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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.

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.

International audience; The runway is a key airport resource and an efficient runway operation is critical to enhance the airport efficiency and to reduce delays. This paper addresses the problem of scheduling aircraft departures incorporating arrival crossings. Constraints for wake turbulence separations, flight time window restrictions, and holding queue capacity at runway threshold are explicitly considered. We present two Integer Linear Programming (ILP) models and a simulated annealing (SA) algorithm. Comparison tests are conducted for the south side of Paris Charles De Gaulle Airport and show that the three proposed methods can significantly improve the solutions based on the simple first-come-first-served rule. Moreover, SA has sufficiently small computation time for real-time deployment.

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; Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to-optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 hours. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.

International audience; In this study, we have highlighted the main challenges of real-time fault diagnosis on small scale fixed-wing UAVs. The feasibility of real-time fault prediction has been shown in real flight conditions experiencing noisy measurements, communication limitations, and wrapped wing structure that breaks the geometric symmetry. A total of eleven flight logs have been recorded and shared publicly for future potential use by other researchers on fault and anomaly detection. Our proposed method uses a data driven algorithm, SVM, in order to classify the behavior of the vehicle in nominal flight phase and faulty phase. Feasibility of a basic binary classification is shown, despite the well-known over-fitting problem caused by limited data. We have shown that geometrical imperfections that are common in small UAVs can cause particular effects on the prediction performance, and we used it in our advantage to improve the detection on multi-class classification. The SVM algorithm with proposed feature trajectories was capable to detect variation of loss of control effectiveness faults up to an accuracy of 95% in real flights. The data-set and all related programs can be downloaded from: (https://github.com/mrtbrnz/fault_detection).

The COVID-19 pandemic has had a significant impact on the air transportation system worldwide. This paper aims at analyzing the effect of the travel restriction measures implemented during the COVID-19 pandemic from a passenger perspective on the US air transportation system. Four metrics based on data generated by passengers and airlines on social media are proposed to measure how the travel restriction measures impacted the relation between passengers and airlines in close to real-time. The proposed metrics indicate that each airline has reacted differently to the COVID-19 travel restriction measures from a passenger perspective, therefore they can be used by airlines and passengers to improve their decision making process. This report comes ahead of official data related to the same sequence of events, thereby showing the value of passenger-borne data in an industry where corporate priorities, institutional prudence, and passenger satisfaction come close together., Highlights • Passenger-generated data can be harnessed earlier than official flight data. • Four passenger-centric metrics are proposed and extracted from the Twitter flow. • These metrics can be used to improve passenger and airline decisions. • US airlines have reacted differently to COVID-19 travel restriction measures. • Each airline has its own passenger communication Twitter profile.

International audience; nce Suvarnabhumi International Airport is considered to be the biggest airport in Thailand, a big travelling-hub of southeast Asia and plays a significant part to the economy of Thailand relying on the tourism industry, an aircraft trajectory recognition is essential to support the high traffic management system from around the world. The first and essential stage of airport capacity enhancement is descriptive-analytic in several sections of the airport, including flight trajectory behaviors in order to plan an improvement procedure in the future. This experiment deploys K-mean and Gaussian Mixture clustering to compare results by using available automatic dependent surveillance-broadcast (ADS-B) dataset provided by the bigdata system from various websites. The test varies the number of clustering from three to ten and measures how similar an object is to its cluster by using the Silhouette score. Gaussian Mixture clustering produces at least three unique flight trajectories when setting the number of clustering equal to four, giving the Silhouette score of 0.43. K-mean clustering with the number of clustering equal to ten gives the highest Silhouette score of 0.45. However, its routes are not clearly recognized when compared with the Gaussian Mixture clustering. Although the overall results are not clearly shown in the pattern, it is enough to describe the trajectory patterns of the aircrafts taking off or landing over Suvarnabhumi International Airport

