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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; 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.

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

The structural map describing the northwestern Paris Basin prioritise several directions governing regional faults depending of N150–160, N120–130 (Armorican) and secondarily N30–50 (Variscan) orientations. The fractal analysis of talweg networks of the ‘Pays de Caux’ plateau, compared with the stratigraphic, structural and cartographic studies of the clay with flints and chalk, underscores a new major direction oriented N90–100. The purpose of this study is to suggest a new structural map in this part of the northwestern Paris Basin. To cite this article: E. Hauchard et al., C. R. Geoscience 334 (2002) 295–302.

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.