An Adaptive Model of Flight Time Uncertainty and Its Application to Time-Based Air Traffic Operations

2018 ◽  
Author(s):  
Noboru Takeichi
Keyword(s):  
Flight Time ◽  
Air Traffic ◽  
Adaptive Model ◽  
Traffic Operations ◽  
Time Uncertainty

In-Trail Procedure for Improved Oceanic Air Traffic Operations

2019 ◽  
Author(s):  
Arman Izadi ◽  
Nicolas Hinze ◽  
Antonio Trani ◽  
Joseph A. Post
Keyword(s):  
Air Traffic ◽  
Traffic Operations

Demonstration of the Traffic Aware Planner

2016 ◽  
Vol 60 (1) ◽  
pp. 1189-1192
Author(s):  
Kelly A. Burke ◽  
David J. Wing
Keyword(s):  
Software Development ◽  
Work Load ◽  
Flight Time ◽  
Human Machine Interface ◽  
Research Development ◽  
Traffic Operations ◽  
National Airspace System ◽  
Passenger Comfort ◽  
Software Application ◽  
Machine Interface

NASA has been developing and testing the Traffic Aware Strategic Aircrew Requests (TASAR) concept for aircraft operations featuring a NASA-developed cockpit automation tool, the Traffic Aware Planner (TAP), which computes traffic/hazard-compatible route changes to improve flight efficiency. The TAP technology is anticipated to save fuel and flight time and thereby provide immediate and pervasive benefits to the aircraft operator, as well as improving flight schedule compliance, passenger comfort, and pilot and controller work-load. Previous work has indicated the potential for significant benefits for TASAR-equipped aircraft, and a flight trial of the TAP software application in the National Airspace System has demonstrated its technical viability. As part of ongoing software research, development, and testing of the TAP software, we developed the capability to run the current build of the TAP software using the NASA Airspace and Traffic Operations Simulator (ATOS) with preloaded flight scenarios. Our demonstration is fully interactive and will allow participants to operate the TAP software on an iPad Air themselves, or observe the demonstrators operating it. The purpose of developing this capability is not only to assist with testing during software development, but also to acquaint researchers and potential stakeholders with the TAP human machine interface and the functionality and capabilities of the software itself.

Expertise in Aviation

2019 ◽  
pp. 661-689 ◽  
Author(s):  
Christopher D. Wickens ◽  
Frederic Dehais
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
Traffic Control ◽  
Technical Skills ◽  
Flight Time ◽  
Air Traffic ◽  
Task Management ◽  
Loosely Coupled ◽  
Air Traffic Controllers ◽  
Very High

This chapter makes the distinction between the experience of aviation professionals, often quantified in terms of hours of flight time, or flight qualifications, and expertise, as revealed by high proficiency at aviation tasks. Very high proficiency defines the expert. Challenge results because of the difficulty in measuring such proficiency, particularly beyond the student pilot level, and in air traffic control. The chapter also reviews the literature that examines the relation between experience, differences in cognitive ability, and the expertise of aviation professionals as pertains to controlling the aircraft, navigating, and communicating, as well as pertains to non-technical skills manifest by both pilots and air traffic controllers: situation awareness, decision making, task management and crew resource management. It is concluded that experience is only loosely coupled with proficiency in these areas.

scholarly journals Air traffic simulation in chemistry-climate model EMAC 2.41: AirTraf 1.0

2016 ◽  
Vol 9 (9) ◽  
pp. 3363-3392 ◽  
Author(s):  
Hiroshi Yamashita ◽  
Volker Grewe ◽  
Patrick Jöckel ◽  
Florian Linke ◽  
Martin Schaefer ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Optimal Solution ◽  
Weather Conditions ◽  
Air Transportation ◽  
Climate Impact ◽  
Flight Time ◽  
Great Circle ◽  
Air Traffic ◽  
Civil Aviation ◽  
Local Weather