International audience; Network science has been a promising tool for characterizing and understanding complex systems. A challenging problem in network science is to uncover the community structure of the network. Community structure generally presents the partition of the nodes in the network into several groups based on various structural properties or dynamic behavior. In this paper, we analyze the community structure of Chinese airport network based on Stochastic Block Models (SBM). Different from exisiting studies, the Chinese Airport Delay Correlation Network (CADCN) is constructed with airports as nodes and the correlations between hourly delay time series of airport pairs as edges. To analyze the temporal patterns of community structures, we employ spectral clustering method and classify Chinese airports into 6 different communities. Airports within each community have closer relationships to each other on the delay propagation. A similar investigation to the traditional Chinese airport network (CAN) is carried out based on SBM as well. By comparing the results of two networks, we find that the CADCN has the advantage in revealing the implicit delay correlation than the directed flights connection between airports. Our findings have potential meanings to understand the spread of flight delays and to develop relevant management and control strategies.

International audience; NextGen and ACARE Flightpath 2050 set some ambitious goals for air travel, including improving the passenger travel experience using door-to-door travel times as a possible metric. Using recently released Uber data along with other online databases, a reliable estimation of door-to-door travel times is possible, which then enables a comparison of cities performance regarding the good integration of their airports as well as a per segment analysis of the full trip. This model can also be used to better evaluate where progress should and can be made with respect to air passenger travel experience.

International audience; This paper addresses the problem of estimating aircraft on-board parameters using ground surveillance available parameters. The proposed methodology consists in training supervised Neural Networks with Flight Data Records to estimate target parameters. This paper investigates the learning process upon three case study parameters: the fuel flow rate, the flap configuration, and the landing gear position. Particular attention is directed to the generalization to different aircraft types and airport approaches. From the Air Traffic Management point of view, these additional parameters enable a better understanding and awareness of aircraft behaviors. These estimations can be used to evaluate and enhance the air traffic management system performance in terms of safety and efficiency.

International audience; Trajectory prediction with Closest Point of Approach (CPA) concept is a fundamental element of aircraft Conflict Detection (CD) problem. Conventional motion-based CPA prediction model generally assumes that aircraft is flying in straight line with constant speed. But due to environment uncertainties and ground speed changes, this conventional method frequently lacks accuracy in the real world with a high rate of false alarms and missed detections. In this paper, we introduce a novel automated data-driven CD framework with Machine Learning (ML) for 3D CPA prediction in a lookahead time of less than 20 minutes. Firstly, a 3D CPA model with cylindrical norm is proposed as the baseline. Then, data preparation with Mode-S observation data in France is explained, including data collection and data processing, to convert raw Mode-S data to the close-to-reality dataset. Furthermore, feature engineering is applied to build up a feature set with 16 features. Finally, four prevailing ML models are used to predict the time, horizontal distance and vertical distance of CPA in 3D airspace. CD is conducted based on the predicted values. The prediction and CD results show that all proposed ML models outperform the baseline model. Especially, GBM and FFNNs could strongly enhance the performance of CD.

International audience; Airport security checkpoints are critical areas in airport operations. Airports have to manage an important passenger flow at these checkpoints for security reason while maintaining service quality. The cost and quality of such an activity depend on the human resource management for these security operations. An appropriate human resource management can be obtained using an estimation of the passenger flow. This paper investigates the prediction at a strategic level of the passenger flows at Paris Charles De Gaulle airport security checkpoints using machine learning techniques such as Long Short-Term Memory neural networks. The derived models are compared to the current prediction model using three different mathematical metrics. In addition, operational metrics are also designed to further analyze the performance of the obtained models.

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; Departure metering has the potential to mitigate airport surface congestion and decrease flight delays. This paper considers several candidate departure metering techniques, including a trajectory-based optimization approach using a node-link model and three aggregate queue-based approaches (a scheduler meant to represent NASA's Airspace Technology Demonstration-2 (ATD-2) logic, an optimal control approach, and a robust control approach). The outcomes of these different approaches are compared for two major airports: Paris Charles De Gaulle airport (CDG) in Europe and Charlotte Douglas International airport (CLT) in the United States. These two airports have similar aggregate demand and capacity, but the banking of demand at CLT leads to congestion, resulting in higher taxi-out delays and more potential for departure metering at CLT. Stochastic simulations are used to show that departure metering with the robust control approach best accommodates operational uncertainties, and that departure metering yields higher taxi-out time savings at CLT compared to CDG, irrespective of the approach. Nomenclature ∆t = Time step ∆v = Speed increment s = Minimum taxi separation distance V min = Minimum allowed taxi speed V max = Maximum allowed taxi speed N∆t = Maximum allowed holding time, N a for arrivals and N d for departures

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.

