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This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches This paper presents a new air traffic complexity metric based on linear dynamical systems, of which the goal is to quantify the intrinsic complexity of a set of aircraft trajectories. Previous works have demonstrated that the structure and organization of air traffic are essential factors in the perception of the complexity of an air traffic situation. Usually, they were not able to explicitly address trajectory pattern organization. The new metric, by identifying the organization properties of trajectories in a traffic pattern, captures some of the key factors involved in ATC complexity. The key idea of this work is to find a linear dynamical system which fits a vector field as closely as possible to the observations given by the aircraft positions and speeds. This approach produces an aggregate complexity metric that enables one to identify high (low) complexity regions of the airspace and compare their relative complexity. The metric is very appropriate to compare different traffic situations for any scale (sector or country) by associating a complexity index to each trajectory sample in the airspace. For instance, to compute the complexity for a sector, one must just sum-up the complexity for trajectory samples intersecting such a sector. This computation can also be extended in the time dimension in order to estimate the average complexity in a given airspace for a period of time. The SESAR START project has funded the research (grant number 893204).

International audience; As the number of vehicles equipped with radars sensors is rapidly increasing, the risk of harmful interference is increasing, especially since the radar waveform parameters are not regulated. Interference mitigation techniques are becoming important for radars to operate properly in this complex environment. We presented in a previous study that the use of V2X technology to communicate radars physical properties as well as the parameters of their waveform allows for new anticipation strategies. These strategies focus on identifying potential interferers thanks to V2X communications and adapt the radar waveform to minimize the risk of interference. This article investigates the importance of time synchronization between the radar's waveform and the V2X messages emissions when using such a cooperative strategy.

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; 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.

International audience; In the air traffic control system, the Terminal Maneuvering Area (TMA) is one of the most complex area in which the flight operations are easily influenced by the inevitable uncertainties such as inaccurate aircraft performance, navigation accuracy, pilot operations. This research addresses the sequencing problem in TMA under uncertainty and aims to improve air transport safety and efficiency at a tactical level. The uncertainty is managed by introducing probabilities to the temporal information at specific points for each flight. Flight by flight conflict is then measured with probability on each designated point taking all the possible arrival times into consideration. By minimizing the total probability of conflict in the network, appropriate safety margin can be imposed. A meta-heuristic simulated annealing optimization algorithm is proposed, and the solution is obtained based on the real flight data of 2 hours in Paris Charle-De-Gaulle airport. A simulation is conducted to verify the performance of our proposed model while considering the deterministic model as a baseline case. Both the candidate solutions are disturbed in terms of TMA entry time and the timestamp for each flight are conditionally deviated from the predicted ones. Final results show the advantage of the proposed model in absorbing the conflicts while experiencing the disruptions.

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; 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; 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.

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.

Best of session paper; International audience; This paper presents a data-driven study of wait time patterns for international arriving passengers across all 61 terminals from the 44 airports of entry of the United States. Each airport is an independent entity which operates with various airlines and handles demand volumes differently. This induces seasonal variation in service quality from one airport to another. Exploring six years worth of data, this paper investigates the current and long-term performance trends-an increasing number of flights versus a decreasing number of customs booths-of all airports of entry from a passenger perspective. A performance analysis is then conducted that compares average wait times of incoming passengers, considering incoming traffic ratios and allocated resources. Leveraging machine learning algorithms, six regression algorithms are trained and tested to accurately predict passenger wait times through customs at selected airports. An analysis of the performance of these models shows that the best approach-using a Gradient Boosting regressor for each terminal of entry-can capture the daily and seasonal variations of traffic patterns and immigration booth availabilities with a mean absolute error of less or equal to 5 minutes for twenty-eight terminals of entry and less than 10 minutes for all terminals. Observations show significant disparities across airports that may be explained by the foreign/US passenger ratio and the quality of booth management.