Abstract. Mobility is becoming more and more important to society and hence air transportation is expected to grow further over the next decades. Reducing anthropogenic climate impact from aviation emissions and building a climate-friendly air transportation system are required for a sustainable development of commercial aviation. A climate optimized routing, which avoids climate-sensitive regions by re-routing horizontally and vertically, is an important measure for climate impact reduction. The idea includes a number of different routing strategies (routing options) and shows a great potential for the reduction. To evaluate this, the impact of not only CO2 but also non-CO2 emissions must be considered. CO2 is a long-lived gas, while non-CO2 emissions are short-lived and are inhomogeneously distributed. This study introduces AirTraf (version 1.0) that performs global air traffic simulations, including effects of local weather conditions on the emissions. AirTraf was developed as a new submodel of the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model. Air traffic information comprises Eurocontrol's Base of Aircraft Data (BADA Revision 3.9) and International Civil Aviation Organization (ICAO) engine performance data. Fuel use and emissions are calculated by the total energy model based on the BADA methodology and Deutsches Zentrum für Luft- und Raumfahrt (DLR) fuel flow method. The flight trajectory optimization is performed by a genetic algorithm (GA) with respect to a selected routing option. In the model development phase, benchmark tests were performed for the great circle and flight time routing options. The first test showed that the great circle calculations were accurate to −0.004 %, compared to those calculated by the Movable Type script. The second test showed that the optimal solution found by the algorithm sufficiently converged to the theoretical true-optimal solution. The difference in flight time between the two solutions is less than 0.01 %. The dependence of the optimal solutions on the initial set of solutions (called population) was analyzed and the influence was small (around 0.01 %). The trade-off between the accuracy of GA optimizations and computational costs is clarified and the appropriate population and generation (one iteration of GA) sizing is discussed. The results showed that a large reduction in the number of function evaluations of around 90 % can be achieved with only a small decrease in the accuracy of less than 0.1 %. Finally, AirTraf simulations are demonstrated with the great circle and the flight time routing options for a typical winter day. The 103 trans-Atlantic flight plans were used, assuming an Airbus A330-301 aircraft. The results confirmed that AirTraf simulates the air traffic properly for the two routing options. In addition, the GA successfully found the time-optimal flight trajectories for the 103 airport pairs, taking local weather conditions into account. The consistency check for the AirTraf simulations confirmed that calculated flight time, fuel consumption, NOx emission index and aircraft weights show good agreement with reference data.

Modeling and Simulation of the Impact of Air Traffic Operations on the Environment

2012 ◽  
Vol 20 (4) ◽  
pp. 285-310 ◽  
Author(s):  
Banavar Sridhar ◽  
Neil Y. Chen ◽  
Hok K. Ng ◽  
Alexander Morando
Keyword(s):  
Modeling And Simulation ◽  
Air Traffic ◽  
Traffic Operations ◽  
The Impact

A Non-Linear Relationship between Controller Workload and Traffic Count

2005 ◽  
Vol 49 (12) ◽  
pp. 1129-1133 ◽  
Author(s):  
Paul U. Lee
Keyword(s):  
Linear Relationship ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Limiting Factor ◽  
Traffic Operations ◽  
Objective Metrics ◽  
Non Linear ◽  
Perceived Workload ◽  
Using Data

Controller workload has been a focal topic in air traffic management research because it is considered a key limiting factor to capacity increase in air traffic operations. Because workload ratings are subjective and highly prone to individual differences, some researchers have tried to replace workload with more objective metrics, such as aircraft count. A significant caveat in substituting these metrics for workload ratings, however, is that their relationships are non-linear. For example, as the number of aircraft increases linearly, the controller's perceived workload jumps from low to high at a certain traffic threshold, resulting in a stepfunction increase in workload with respect to aircraft count, suggesting that controllers perceive workload categorically. The non-linear relationship between workload and aircraft count has been validated using data collected from a recent study on the En Route Free Maneuvering concept element (Lee, Prevot, Mercer, Smith, & Palmer, 2005). The results suggest that objective metrics, such as aircraft count, may not be used interchangeably with subjective workload. In addition, any estimation on workload should not be extrapolated from a set of workload measures taken from an experiment since the extrapolated workload is likely to significantly underestimate workload.

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