As environmental awareness has increased, airlines are paying attention to fuel consumption during daily flight operations with the hope of reducing consumption. Because the direction and strength of the wind affects the fuel consumption of an aircraft, airlines are seeking to minimize the adverse effects of headwinds or maximize the beneficial effect of tailwinds when planning flight trajectories. According to the jet stream profile of the world, it is easy to notice that the jet streams flow in the eastwest direction instead of the north-south direction. Therefore, the flights on east-west routes are affected more by en route winds than the flights on north-south routes. In order to achieve the best flight performance in terms of flight time and fuel consumption, airlines may adjust the flight trajectories based on en route wind profiles. Consequently, it is necessary to consider en route wind effects when planning flight trajectories. However, it is difficult to identify the most suitable trajectory in a complex wind field because such field is not easy to estimate [17], [25], [8], [9], [26]. The wind directions and strength are varying in different regions, at different altitudes, and different times. Even though the problem is complex, solving it may benefit airlines in terms of fuel cost and on-time performance.

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 in the time dimension (e.g. sequencing and merging of aircraft flows taking off or landing at airports). The objective of this paper is to develop a methodology that allows the traffic to self-organize in the time and space dimensions 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 which aircraft cooperate to structure traffic. Multi-agent systems have several advantages, including a good resilience when confronted with disruptive events. In this system, three algorithms have been implemented, aiming at reducing traffic complexity in three different ways. The first algorithm allows aircraft agents flying on a route network to regulate speed in order to reduce the number of conflicts, a conflict occurring when two aircraft do not respect separation norms. The second algorithm allows aircraft to solve conflicts when the traffic is not structured by a route network. The third algorithm creates temporary local route networks allowing to structure traffic. The three algorithms implemented in this multi-agent system allow to decrease overall traffic complexity, which becomes easier to manage by air traffic controllers. This algorithm was applied on realistic examples and was able to structure traffic in a resilient way.

International audience; Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models-the deterministic model and a model that incorporates separation buffers-are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.

International audience; The efficient design of departure and arrival routes in the airspace surrounding airports, called the Terminal Maneuvering Area (TMA), is crucial for increasing the capacity of such areas, and thus alleviating congestion around airports caused by worldwide air traffic growth. In this paper, an efficient method of designing departure and arrival routes in TMA is proposed, taking into account the configuration of the airport and nearby environment, as well as related operational constraints, such as obstacle avoidance and route separation. Each route is modeled in three dimensions (3D), and consists of two components: a curve in the horizontal plane and a cone in the vertical plane. A Branch and Bound (B&B)-based approach is developed, whose branching strategies are tailored to how the obstacles are avoided. Routes are generated sequentially, and each route is initially built in isolation. If the route is found to be in conflict with previously generated routes, it is perturbed locally around the conflict zones. Numerical tests, performed on artificially generated problems and the TMA of Paris Charles-de-Gaulle (CDG) airport, demonstrate that the proposed method is efficient and could be embedded in a decision-aid tool for procedure design.

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.

The bottleneck of today&rsquo, s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints.

