Abstract

Trajectory prediction and optimisation algorithms will be the keystone for a successful trajectory based operations concept implementation, where accurate predictions and optimal trajectories will be needed for a wide variety of look-ahead times and operational contexts. The main goal of this paper is to present the architecture and capabilities of an aircraft trajectory prediction and optimisation framework suitable for various air traffic management research applications. The flexibility of this framework, called DYNAMO, allows for an easy implementation and assessment of actual and future concepts of operation, considering at the same time realistic weather data and aircraft performance models. In addition, its design enables the use for real-time applications and when a large set of trajectories needs to be rapidly generated for simulation and benchmarking purposes. The performance of the framewor Peer Reviewed

Abstract

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Best paper award in Weather session at the 36th DASC - Digital Avionics Systems Conference State-of-the-art weather data obtained from numerical weather predictions are unlikely to satisfy the requirements of the future air traffic management system. A potential approach to improve the resolution and accuracy of the weather predictions could consist on using airborne aircraft as meteorological sensors, which would provide up-to-date weather observations to the sur- rounding aircraft and ground systems. This paper proposes to use Kriging, a geostatistical interpolation technique, to create short- term weather predictions from scattered weather observations derived from surveillance data. Results show that this method can accurately capture the spatio-temporal distribution of the temperature and wind fields, allowing to obtain high-quality local, short-term weather predictions and providing at the same time a measure of the uncertainty associated with the prediction. Peer Reviewed Award-winning Document type: Conference object

Abstract

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Best student paper award 36th DASC Peer Reviewed Award-winning Document type: Conference object

Abstract

ming to a more collaborative demand and capacity balancing (DCB), in the scope of trajectory based operations, this paper presents an approach that takes alternative trajectories into a DCB optimization algorithm. These alternative trajectories are generated by the airspace users for those flights traversing hotspots (i.e. sectors with demand above capacity), which are predicted by the Network Manager. The trajectories consider lateral re-routings and/or vertical avoidance of all detected hotspots, which, along with different types of delay measures (including linear holding and in-flight delay recovery), are then integrated as a whole into a centralized optimization model to manage the traffic flow under a set of static scheme of airspace capacities. The combination of trajectory options and distribution of delays are hence optimized with the objective of minimizing the total deviation with regard to airspace users’ preferences (taking into account the fuel consumption, route charge and the cost of delay). Results suggest that delays can be remarkably reduced once alternative trajectory options are included in the DCB algorithm. Nevertheless, this delay reduction is obtained by diverting a large number of flights, yielding to an interesting trade-off between environmental impact and cost-efficiency for the airspace users. Peer Reviewed