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; 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; In this study, we focus on the problem of locating optimally dynamic military areas with the aim of minimizing the number of civil flight trajectories potentially impacted by the military activity, and the distance between the military area and the military base. We model the military areas by 2D geometry shapes with a vertical extension associated to given flight levels during the temporary area-activation time window. We propose a mathematical formulation of this problem as a constrained-optimization problem. We then introduce a global-optimization methodology based on a simulated annealing algorithm featuring tailored neighborhood-search strategies and an astute computational evaluation of the otherwise costly objective function. This is applied to one day of French traffic involving 8,836 civil flights. The results show that the proposed method is efficient to locate the military area that is nearest from the military base, while minimizing the potential impact on civil flight trajectories.

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; 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; 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; 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.

Passengers' experience is becoming a key metric to evaluate the air transportation system's performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers' interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers' experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations., This paper is submitted to the tenth USA/Europe ATM 2013 seminar

International audience; This work proposes a methodology for developing an airport arrival and departure manager tool. This methodology employs optimization together with simulation techniques for improving the robustness of the solution. An arrival and departure manager tool is intended to help air traffic controllers in managing the inbound and outbound traffic without incurring in conflicts or delays. In this context, air traffic controllers need to have a tool able to help them to make the right decisions in a short time horizon. The maindecisions taken in the present methodology for each aircraft are: entry time and entry speed in the airspace and push back time at the gate. The objective of this methodology is to have a smooth flow of aircraft both in the airspace and on the ground. Preliminary tests were made using Paris Charles de Gaulle Airport as case study, and the results show that conflicts were sensibly reduced.

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.

The current overloaded airspace near large airports leads the stakeholders to develop more fundamental means to improve the use of available air capacity in dense traffic areas as terminal maneuvering area. A smart planning of aircraft trajectories and an appropriate designing of route structure can be a solution. Therefore, to analyze the capability of them, this paper proposes a novel approach to deal with the merging and sequencing problem of arrival traffic in terminal maneuvering area, and examines the performance of diverse conflict resolution strategies and different terminal maneuvering area route structure. An evolutionary algorithm is employed to resolve potential conflicts, altering one or more of the following decision variables: routes, speeds, and entrance time to terminal maneuvering area (slot). Two different fitness functions were developed. One aims to minimize potential conflicts and the other one aims to minimize at the same time potential conflicts and delay induced at runway. The performance of different combinations of decision variables, or conflict resolution strategies, is evaluated through a set of arrival aircraft to Grand Canaria airport. Also, four different route structures are tested by using the same set of incoming aircraft. The results showed that the slot and speed change and mix of all change strategies can successfully resolve all conflicts in an efficient manner. The results also suggested that topologies with one main merging point are more efficient than the ones that have two merging points. Finally, a significant abatement of delay was observed when minimizing conflicts and delay while achieving conflict-free solutions.

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.

In recent years, special interest has been paid to the solution of sector design problem. The airspace is partitioned into sectors, each of them being controlled by a group of controllers. Airspace sectors should be designed cautiously, ensuring that no sector would be overloaded during the day. The objective of an airspace design process is to adapt the airspace according to the evolution of the traffic. The aim of the presented work is to propose a new global method for the sectorization of the European airspace based on a novel mathematical modeling of the airspace and heuristic optimization methods. The main purpose of this research is to develop a method for automatic airspace sectorization, which would be able to create operationally acceptable sectors.

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; 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; The North Atlantic (NAT) is the busiest oceanic airspace in the world. In its most part, Air Traffic Services (ATS) radar surveillance is unavailable and typical procedures have been established in order to ensure safe navigation. Aircraft wish to follow what is called the minimum-time route that depends on the position of the jet stream. Generally the preferred east-west trajectories lie further north than the west-east ones. So all USA-Europe flights, for instance, want to follow roughly the same route which is not possible. In order to accommodate as many flights as possible on, or close to, their minimum time tracks and to provide the best traffic control service, a system of tracks referred to as the Organized Track System (OTS) is constructed [1]. The OTS is set up on a diurnal basis is built according to the position of the jet stream. The USA-Europe network is located on the jet stream and the Europe-USA network avoid it. Each network consists of a set, typically 4 to 7, of parallel or nearly parallel tracks (see Figure 1).

