application to aircraft trajectory design. Finally, we introduce some path planning techniques via natural language processing and mathematical programming.

International audience; Since air traffic volume increased over the oceanic airspaces, it has been primordial to improve oceanic air traffic management procedures. One of the most important limitations in the oceans air traffic is the lack of radar coverage. The availability of new surveillance means, called automated dependence surveillance broadcast system (ADS-B), permits to enhance the strategic flight planning over the oceans by reducing the separation standards. Besides, oceanic flights are mainly subjected to strong winds caused by the jet streams. In this work, we focus on optimizing the strategic flight planning over the North Atlantic airspace. First, we organize the traffic inside a route structure that benefits from both the Jet streams and the exploitation of ADS-B systems. Indeed, from one side, these routes are merged inside the jet streams in order to be as close as possible from wind-optimal routes. On the other side, these routes are constructed to fit in with the new separation standards required when implementing the ADS-B systems. Then, we resolve conflicts between aircraft via an optimization model based on a speed regulation. Simulations were conducted for a real traffic data. Computational findings show that the proposed methodology provides satisfying results.

International audience; In the SESAR framework (Single European Sky ATM Research), the need to increase the air traffric capacity motivated the 4D (space + time) aircraft trajectory planning. This paper deals with an important Air Traffic Management (ATM) problem that consists in generating sets of 4D conflict-free trajectories (the tactical planning problem). The Light Propagation Algorithm (LPA) was introduced in [1] to deal with this problem. LPA has recently been shown to manage successfully a full day of traffic over the French airspace, removing all conflicts while satisfying ATM constraints. In this paper, we adapt the LPA to take into account uncertainties in trajectory prediction. We introduce and test a new algorithm called u/LPA (LPA under uncertainty) on the same day of traffic. For some situations, uncertainties reduce so much the search space that the standard algorithm cannot guarantee conflict free situation. As a consequence, one must include some time constraints for few trajectories (so-called RTA points: Real Time of Arrival constraints) in order to remove the remaining conflicts. The goal of RTA points is to impose an aircraft to be at a specified position at some given time. This results into a new optimization formulation of the tactical trajectory planning problem involving the decision as to where/when RTA points should be imposed. In order to solve this new problem, here we are content with a simple heuristic that yields encouraging results.

Best presentation Award; International audience; The need for increasing air traffic capacity motivates 4D trajectory planning concept. In order to generate conflict-free 4D trajectories, we introduce a new concept based on light propagation modeling algorithm. This algorithm is a wavefront propagation method that yields a natural solution for the path planning problem specifically in the case of air traffic congestion.

International audience; Conflict resolution has always been a sensitive matter in air-traffic management. Current European projects aim partial or total automation of air traffic control to deal with the constant growth of air traffic. Technological advances on flight management system allows us to consider an automatic conflict resolution using continuous trajectories. In this paper, we present a new methodology that, first, relies on B-splines to model trajectories, secondly models air-traffic conflict resolution as an optimization problem whose decision variables are the spline control points. Finally, we use genetic algorithms to tackle this optimization problem in order to generate optimal conflict-free situations.

International audience; The growth of air traffic in future years requires a paradigm shift in the way the aircraft are controlled. Major innovative projects (SESAR in Europe, NexGen in USA) have started in order to define and implement control tools based on time-space constraints on aircraft trajectories. As a consequence, an increasing level of automation is expected. In this framework, it is of primary importance to be able to quantify the hardness to produce conflict free trajectories for a given situation and the robustness of the solution found. In this paper, a characterisation based on the Lyapunov exponents of a dynamical system interpolating the observed data will be presented. A first part will be devoted to vector field fitting, and a second one to efficient lyapunov exponents computation. Then, some practical implementation issues will be discussed.

International audience; The objectives of Air Traffic Management (ATM) system is to ensure safe and efficient operation of an individual flight as well as to maximize the overall capacity and minimize the adverse environmental impacts of the air transportation system. To achieve these objectives, airspace capacity, which drives capacity and traffic flow management policies, should be more properly estimated according to the detailed traffic configuration. There have been some efforts to describe the complexity of a traffic situation based on the idea of modeling airspace onto a dynamical system. This paper extends the previous research efforts by accounting for uncertainties on aircraft's positions and velocities. The proposed method is illustrated with examples.

International audience; This paper presents a new method for 3D cutting of geometrical space with application to airspace sectoring. This problem comes from the air traffic management but the proposed method may be applied to many other areas. This problem consists in finding a cutting of a 3D volume into sectors in order to balance the weights of sectors and which minimizes the flow cut on sector boundaries. A mathematical modeling of this problem has been proposed for which state space, objective functions and constraints are defined. The complexity of such problem being NP_Hard, stochastic optimization have been used to address it. An Evolutionary Algorithm has been implemented for which chromosome coding and operators have been developed. Realistic problem instances have been tested on this algorithm for which the solutions produced fulfill our objective.

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. A sector congestion measure has been developed which gather the major control workload indicators. This measure is then computed for each proposed planning by referring to an off-line simulation. New problem-based stochastic operators have been developed and successfully applied on real instances of the problem.

International audience; The ability of air traffic controllers to deal with complex situations is a limiting factor in airspace capacity. The underlying airspace structure and other procedural elements are thought to be important factors in reducing a controller's cognitive complexity through the use of structure based abstractions. Because cognitive complexity cannot be directly observed it must be investigated indirectly. This paper discusses and presents examples of how directly observable states and controllers' subjective responses can be used to indirectly probe and gain insight into how structure based abstractions are used to manage cognitive complexity.

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.

International audience; This paper presents new concepts to address the air traffic complexity modeling problem. Two new geometrical metrics have been introduced and have been found very useful to capture typical features of traffic complexity. The covariance metric is very adapted to identify disorder in a set of speed vectors and can be applied for en route airspace (en route airspace is the airspace between airports). Similarly, the Koenig metric identifies easily the curl movement organizations and can be applied to areas around airports where air traffic control procedures impose turns on aircraft trajectories.

International audience; This paper addresses the classical graph partitioning problem applied to the air network. We consider an air transportation network with aircraft inducing a control workload. This network has to be partitioned into K balanced sectors for which the cutting flow is minimized.