International audience; Air Traffic growth requires a modernization of current Air Transportation System in order to cope with the increasing demand, and thereby its impact on the environment poses aviation development at risk. Under this context, trajectory optimization projects constitute a relevant mean to reduce fuel consumption and decrease the likelihood of go-around events as a result of a non-stabilized approach. The generation of a continuous optimal trajectory would provide flight crews with an useful tool to manage aircraft flight path or directly automate the trajectory management. This papers proposes a novel methodology based on a tailored version of A* algorithm that generates upstream optimal trajectories with the certainty of terminating at current aircraft position, and solves energy errors during the approach phase through flap and airbrakes adjustments. Three use cases are presented: two concerning the calculation of the vertical profile for a complete arrival procedure and one regarding the computation of a trajectory that dissipates the excess of energy of an aircraft located close to the runway threshold. On one hand, results show fuel savings of 6% and 12% for the analyzed case studies accompanied by a reduction of 3% descent time, which are obtained due to enhanced energy management; on the other hand, during high-energy situations the function design prioritizes safety instead of fuel optimization, and proposes a flight strategy to stabilize the aircraft energy-state before landing.

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; Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.

International audience; This paper addresses the resolution of combinatorial optimization problems presenting some kind of recurrent structure, coupled with machine learning techniques. Stemming from the assumption that such recurrent problems are the realization of an unknown generative probabilistic model, data is collected from previous resolutions of such problems and used to train a supervised learning model for multi-label classification. This model is exploited to predict a subset of decision variables to be set heuristically to a certain reference value, thus becoming fixed parameters in the original problem. The remaining variables then form a smaller sub-problem whose solution, while not guaranteed to be optimal for the original problem, can be obtained faster, offering an advantageous tool for tackling time-sensitive tasks.

ISBN: 978-1-5386-6573-2; International audience; The following paper presents an innovative approach for dealing with complex capacity problems in aviation. We introduce a sliding window framework composed by an optimization method with a simulation component. By applying this framework in diverse problems that are dependent on time it is possible to find feasible and close-to-reality solutions in shorter time than the ones that could be achieved by evaluating the problem in the complete time-horizon. The framework can be applied to solve diverse problems in aviation or similar industries. We exemplify the approach with a model of Paris Charles de Gaulle Airport in France.

This paper is an extension of work originally reported in SESAR Innovation Days 2018 : Zhengyi Wang, Man Liang, Daniel Delahaye. Automated Data-Driven Prediction on Aircraft Estimated Time of Arrival. SID 2018, 8th Sesar Innovations Days, Dec 2018, Salzburg, Austria. ⟨hal-01944608⟩; International audience; 4D trajectory prediction is the core element of the future air transportation system. It aims to improve the operational ability and the predictability of air traffic. In this paper, a novel automated data-driven framework to deal with the prediction of Estimated Time of Arrival (ETA) on the runway at the entry point of Terminal Manoeuvring Area (TMA) is introduced. The proposed framework mainly consists of data preprocessing and machine learning models. Firstly, the dataset is divided, analyzed, cleaned, and estimated. Then, the flights are clustered into partitions according to different runway-in-use (QFU). Several candidate machine learning models are trained and selected on the corresponding dataset of each QFU. Feature engineering is conducted to transform raw data into features. After that, the experiments are performed on real ADS-B data in Beijing TMA with nested cross validation. By comparing the prediction performance on the preprocessed and un-preprocessed datasets, the results demonstrate that the proposed data preprocessing is able to improve the data quality. It is also robust to outliers, missing data, and noise. Finally, an ensemble learning strategy named stacking is introduced. Compared to other individual models, the stacked model has a more complex structure and performs best in ETA prediction. This fact reveals that the framework proposed in this study could make accurate and reliable ETA predictions.

The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft&rsquo, s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.

International audience; Airports Terminal Maneuvering Areas (TMA) and Control Traffic Regions (CTR) are characterized by a dense air traffic flow with high complexity. In nominal operations, approach flight path safety management consists in procedures which guide the aircraft to intercept the final approach axis, and the runway slope with an expected configuration in order to land. Some abnormal flights are observed and considered as Non Compliant when the intermediate and the final leg intercepting conditions do not comply with the prescription of the operational documentation. This kind of trajectories generates difficulties for both crew and Air Traffic Control (ATC) and may induce undesirable events such as Non Stabilized Approaches or ultimate events like Control Flight Into Terrain (CFIT), in the worst cases. There is a real need for atypical flights detection tools in order to improve safety. In this paper, a post-operational detection method based on functional principal component analysis and unsupervised learning will be presented and compared to geometric features.

