scholarly journals Modeling the Effects of Uncertainty on the National Airspace System

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Erin C. DeCarlo ◽  
Barron J. Bichon
Keyword(s):  
Traffic Control ◽  
Probabilistic Models ◽  
Quantitative Measure ◽  
Air Traffic ◽  
Control Analysis ◽  
National Airspace System ◽  
Flight Plan ◽  
Traffic Scenario ◽  
Future Work ◽  
The Impact

This paper presents the first steps toward managing uncertainty and assessing risk within the national airspace system (NAS) by investigating the impact of uncertainty on “flight plan flexibility” (FPF) – a proposed quantitative measure of an aircraft’s ability to adapt its flight plan due to improbable events. First, an air traffic scenario derived from national flight plan data is simulated with an open source BlueSky air traffic control analysis centered on a busy airport. Next, state-space diagrams derived from the aircraft state parameters (i.e., speeds, altitudes, headings), spatial proximities, and surveillance signals are used to construct the FPF metric. Finally, a probabilistic analysis is used to propagate uncertainty in the aircraft positions through BlueSky to observe the resulting uncertainty in FPF through time. Future work will aggregate individual aircraft safety measures and additional metrics into a single system-wide indicator, transitioning from BlueSky to a gate-to-gate simulation for prognostics, and deriving probabilistic models of epistemic and aleatory sources of uncertainty in the NAS from available data.

scholarly journals An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Duc-Thinh Pham ◽  
Sameer Alam ◽  
Vu Duong
Keyword(s):  
Traffic Control ◽  
Air Traffic ◽  
Learning Problem ◽  
Air Traffic Controller ◽  
Explanatory Variables ◽  
Human In The Loop ◽  
Machine Learning Approach ◽  
Traffic Scenario ◽  
Trajectory Features ◽  
Future Work

In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller (D-side) and executive controller (R-side). D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem, where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy (99%). Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions (altitude, speed, and course change) for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach.

scholarly journals In-Flight Aircraft Trajectory Optimization within Corridors Defined by Ensemble Weather Forecasts

Aerospace ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 144 ◽  
Author(s):  
Martin Lindner ◽  
Judith Rosenow ◽  
Thomas Zeh ◽  
Hartmut Fricke
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Air Traffic ◽  
Weather Data ◽  
Parameter Uncertainties ◽  
Weather Forecasts ◽  
Fuel Savings ◽  
Flight Plan ◽  
Aircraft Trajectory

Today, each flight is filed as a static route not later than one hour before departure. From there on, changes of the lateral route initiated by the pilot are only possible with air traffic control clearance and in the minority. Thus, the initially optimized trajectory of the flight plan is flown, although the optimization may already be based upon outdated weather data at take-off. Global weather data as those modeled by the Global Forecast System do, however, contain hints on forecast uncertainties itself, which is quantified by considering so-called ensemble forecast data. In this study, the variability in these weather parameter uncertainties is analyzed, before the trajectory optimization model TOMATO is applied to single trajectories considering the previously quantified uncertainties. TOMATO generates, based on the set of input data as provided by the ensembles, a 3D corridor encasing all resulting optimized trajectories. Assuming that this corridor is filed in addition to the initial flight plan, the optimum trajectory can be updated even during flight, as soon as updated weather forecasts are available. In return and as a compromise, flights would have to stay within the corridor to provide planning stability for Air Traffic Management compared to full free in-flight optimization. Although the corridor restricts the re-optimized trajectory, fuel savings of up to 1.1%, compared to the initially filed flight, could be shown.

Design of a conflict-detection air traffic control tool for the implementation of continuous climb operations: A case study at Palma TMA

2019 ◽  
Vol 233 (13) ◽  
pp. 4839-4852 ◽  
Author(s):  
Javier A Pérez-Castán ◽  
Fernando Gómez Comendador ◽  
Álvaro Rodríguez-Sanz ◽  
Rocío Barragán ◽  
Rosa M Arnaldo-Valdés
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Conflict Detection ◽  
Air Traffic ◽  
Traffic Flows ◽  
Control Intervention ◽  
Static Approach ◽  
Operational Concept ◽  
The Impact ◽  
Control Tool

