TY - GEN
T1 - Airspace complexity estimations based on data-driven flow modeling
AU - Salaün, Erwan
AU - Gariel, Maxime
AU - Vela, Adan E.
AU - Feron, Eric
AU - Clarke, John Paul B.
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-18
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
AB - 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. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
UR - http://arc.aiaa.org/doi/10.2514/6.2010-8074
UR - http://www.scopus.com/inward/record.url?scp=84867762996&partnerID=8YFLogxK
U2 - 10.2514/6.2010-8074
DO - 10.2514/6.2010-8074
M3 - Conference contribution
SN - 9781600869624
BT - AIAA Guidance, Navigation, and Control Conference
ER -