International audience; An Airport-Sector Network Delays model is developed in this paper for flight delay estimation within air transport network. This model takes both airports and airspace capacities into account by iterating among its three main components: a queuing engine, which treats each airport in the network as a queuing system and is used to compute delays at individual airport, a Link Transmission Model, which computes delays at individual sector and transmits all air delays into ground delays, and a delay propagation algorithm that updates flight itineraries and demand rates at each airport on the basis of the local delays computed by the queuing engine and flow control delays computed by the Link Transmission Model. The model has been implemented to a network consisting of the 21 busiest airports in China and 2962 links that represent to 151 enroute control sectors in mainland China, and its performance is evaluated by comparing with the actual delay data and results of Airport Network Delays model. It is found that the proposed model is well-suited for simulating delays in air transport system where either airports or airspace could be the bottleneck of the system.; 143575

International audience; This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide. Refereed/Peer-reviewed

International audience; 4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.

International audience; Global air-traffic demand is continuously increasing. To handle such a tremendous traffic volume while maintaining at least the same level of safety, a more efficient strategic trajectory planning is necessary. Static 4D trajectory planning with constant 4D segments, where aircraft have to stay all along their flights, ensures a strong predictability of traffic and may reduce congestion in airspace. The main limitation of this approach is linked to the 4D constraint associated to aircraft. As a matter of fact, each aircraft has to comply to this 4D segment to maintain separation from other aircraft, but this induces a real control of the engine in order to stay all the time in this 4D segment. This could result in extra fuel consumption and shorter engine life. In this work, we present an adaptive 4D strategic trajectory planning methodology which aims to minimize interaction between aircraft at the European-continent scale. The main purpose of this work is to associate to each aircraft a 4D bubble which is adapted to the current traffic situation. When aircraft are located in low density areas, the size of such bubbles can extend (with a maximum range of 20 minutes) and when aircraft enter high congestion areas, such bubbles can shrink until a minimum size of 2 minutes. The size of bubbles is then optimized according to the local density of aircraft. This adaptive process, avoid to constrain aircraft in 4D all along their trajectories. The proposed methodology separates aircraft by modifying their trajectories and departure times. This route/departure-time assignment problem is modeled as a mixed-integer optimization problem. Due to the very high combinatorics involved in the continent-scale context (involving more than 30,000 flights), we develop and implement a hybrid-metaheuristic optimization algorithm. This first optimization is done with a minimum bubble size of 2 minutes. A second optimization loop uses the solution produced by the first algorithm in order to optimally extend the size of the 4D bubbles along trajectories in order to minimize the time constraint of aircraft. Index Terms-air traffic management, 4D aircraft trajectory, strategic planning, adaptive trajectory planning

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.

International audience; Simulated Annealing (SA) is one of the simplest and best-known metaheuristic method for addressing difficult black box global optimization problems whose objective function is not explicitly given and can only be evaluated via some costly computer simulation. It is massively used in real-life applications. The main advantage of SA is its simplicity. SA is based on an analogy with the physical annealing of materials that avoids the drawback of the Monte-Carlo approach (which can be trapped in local minima), thanks to an efficient Metropolis acceptance criterion. When the evaluation of the objective-function results from complex simulation processes that manipulate a large-dimension state space involving much memory, population-based algorithms are not applicable and SA is the right answer to address such issues. This chapter is an introduction to the subject. It presents the principles of local search optimization algorithms, of which simulated annealing is an extension, and the Metropolis algorithm, a basic component of SA. The basic SA algorithm for optimization is described together with two theoretical properties that are fundamental to SA: statistical equilibrium (inspired from elementary statistical physics) and asymptotic convergence (based on Markov chain theory). The chapter surveys the following practical issues of interest to the user who wishes to implement the SA algorithm for its particular application: finite-time approximation of the theoretical SA, polynomial-time cooling, Markov-chain length, stopping criteria, and simulation-based evaluations. To illustrate these concepts, this chapter presents the straightforward application of SA to two classical and simple classical NP-hard combinatorial optimization problems: the knapsack problem and the traveling salesman problem. The overall SA methodology is then deployed in detail on a real-life application: a large-scale aircraft trajectory planning problem involving nearly 30,000 flights at the European continental scale. This exemplifies how to tackle nowadays complex problems using the simple scheme of SA by exploiting particular features of the problem, by integrating astute computer implementation within the algorithm, and by setting user-defined parameters empirically, inspired by the SA basic theory presented in this chapter.

