Machine Learning Applied to Airspeed Prediction During Climb

International audienceIn this paper, we apply Machine Learning methods to improve the aircraft climb prediction in the context of groundbased applications. Mass and speed intent are key parameters for climb prediction. As they are considered as competitive parameters by many airlines, they are currently not available to groundbased trajectory predictors. Consequently, most predictors today use reference parameters that may be quite different from the actual ones. In our most recent paper ([1]), we have demonstrated that Machine Learning techniques provide a mass estimation significantly more precise than two state-of-the-art mass estimation methods. In this paper, we apply similar techniques to the speed intent. We first build a set of examples by adjusting CAS/Mach speed profile to each climb trajectory in our database. Then, using the adjusted values (ccas; cM) in this database, we learn a model able to predict the (cas;M) values of a new trajectory, using its past points as input. We apply this technique to actual Mode-C radar data and we consider 9 different aircraft types. When compared with the reference speed profiles provided by BADA, the reduction of the speed RMSE ranges from 36 % to 79 %, depending on the aircraft type. Using the predicted mass and speed profile, BADA is used to compute the predicted future trajectory with a 10 minute horizon. When compared with BADA used with the reference parameters, the reduction of the future altitude RMSE ranges from 45 % to 87 %

Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction

International audienceIn this paper, we apply Machine Learning methods to improve the aircraft climb prediction in the context of ground-based applications. Mass is a key parameter for climb prediction. As it is considered a competitive parameter by many airlines, it is currently not available to ground-based trajectory predictors. Consequently, most predictors today use a reference mass that may be different from the actual aircraft mass. In previous papers, we have introduced a least square method to estimate the mass from past trajectory points, using the physical model of the aircraft. Another mass estimation method, based on an adaptive mechanism, has also been proposed by Schultz et. al. We now introduce a new approach, where the mass is considered as the response variable of a prediction model that is learned from a set of example trajectories. This Machine Learning approach is compared with the results obtained when using the BADA (Base of Aircraft Data) reference mass or the two state-of-the-art mass estimation methods. In these experiments, 9 different aircraft types are considered. When compared with the baseline method (resp. the mass estimation methods), the Machine Learning approach reduces the RMSE (Root Mean Square Error) on the predicted altitude by at least 58 % (resp. 27 %) when assuming the speed profile to be known, and by at least 29 % (resp. 17 %) when using the BADA speed profile except for the aircraft types E145 and F100. For these types, the observed speed profile is far from the BADA speed profile

Predicting Aircraft Descent Length with Machine Learning

International audiencePredicting aircraft trajectories is a key element in the detection and resolution of air traffic conflicts. In this paper, we focus on the ground-based prediction of final descents toward the destination airport. Several Machine Learning methods – ridge regression, neural networks, and gradient-boosting machine – are applied to the prediction of descents toward Toulouse airport (France), and compared with a baseline method relying on the Eurocontrol Base of Aircraft Data (BADA). Using a dataset of 15,802 Mode-S radar trajectories of 11 different aircraft types, we build models which predict the total descent length from the cruise altitude to a given final altitude. Our results show that the Machine Learning methods improve the root mean square error on the predicted descent length of at least 20 % for the ridge regression, and up to 24 % for the gradient-boosting machine, when compared with the baseline BADA method

Energy rate prediction using an equivalent thrust setting profile

International audienceGround-based aircraft trajectory prediction is a major concern in air traffic management. A safe and efficient prediction is a prerequisite for the implementation of automated tools that detect and solve conflicts between trajectories. This paper focuses on the climb phase because predictions are less accurate in this phase. The Eurocontrol BADA1 model, as a total energy model, relies on the prediction of energy rate. In a kinetic model, this energy rate comes from the power provided by the forces applied to the aircraft. Computing these forces requires knowledge of the aircraft state (mass, airspeed, etc), atmospheric conditions (wind, temperature) and aircraft intent (maximum climb thrust or reduced climb thrust, for example). Some of this information like the mass and thrust setting are not available to ground-based systems. In this paper, we try to infer an equivalent weight and an equivalent thrust profile. These parameters are not meant to be true, however they are designed to improve the energy rate prediction. One common thrust setting profile for all the trajectories is built. This thrust profile is designed in such a way that the estimated equivalent weight provides a good energy rate prediction. We have compared the energy rate prediction using these equivalent parameters and BADA standard parameters

High Confidence Intervals Applied to Aircraft Altitude Prediction

International audienceThis paper describes the application of high confidence interval prediction methods to the aircraft trajectory prediction problem, more specifically to the altitude prediction during climb. We are interested in methods for finding twosided intervals that contain, with a specified confidence, at least a desired proportion of the conditional distribution of the response variable. This paper introduces Two-sided Bonferroni-Quantile Confidence Intervals (TBQCI), which is a new method for obtaining high confidence two-sided intervals in quantile regression. The paper also uses the Bonferroni inequality to propose a new method for obtaining tolerance intervals in least-squares regression. This latter has the advantages of being reliable, fast and easy to calculate. We compare physical point-mass models to the introduced models on an Air Traffic Management (ATM) dataset composed of traffic at major French airports. Experimental results show that the proposed interval prediction models perform significantly better than the conventional pointmass model currently used in most trajectory predictors. When comparing with a recent state-of-the-art point-mass model with adaptive mass estimation, the proposed methods giv

