4D Trajectories Complexity Metric Based on Lyapunov Exponents

International audienceThe ATM system has to cope with an increasing number of flights, pushing the capacity to its limits. As an example, the average daily traffic above Europe was 26286 flights/day, with a peak traffic demand in excess of 31000 flights..

Drone Fleet Deployment Strategy for Large Scale Agriculture and Forestry Surveying

International audienceAgriculture drones offer clear advantages over other monitoring methods including satellite imaging, manned scouting, and manned aircraft. However, for large scale areas, such as large forestry and agriculture mapping problems, the single drone is hard to accomplish its mission of mapping in a relatively short time period of 30 to 45 minutes. In addition, in large forestry mapping, camera, communication, and payload settings may further reduce the maximum endurance of drones in the air. With a single drone, the total required mission time to cover all the area is prolonged, not only producing a high cost for a drone service provider but also having more uncertainty. While with multiple drones, or a fleet of drones, it is possible to identify a globally optimized solution to reduce the total required mission time. In this paper, we mainly discuss the strategy of drone fleet deployment for large scale area surveying. Three key parts are analyzed, including a fleet of drones, cooperative coverage path planning, communication and data processing. The associated state-of-the-art solutions are listed and reviewed. In addition, in this paper, the key operational constraints for large scale agriculture and forestry surveying are analyzed. It should be pointed out that, from a comprehensive point of view, a drone fleet deployment for large scale surveying could attract more attention from the commercial drone industry

Simulation-Free Runway Balancing Optimization Under Uncertainty Using Neural Network

International audienceThis paper proposes a new optimization scheme using neural network for runway balancing to minimize departure and arrival aircraft delay. While other researchers have proposed solutions to the runway balancing problem using a simulation-based technique to calculate aircraft delay, the proposed method replaces the simulation by a neural network model-based estimation using the actual operational data, thus providing the following two advantages. First, accurate estimation of aircraft delay can improve the solution of the runway balancing problem. Second, the simulation process is not required in the optimization. Although it is difficult to develop an accurate simulation model especially under uncertain environment, the neural network model can estimate the average delay without explicitly modeling uncertainty. In this paper, as a first step, the effectiveness of the proposed method is validated through simulations. First, simulations considering uncertainty are used to generate the data, which are then used to train the neural network. The neural network predicts the delay under the current traffic and only this predicted delay is used for the runway balancing optimization with simulated annealing. The simulation result shows that the result by neural network outperforms the one by the simulation-based method under uncertainty. This means that the neural network can accurately estimate the delay under uncertainty environment, and is applicable in the optimization process

A comparative study for merging and sequencing flows in TMA

Se ha previsto diversos escenarios para explorar el futuro Sistema de Transporte Aéreo. De acuerdo con EUROCONTROL, el escenario más probable de los movimientos de vuelo IFR en Europa hasta 2035, prevé 14,4 millones de vuelos, lo cual es 50% más que en 2012. [10] El aumento en el tráfico aéreo se está traduciendo en diversos problemas tanto en el lado aire como en tierra. En el lado aire, se hace más evidente en el espacio aéreo circundante a los aeropuertos, donde las llegadas y salidas sirven a un gran número de aviones que están sometidos a diversos problemas logísticos que continuamente hay que resolver para asegurarse de que cada vuelo y pasajero viaje con seguridad y eficiencia hasta su destino final. La presente investigación propone una metodología basada en algoritmos evolutivos para resolver el problema de fusión y secuenciación de un conjunto de aeronaves. Para dicho fin, se realiza un análisis del diseño de la topología de las rutas de aterrizaje. Este enfoque propone para cada aeronave una nueva ruta y perfil de velocidad con el fin de evitar posibles conflictos en los puntos de fusión, mientras que se mantienen las normas de separación de la OACI. La función objetivo se basa en adquirir la desviación mínima de cada aeronave con respecto a su plan de vuelo original. El algoritmo se ha aplicado con éxito en el aeropuerto de Gran Canaria en España con muestras de la demanda de tráfico reales para lo que se ha encontrado una configuración óptima para la alimentación óptima pista.The imminent growing in the Air transport System has forecast diverse scenarios to explore the future of the aviation. According to EUROCONTROL forecast of IFR flight movements in Europe up to 2035, the most likely scenario predicts 14.4 million flights, which is 50% more than in 2012. [10] This increase in the air traffic is translating into diverse problems in the airside and landside. In the airside, it becomes more evident in the airspace surrounding airports, where the arrivals and departures serve a large number of aircraft which are subjected to many logistical problems that must continuously be solved to make sure each flight and passenger travels safely and efficiently. The present research proposes a methodology based on evolutionary algorithms to tackle the merging and sequencing problem of a set of aircraft by analyzing the topology design of the landing routes. It is proposed to merge the arrivals from different routes by changing the topology design of the STARs (Standard Terminal Arrival Route). The approach proposes to each aircraft a new route and speed profile in order to avoid potential conflicts at merging points while maintaining ICAO separation standards. The objective function is based on achieving the minimum deviation of each aircraft from it original flight plan. 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.Grupo de Transporte Aéreo - Grupo de Ingeniería Aplicada a la Industri

Large Scale 4D Trajectory Planning

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

Hybrid metaheuristic optimization algorithm for strategic planning of {4D} aircraft trajectories at the continent scale

International audienceGlobal 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. In this work, we present a strategic trajectory planning methodology which aims to minimize interaction between aircraft at the European-continent scale. In addition, we propose a preliminary study that takes into account uncertainties of aircraft positions in the horizontal plane. 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. In addition, we present a computationally-efficient interaction detection method for large trajectory sets. The proposed methodology is successfully implemented and tested on a full-day simulated air traffic over the European airspace, yielding to an interaction-free trajectory plan

Application of cellular automata modeling to analyze the dynamics of hyper-concentrated stream flows on loamy plateaux (Paris Basin, North-west France)

International audienceFor several years, flash floods appear frequently on small dry valleys of the Paris Basin, in the North of France. These “hyper-concentrated stream flows” are defined as floods with sudden apparition, rapid rising time and important specific flow. They are produced by intense rainfall (>50mm.h-1) over small areas (<40km²) and present single features. The catchment morphology is a first-order controlling factor of floods dynamic. For a better understanding of such type of processes, analysing the morphological signature of these catchments becomes of paramount importance. Therefore, classical morphometric methods do not consider the dynamics generated by the catchments structure. So we develop a new tool based on spatial analysis and the cellular automata “Ruicells” is proposed. It allows us to integrate the catchment geometry, the slopes pattern and the drainage network in order to simulate runoff response at different outlets. First results attempt to validate the influence of drainage network on the hydrological response of the catchments, while size of drainage area has a minor influence. Moreover, the application of cellular automata modelling highlights the causes of spatial and temporal variability of flows as for example the relation between the location of streaming areas and water paths