International audience; Air traffic control in non-radar oceanic airspace has always been relatively limited. Current American and European projects aim at transiting from ground-based systems to satellite-based systems. New technologies will allow to reduce significantly the present aircraft separation standards. As a consequence, the aircraft crossing the North Atlantic will be able to follow better routes, which will improve the air traffic situation by decreasing of flight durations and congestion in pre-oceanic continental airspace. In this paper an optimization model is introduced and a simple methodology to solve this problem is proposed that yields encouraging preliminary results.

Best student paper award. Best paper in the ATM Capacity Improvements track award. Best paper of session award.; International audience; This paper presents a new approach for merging multiple aircraft flows in a Terminal Maneuvering Area (TMA). This work is motivated by the current overloaded airspace near large airports and the need of more efficient methods to help controllers. Some attempts to alleviate airspace congestion such as the minimum spacing requirements, negotiation of voluntary reductions in scheduled service, and the construction of additional runways at major airports have been done. Even though, more fundamental changes are needed to improve the use of available air capacity. Present research consists of a new approach to optimize a set of aircraft planned to land at a given airport; it is proposed to merge the incoming flows from different routes by mean of speed and path changes. Due to the high combinatoric induced by such a problem, a stochastic optimization algorithm has been developed in order to propose to each aircraft a new route and speed profile. Those changes aim to remove conflicts at merging points and to maintain separation of aircraft following the same route link according to their wake turbulence constraint. The optimization criteria is based on the minimum deviation from the initial path planning. This algorithm has been successfully applied to Gran Canaria airport in Spain with real traffic demand samples for which conflict free flow merging is produced smoothly with optimal runway feeding.

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 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.

International audience; This paper addresses the general time-route assignment problem: One considers an air transportation network and a fleet of aircraft with their associated route and slot of departure. For each flight a set of alternative routes and a set of possible slots of departure are defined. One must find optimal route and slot allocation for each aircraft in a way that significantly reduces the peak of workload in the most congested sectors and in the most congested airports, during one day of traffic.

International audience; In this paper, we show how genetic algorithms can be used to compute automatically a balanced sectoring of air-space to increase air traffic control capacity in high density areas.

International audience; Airports and surrounding airspaces are limited in terms of capacity and represent the major bottleneck in the air traffic management system. This paper proposes a two-level model to tackle the integrated optimization problem of arrival, departure, and surface operations. The macroscopic level considers the terminal airspace management for arrivals and departures and airport capacity management, while the microscopic level optimizes surface operations and departure runway scheduling. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on real-world case studies of Paris Charles De-Gaulle airport, show the benefits of this integrated approach.

International audience; As air traffic demand exceeds available capacity, severe congestion during peak periods occurring at airports and the surrounding terminal airspace leads to flight delays as well as potential safety issues. This paper addresses the optimization problem of integrated traffic management of airport and terminal airspace on macroscopic level. Instead of evaluating the flight conflicts as we did in our previous research, a congestion evaluation model is proposed to quantify the flights for traffic flows in the terminal airspace and the runways, meanwhile, the occupancy evaluations of taxiway network and terminals are conducted. An adapted heuristic optimization method simulated annealing is implemented to solve the problem. Optimization process is put forward based on the case studies of Paris Charles De-Gaulle airport with 2.5-hour data. The comparisons between initial traffic data and optimized solution are provided and the advantage of the proposed model is analyzed. In the aspect of time uncertainty, simulations based on the proposed model and the model of our previous work are conducted, the final results indicate that our model shows an advantage in uncertainty absorption over the previous work.