Continuous climb operation is an operational concept that allows airlines to perform an optimal departing trajectory avoiding air traffic control segregation requirements. This concept implies the design and integration of air traffic flows for the sake of safety performance. This paper designs a new conflict-detection air traffic control tool based on the blocking-area concept, characterises the conflict probability between air traffic flows and assesses the impact of continuous climb operation integration in a terminal manoeuvring area. In this paper, a conflict is set out by the infringement of vertical and longitudinal separation minima and coincides with the probability of air traffic control tool usage. Moreover, this research discusses two different approaches for the conflict-detection air traffic control tool: a static approach considering nominal continuous climb operations and landing trajectories, and a dynamic approach that assesses 105 continuous climb operations and landing trajectories. Finally, the air traffic control tool is implemented using Palma TMA data and proves that out of 11 intersections (between departing and landing routes), solely 4 generate vertical separation infringements. The conflict probability between continuous climb operations and arrivals is less than 10−5. Except for one intersection, that is roughly 10−2, similar to current air traffic control intervention designed levels. Therefore, results conclude the viability of the conflict-detection air traffic control tool and continuous climb operations integration.

Simple Models for Airport Delays During Transition to a Trajectory-Based Air Traffic System

2009 ◽  
Vol 62 (4) ◽  
pp. 555-570 ◽  
Author(s):  
Peter Brooker
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Queuing Theory ◽  
New Technologies ◽  
Transition Process ◽  
Air Traffic ◽  
Paradigm Shifts ◽  
New Paradigm ◽  
Operational Feature ◽  
The Impact

It is now widely recognised that a paradigm shift in air traffic control concepts is needed. This requires state-of-the-art innovative technologies, making much better use of the information in the air traffic management (ATM) system. These paradigm shifts go under the names of NextGen in the USA and SESAR in Europe, which inter alia will make dramatic changes to the nature of airport operations. A vital part of moving from an existing system to a new paradigm is the operational implications of the transition process. There would be business incentives for early aircraft fitment, it is generally safer to introduce new technologies gradually, and researchers are already proposing potential transition steps to the new system. Simple queuing theory models are used to establish rough quantitative estimates of the impact of the transition to a more efficient time-based – four-dimensional (4D) – navigational and ATM system. Such models are approximate, but they do offer insight into the broad implications of system change and its significant features. 4D-equipped aircraft in essence have a contract with the airport runway – they would be required to turn up at a very precise time – and, in return, they would get priority over any other aircraft waiting for use of the runway. The main operational feature examined here is the queuing delays affecting non-4D-equipped arrivals. These get a reasonable service if the proportion of 4D-equipped aircraft is low, but this can deteriorate markedly for high proportions, and be economically unviable. Preventative measures would be to limit the additional growth of 4D-equipped flights and/or to modify their contracts to provide sufficient space for the non-4D-equipped flights to operate without excessive delays. There is a potential for non-Poisson models, for which there is little in the literature, and for more complex models, e.g. grouping a succession of 4D-equipped aircraft as a batch.

Reducing Situation Awareness Errors in Air Traffic Control

1997 ◽  
Vol 41 (1) ◽  
pp. 230-233 ◽  
Author(s):  
Debra G. Jones
Keyword(s):  
Situation Awareness ◽  
Traffic Control ◽  
Decision Process ◽  
Air Traffic Control ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Air Traffic ◽  
High Fidelity ◽  
Expected Information ◽  
The Impact

Since situation awareness (SA) is vital to the decision process, SA errors can degrade decision making. Many SA errors occur when all the relevant information has been correctly perceived. In these cases, the information's significance is not comprehended, and a representational error occurs. Schema influence this comprehension aspect of SA. This study investigates the impact of information with certain schema related characteristics on SA: (1) schema bizarre information will impact SA more than schema irrelevant information, and (2) schema unexpected information will impact SA more than the absence of schema expected information. Using a high fidelity air traffic control simulation, misinformation was provided to the controller and schema related cues were furnished to indicate the error. The results indicated that (1) schema bizarre cues impacted SA more than schema irrelevant cues and (2) no difference existed between the impact of the absence of schema expected cues and schema unexpected cues. Additionally the results emphasize the difficulty incurred when trying to prevent SA errors.

scholarly journals Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 379 ◽  
Author(s):  
Victor Gomez Comendador ◽  
Rosa Arnaldo Valdés ◽  
Manuel Villegas Diaz ◽  
Eva Puntero Parla ◽  
Danlin Zheng
Keyword(s):  
Bayesian Network ◽  
Traffic Control ◽  
Air Traffic ◽  
Network Modelling ◽  
Traffic Demand ◽  
Complexity Metrics ◽  
Dynamic Airspace Configuration ◽  
Input Variables ◽  
The Impact

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

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