Publication étudiante; International audience; This article explores the mission planning problem for a constellation of non-homogeneous Earth observation satellites for disaster response to promote cooperation in the Asia-Pacific region between different space agencies or satellite operators. Given a set of weighted requests consisting of a polygonal area, a list of collaborating satellites and a time horizon, the goal is first to compute the observation opportunities and then to plan a sequence of acquisitions for each sensor to optimize the total coverage in the first days to help the disaster management agencies. The non-homogeneous satellites (non-agile or agile) and the different orbits result in a huge combinatorial search space. A simplified computational method is proposed to generate the observation opportunities and a metaheuristic based on the Simulated Annealing algorithm is used to solve the mission planning problem. Real requests from disaster management agencies are used to evaluate the proposed methodology.

International audience; The hydro-sedimentary fluxes that take place on the hillslopes significantly impact the different components of hydrosystems (soil erosion, water pollution, flooding, etc.). In low-energy hydrosystems, such as in northwestern France, to understand these dynamics, we first need to look at the respective roles of each landscape entities (agricultural plot, hedge, ditch, etc.) and the interactions they generate with hydro-sedimentary fluxes. In this paper we propose two complementary approaches (computational modeling and geomorphology) to better understand the relationships between the landscape structure and the hydro-sedimentary fluxes. In a first approach, a model is developed and based upon the Multi-Agents System (SMA) in order to measure the impact of the landscape structure on the structural connectivity between production areas and rivers. The first results show the relevance of such a tool for a better understanding of hydro-sedimentary processes/landscape interactions and opens perspectives for the evaluation of functional connectivity. The latter is here described by a quantification of the hydro-sedimentary fluxes during different episodes of runoff. This work took place on a small Norman farming catchment (17.6 km²). The results of this geomorphological approach reveal the influence of some hydrological thresholds, the availability of sedimentary stocks and the effects of scales, the understanding of which is a key requirement in order to better understand functional connectivity.; Les transferts hydro-sédimentaires de surface qui ont lieu sur les versants influencent sensiblement les différents compartiments des hydrosystèmes et génèrent de multiples aléas (érosion des sols, pollution de l’eau, inondations aux exutoires, etc.). Dans les hydrosystèmes de faible énergie, tels qu’on les rencontre dans le nord-ouest de la France, appréhender les dynamiques d’écoulement sur les versants nécessite en premier lieu de s’intéresser aux rôles respectifs de chacune des entités du paysage (parcelles agricoles, haie, fossé, etc.) et aux interactions qu’elles engendrent avec les transferts hydro-sédimentaires. Dans cet article nous proposons une double approche (modélisatrice et géomorphologique) pour mieux formaliser les liens qui unissent la structure paysagère aux processus de transferts hydro-sédimentaires. Dans une première approche, un modèle développé sous Système Multi-Agents a été conçu en vue de développer des indices d’analyse spatiale capables de mesurer l’impact de la structure paysagère sur la connectivité structurelle. Les premiers résultats de cette approche exploratoire montrent la pertinence d’un tel outil pour une meilleure compréhension des interactions processus/paysage et ouvre des pistes pour l’évaluation de la connectivité fonctionnelle. Cette dernière est ici appréhendée par un travail de quantification des flux hydro-sédimentaires au cours de différents épisodes de ruissellement. Le travail s’est déroulé sur un petit bassin versant normand agricole (17,6 km²). Les résultats de cette approche géomorphologique mettent en évidence des effets de seuils hydrologiques, de disponibilité des stocks sédimentaires ainsi que des effets d’échelles dont la bonne compréhension est fondamentale pour mieux appréhender la connectivité fonctionnelle et la complexité des processus.

