Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

基于BADA模型的飞机持续爬升运行减噪效果研究

随着中国民航业的快速发展,机场周边噪声污染不断加剧,改进飞行程序来缓解噪声影响因其研究周期短、应用性强等特点,成为当前研究热点.本文概述了CCO的定义、特点,分析其飞行剖面,简述了基于BADA的航空器性能模型和机场噪声评估模型;并以广州白云机场VIBOS离场方向为例,运用MATLAB编程计算噪声并绘制噪声等值线图,从噪声影响时间、面积及区域形状等方面对比传统离场程序和CCO离场程序的噪声影响效果.结果表明:CCO离场程序可有效减少噪声影响时间,且在高噪声等级下降噪效果更佳;CCO离场程序可有效减少噪声影响面积;CCO离场程序可改变噪声等值线形状.     

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools.In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems.This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48% on average on a 10 min horizon prediction. The RMSE on the speed is reduced by 25% on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft take-off, the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25% on average.The data set and the Machine Learning code are publicly available.     

Calculation of aircraft fuel consumption and CO2 emissions based on path profile estimation by clustering and registration

In this paper, typical flight paths, fuel burn and carbon dioxide (CO₂) emissions are computed using a rich data set and two estimation approaches: (i) a clustering and landmark registration technique and (ii) a method based on the EUROCONTROL’s Base of Aircraft Data (BADA) performance model. Clustering is employed to extract flight characteristics and organize altitude profiles accordingly. Our flight path and CO₂ emissions analysis focuses on the Climb-Cruise-Descent (CCD) cycle, since different operational conditions during the Landing and Take-off cycle may result in significant deviations in terms of fuel burn and CO₂ emissions and different modeling assumptions and approaches should be adopted. The key features of the CCD cycle are the flight distance, the aircraft type and the flight direction. Path segmentation and landmark registration are employed for path representation and smoothening of discontinuities. The paths estimated by the above method are compared to those obtained by the point mass BADA model. Noticeable deviations in the resulting estimates of the operational characteristics are found. Higher deviations in prediction errors are found in the climb and descent duration and the rate of climb and descent. The typical altitude profiles obtained by the two methods are used to determine fuel burn and CO₂ emissions. The difference in the resulting estimates are less stark; on a fleet-wide level the fuel burn of the relevant typical profiles differ by 7%. Emission maps of the U.S. airspace enabling the identification of critical emission spots including routes, airports, seasons and aircraft type are constructed.