Optimisation des flux de trafic aérien

Cette thèse s'inscrit dans le domaine de l'optimisation globale appliquée aux flux de trafic aérien. Le problème abordé consiste à optimiser les flux de trafic aérien sans imposer de retards au décollage. On considère tout d'abord le système existant tel quel, en cherchant à améliorer l'écoulement du trafic simplement en équilibrant les regroupements des secteurs élémentaires d'espace sur les positions de contrôle. Des méthodes déterministes (A*, Branch and bound) et un algorithme génétique sont utilisés pour répartir au mieux la charge de trafic entre les positions. Dans un deuxième temps on s'autorise à modifier la structure de l'espace aérien, en partant des flux directs origine-destination pour construire, par une méthode de partitionnement et une triangulation de Delaunay, un réseau de routes aériennes répondant à certains critères d'espacement des points de croisement. On évalue dans un troisième temps l'intérêt de séparer verticalement les flux aériens, dans leur phase de croisière. Cette évaluation porte sur le nombre et la nature des conflits détectés lors de simulations en temps accéléré, en allouant ou non des niveaux de croisières séparés. Dans un quatrième temps, on génère pour les principaux flux de trafic des trajectoires 3D complètes, séparées les unes des autres, en tenant compte de la disparité des performances des avions sur chaque flux. Deux types de stratégies sont explorées : une approche séquentielle où un algorithme A* est appliqué successivement à chaque flux dans un ordre choisi, et une approche globale où toutes les trajectoires sont considérées simultanément, en utilisant un algorithme génétique. Les algorithmes sont d'abord testés sur des cas simples avant d'être appliqués aux données réelles, en France et en Europe. Enfin, en dernier lieu, la dimension temporelle est prise en compte afin de planifier dynamiquement des trajectoires 4D non-conflictuelles pour des trains d'avions. ABSTRACT : This work belongs to the field of global optimization, applied to air traffic flows. The problem being addressed consists of optimizing air traffic flows without regulating the traffic demand. Firstly, the current system is enhanced only by considering the sector configurations of the controllers working positions. Deterministic methods (A*, Branch and bound) and a genetic algorithm are used to balance the workload between control positions, by splitting and merging airspace sectors. Secondly, we allow ourselves to modify the airspace structure. A routes network is computed from the direct origin-destination flows, with crossing points satisfying constraints of minimum distance, using a partitioning method and a Delaunay triangulation. Thirdly, the profit brought by the vertical separation of air traffic flows is assessed through fast-time simulations, by considering the nature of conflicts detected with or without a cruise level allocation. Fourthly, full 3D-trajectories are computed for the main flows, taking into account the variety of aircraft performances within each flow. Two strategies are proposed : the 1 vs. n strategy uses an A* algorithm to compute each trajectory in turn, separating the new trajectory from the previous ones, and the global strategy applies a genetic algorithm to the whole set of trajectories. Both algorithms are first tried on basic flow configurations, and then applied to real traffic data over France and Europe. Finally, the time dimension is taken into account in order to generate conflict-free 4D-trajectories for groups of aircraft following the same route

A participatory design for the visualization of airspace configuration forecasts

International audienceCurrently, airspace-related activities in Air Traffic Control Centers (ATCC) are dispatched between the Flow Management Position (FMP) operators and the control room manager, and take place in two different time frames. The first activity (FMP) is the planning, 2 days ahead, of airspace usage and anticipated overloads, using coarse-grain and relatively inaccurate workload prediction metrics. The second activity (control room manager) is the day-to-day operation, where workload is re-assessed in real-time and where airspace may be re-configured according to the actual traffic of the day. In previous works, a workload model relying on relevant air traffic complexity metrics was proposed, using a neural network trained on past sector operations. This workload prediction model was combined with tree search algorithms, in order to compute optimal partitions of the airspace in Air Traffic Control (ATC) sectors. This method provides more accurate airspace configuration forecasts than today, thus improving the overall predictability of the Air Traffic Management (ATM)/ATC system. When relying on accurate 4D-trajectory predictions, as expected in the SESAR program, it could contribute towards bridging the current gap between the pre-tactical airspace/flow management and real-time operations. In this paper, we detail the participatory design approach that we used to develop a research prototype displaying the algorithm's results. As there is no such forecasting tool today, the main issue was to create a user interface in the absence of an existing user

Statistical prediction of aircraft trajectory : regression methods vs point-mass model

International audienceGround-based aircraft trajectory prediction is a critical issue for air traffic management. A safe and efficient prediction is a prerequisite for the implementation of automated tools that detect and solve conflicts between trajectories. Moreover, regarding the safety constraints, it could be more reasonable to predict intervals rather than precise aircraft positions . In this paper, a standard point-mass model and statistical regression method is used to predict the altitude of climbing aircraft. In addition to the standard linear regression model, two common non-linear regression methods, neural networks and Loess are used. A dataset is extracted from two months of radar and meteorological recordings, and several potential explanatory variables are computed for every sampled climb segment. A Principal Component Analysis allows us to reduce the dimensionality of the problems, using only a subset of principal components as input to the regression methods. The prediction models are scored by performing a 10-fold cross-validation. Statistical regression results method appears promising. The experiment part shows that the proposed regression models are much more efficient than the standard point-mass model. The prediction intervals obtained by our methods have the advantage of being more reliable and narrower than those found by point-mass model

Ground-based prediction of aircraft climb : point-mass model vs regression methods

Predicting aircraft trajectories with great accuracy is central to most operational concepts ([1], [2]) and automated tools that are expected to improve the air traffic management (ATM) in the near future. On-board flight management systems predict the aircraft trajectory using a point-mass model describing the forces applied to the center of gravity. This model is formulated as a set of differential algebraic equations that must be integrated over a time interval in order to predict the successive aircraft positions in this interval. The point-mass model requires knowledge of the aircraft state (mass, thrust, etc), atmospheric conditions (wind, temperature), and aircraft intent (target speed or climb rate, for example)