Airspace Complexity Estimations Based on Data-Driven Flow Modeling

Abstract

This paper presents a new methodology that aims to rapidly generate airspace complexity estimations, which are principally based on the probability of presence of at least one or two aircraft at any given point in a considered sector. Three-dimensional complexity maps are generated using an aircraft flow model driven from historical data. Time-varying flow characteristics such as routes, speed, probability density function of the inter-arrival time between two consecutive aircraft, are determined using Enhanced Traffic Management System (ETMS) data. In addition to the flow characteristics, the complexity maps take into account the sector geometrical configuration and the probability of severe weather. From the complexity maps, a scalar estimation of the airspace complexity is proposed. The complexity estimations presented in this paper are intended to be a predictive tool to support traffic flow management, in order to anticipate for a given time period how different flows may interact together. Especially, the 3-D complexity maps allow to predict which “critical” regions may be subject to possible conflict between aircraft or to the presence of aircraft in severe weather area.