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; Congestion in Terminal Maneuvering Area (TMA) in hub airports is the main problem in Chinese air transportation. In this paper we propose a new system to integrated sequence and merge aircraft to parallel runways at Beijing Capital International Airport (BCIA). This system is based on the advanced avionics capabilities. Our methodology integrates a Multi-Level Point Merge (ML-PM) system, an economical descent approaches procedure, and a tailored heuristic algorithm to find a good, systematic, operationally-acceptable solution. First, Receding Horizontal Control (RHC) technique is applied to divide the entire 24 h of traffic into several sub-problems. Then in each sub-problem, it is optimized on given objectives (conflict, deviation from Estimated Time of Arrival (ETA) on the runway and makespan of the arrival flow). Four decision variables are designed to control the trajectory: the entry time, the entry speed, the turning time on the sequencing leg, and the landing runway allocation. Based on these variables, the real time trajectories are generated by the simulation module. Simulated Annealing (SA) algorithm is used to search the best solution for aircraft to execute. Finally, the conflict-free, least-delay, and user-preferred trajectories from the entry point of TMA to the landing runway are defined. Numerical results show that our optimization system has very stable de-conflict performance to handle continuously dense arrivals in transition airspace. It can also provide the decision support to assist flow controllers to handle the asymmetric arrival flows on different runways with less fuel consumption, and to assist tactical controllers to easily re-sequence aircraft with more relaxed position shifting. Moreover, our system can provide the fuel consumption prediction, and runway assignment information to assist airport and airlines managers for optimal decision making. Theoretically, it realizes an automated, cooperative and green control of routine arrival flows. Although the methodology defined here is applied to the airport BCIA, it could also be applied to other airports in the world.

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; The new era of small UAVs necessitates intelligent approaches towards the issue of fault diagnosis to ensure a safe flight. A recent attempt to accommodate quite a number of UAVs in the airspace requires to assure a safety level. The hardware limitations for these small vehicles point the utilization of analytical redundancy rather than the usual practice of hardware redundancy in the conventional flights. In the course of this study, fault detection and diagnosis for aircraft is reviewed. An approach of implementing machine learning practices to diagnose faults on a small fixed-wing is selected. The selection criteria behind is that, data- driven fault diagnosis enables avoiding the burden of accurate modeling needed in model-based fault diagnosis. In this study, first, a model of an aircraft is simulated. This model is not used for the design of Fault Detection and Diagnosis (FDD) algorithms, but instead utilized to generate data and test the designed algorithms. The measurements are simulated using the statistics of the hardware in the house. Simulated data is opted instead of flight data to isolate the probable effects of the controller on the diagnosis, which will complicate this preliminary study on FDD for drones. A supervised classification method, SVM (Support Vector Machines) is used to classify the faulty and nominal flight conditions. The features selected are the gyro and accelerometer measurements. The fault considered is loss of effectiveness in the control surfaces of the drone. Principle component analysis is used to investigate the data by reducing the feature space dimension. The training is held offline due to the need of labeled data. The results show that for simulated measurements, SVM gives very accurate results on the classification of loss of effectiveness fault on the control surfaces.

International audience; Traditionally airport systems have been studied using an approach in which the different elements of the system are studied independently. Until recently scientific community has put attention in developing models and techniques that study the system using holistic approaches for understanding cause and effect relationships of the integral system. This chapter presents a case of an airport in which the authors have implemented an approach for improving the turnaround time of the operation. The novelty of the approach is that it uses a combination of simulation, parameter analysis and optimization for getting to the best amount of vehicles that minimize the turnaround time of the airport under study. In addition, the simulation model is such that it includes the most important elements within the aviation system, such as terminal manoeuvring area, runway, taxi networks, and ground handling operation. The results show clearly that the approach is suitable for a complex system in which the amount of variables makes it intractable for getting good solutions in reasonable time.