Dynamic Density: An Air Traffic Management Metric

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms

An Empirically grounded Agent Based simulator for Air Traffic Management in the SESAR scenario

In this paper we present a simulator allowing to perform policy experiments relative to the air traffic management. Different SESAR solutions can be implemented in the model to see the reaction of the different stakeholders as well as other relevant metrics (delay, safety, etc). The model describes both the strategic phase associated to the planning of the flight trajectories and the tactical modifications occurring in the en-route phase. An implementation of the model is available as an open-source software and is freely accessible by any user. More specifically, different procedures related to business trajectories and free-routing are tested and we illustrate the capabilities of the model on an airspace which implements these concepts. After performing numerical simulations with the model, we show that in a free-routing scenario the controllers perform less operations but the conflicts are dispersed over a larger portion of the airspace. This can potentially increase the complexity of conflict detection and resolution for controllers. In order to investigate this specific aspect, we consider some metrics used to measure traffic complexity. We first show that in non-free-routing situations our simulator deals with complexity in a way similar to what humans would do. This allows us to be confident that the results of our numerical simulations relative to the free-routing can reasonably forecast how human controllers would behave in this new situation. Specifically, our numerical simulations show that most of the complexity metrics decrease with free-routing, while the few metrics which increase are all linked to the flight level changes. This is a non-trivial result since intuitively the complexity should increase with free-routing because of problematic geometries and more dispersed conflicts over the airspace

Describing Air Traffic Flows Using Stochastic Programming

International audienceThe objectives of Air Traffic Management (ATM) system is to ensure safe and efficient operation of an individual flight as well as to maximize the overall capacity and minimize the adverse environmental impacts of the air transportation system. To achieve these objectives, airspace capacity, which drives capacity and traffic flow management policies, should be more properly estimated according to the detailed traffic configuration. There have been some efforts to describe the complexity of a traffic situation based on the idea of modeling airspace onto a dynamical system. This paper extends the previous research efforts by accounting for uncertainties on aircraft's positions and velocities. The proposed method is illustrated with examples