Abstract

The Air Traffic Management (ATM) community strives to reduce the environmental impact per flight. Continuous Descent Operation (CDO) has been identified by the ATM community as one of the operational improvements that could reduce aviation’s environmental impact, both in terms of aircraft noise and gaseous emissions. In the current ATM system, CDOs are only feasible in low-density traffic. The ultimate goal is an ATM system that facilitates CDOs in high-density traffic. This research described in this thesis focused on two features of such an ATM system: decision support that enables the air traffic controller to accurately set up traffic for CDO, and delegation of the spacing task to the flight crew during the CDO. Two enablers are real-time availability of meteorological data, and accurate trajectory prediction. In this thesis new methods were developed and validated to infer wind, air pressure, and air temperature profiles from aircraft surveillance data. Trajectory prediction was defined as a machine learning problem, enabling predictions based on historic aircraft trajectory and meteorological data without the need for explicit modeling of the aircraft performance and procedures. A decision support tool was developed further and tested using a human-in-the-loop experiment. The tool enabled the subjects to set up traffic for CDO in high-density traffic at an acceptable work load level and high level of situation awareness. Monte-Carlo simulations were carried out to assess the runway capacity that can be achieved when delegating the spacing task to the flight crew. These simulations showed the feasibility of CDOs in high-density traffic.

Abstract

This paper presents a model based on an hybrid system to numerically simulate the climbing phase of an aircraft. This model is then used within a trajectory prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm is used to tune five selected parameters, and thus improve the accuracy of the model. Incorporated within a trajectory prediction tool, this model can be used to derive the order of magnitude of the prediction error over time, and thus the domain of validity of the trajectory prediction. A first validation experiment of the proposed model is based on the errors along time for a one-time trajectory prediction at the take off of the flight with respect to the default values of the theoretical BADA model. This experiment, assuming complete information, also shows the limit of the model. A second experiment part presents an on-line trajectory prediction, in which the prediction is continuously updated based on the current aircraft position. This approach raises several issues, for which improvements of the basic model are proposed, and the resulting trajectory prediction tool shows statistically significantly more accurate results than those of the default model. Comment: SESAR 2nd Innovation Days (2012)

Abstract

Considering the expected air traffic growth, the innovation and development of new tools able to allow a more efficient and safer way of managing aircraft operations is necessary to achieve future expectations. In this context, it is important to be capable of accurately predict aircraft trajectories to ensure efficient aircraft operations (e.g., planning and dispatching of flights, in-flight trajectory prediction, etc.) as well as to make more robust the Air Traffic Management (ATM) system (which includes ground based systems for ATC, predicting demand in ATC sectors, etc). The way of predicting them is based on aircraft performance models (APM), i.e., sets of equations that allow to model the aircraft performance according to some specific coefficients that depend on the aircraft carrying out the flight. Therefore, the predicted trajectories accuracy will depend directly on the aircraft performance model used. If the APM does not reflect the reality, the predicted trajectories will not be accurate enough. Moreover, since these trajectories will not be longer optimal according to the real performance model, the performance in cost-efficiency and the environmental impact of aircraft operations will be decreased. Then, it is required the use of aircraft performance models as realistic as possible. The objective of this Master Thesis is the design of an algorithm capable to estimate the coefficients of the functions describing the aircraft performance model considered, which will be the Base of Aircraft Data (BADA), from Quick Access Recorder (QAR) flight data. The idea is to obtain the real coefficients values of each specific tail number aircraft to accurately model their performance for trajectory prediction purposes. To perform the coefficients' estimation, real flight data has been used, consisting in a set of trajectories carried out by more than 30 different tail number aircrafts and obtained from their respective QAR. Among these aircrafts, only one (i.e, its trajectories) will be selected to design the estimation algorithm, which consist in a nonlinear least squares optimization problem. The output will be the coefficients value that better fits to the QAR data used, i.e, that minimize the difference between the real trajectories and the generated predicted trajectories as a result of applying the estimated coefficient's value to the BADA model equations. The results show a good estimation accuracy for the four aircrafts analysed, obtaining a very low prediction error in all cases, which proves the reliability of the algorithm and the necessity of tailoring the existing performance models (in this case BADA).

Abstract

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS. Peer Reviewed

Abstract

traffic predictability is paramount in the air traffc system in order to enable concepts such as Trajectory Based Operations (TBO) and higher automation levels for self-separation. Whereas in simulated environments 4D conflict-free trajectory optimisation has shown good potential in the improvement of air traffc effciency, its application to real operations has been very challenging due to the current lack of information sharing between airspace users. Consequently, such operations are still very limited in scope and rarely attempted in dense traffic situations. Better predictability of other traffc future states would be an enabler for each aircraft to fly its user preferred route without decreasing safety in a self-separation context. But this is not an easy task when basic aircraft parameters such as aircraft weight, performance data or airline strategies are not available at the time of prediction. In this paper the authors propose to compensate this hindrance by continuously integrating the state of the surounding traffc to improve the ownship's knowledge of other aircraft's dynamics. Specifically, conventional position (and velocity) messages, as coming from Automatic Dependent Surveillance Broadcast (ADS-B), are integrated at the ownship. Then, an optimisation problem is formulated, using optimal control theory, that minimises the error with the known states, having the parameters of study (i.e. mass) as decision variables. A scenario with two departing trajectories is used to demonstrate the e ectiveness of this parameter estimation method. In it, the take-off mass of the potential intruder is estimated on- board the ownship and its impact to conflict detection and resolution is presented, demonstrating the big improvements in predictability and safety. Peer Reviewed Document type: Conference object

Abstract

The concept of 4D trajectory management relies on the prediction of aircraft trajectories in time and space. Due to changes in atmospheric conditions and complexity of the air traffic itself, the reliable prediction of system states is an ongoing challenge. The emerging uncertainties have to be modeled properly and considered in decision support tools for efficient air traffic flow management. Therefore, the subjacent causes for uncertainties, their effects on the aircraft trajectory and their dependencies to each other must be understood in detail. Besides the atmospheric conditions as the main external cause, the aircraft itself induces uncertainties to its trajectory. In this study, a cause-and-effect model is introduced, which deals with multiple interdependent uncertainties with different stochastic behavior and their impact on trajectory prediction. The approach is applied to typical uncertainties in trajectory prediction, such as the actual take-off mass, non-constant true air speeds, and uncertain weather conditions. The continuous climb profiles of those disturbed trajectories are successfully predicted. In general, our approach is applicable to all sources of quantifiable interdependent uncertainties. Therewith, ground-based trajectory prediction can be improved and a successful implementation of trajectory-based operations in the European air traffic system can be advanced. Document type: Article

Abstract

Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle surroundings and predict collisions, on-board sensors such as radar, lidar, and cameras are often used. However, the driving environment perception based on these sensors can be adversely affected by a number of factors such as weather and solar irradiance. In addition, potential dangers cannot be detected if the target is located outside the limited field-of-view of the sensors, or if the line of sight to the target is occluded. In this paper, we propose an approach for designing a vehicle collision warning system based on fusion of multisensors and wireless vehicular communications. A high-level fusion of radar, lidar, camera, and wireless vehicular communication data was performed to predict the trajectories of remote targets and generate an appropriate warning to the driver prior to a possible collision. We implemented and evaluated the proposed vehicle collision system in virtual driving environments, which consisted of a vehicle&ndash vehicle collision scenario and a vehicle&ndash pedestrian collision scenario. Document type: Article