TY - CONF
T1 - Statistical Regularities in ATM: network properties, trajectory deviations and delays
AU - Vitali, Stefania
AU - Micciche', Salvatore
AU - Lillo, Fabrizio
AU - Cipolla, Marco
AU - Mantegna, Rosario Nunzio
AU - Gurtner, Gérald
AU - Beato, null
AU - Pozzi, Simone
AU - Lillo, Fabrizio
PY - 2012
Y1 - 2012
N2 - One of the key enabler to the productivity and efficiency shift foreseen by SESAR will be the business-trajectory concept. The path to a deep understanding of how this new concept impacts on the future SESAR Air Traffic Management scenario goes through a better understanding of the actual air traffic network, and this will be done in the present paper by analyzing traffic data within the framework of complex network analysis. In this paper we will consider flights trajectory data from the Data Demand Repository database. In a first investigation, we perform a network study of the air traffic infrastructure starting from the airports and then refining our analysis at the level of navigation points in order to understand what are the main features that may help explaining why some nodes of the network happen to be found in the same community, i.e. cluster. In a second investigation we perform a study at the level of flight trajectories with the aim of identify statistical regularities in the spatio-temporal deviations of flights between their planned and actual 4D trajectories.
AB - One of the key enabler to the productivity and efficiency shift foreseen by SESAR will be the business-trajectory concept. The path to a deep understanding of how this new concept impacts on the future SESAR Air Traffic Management scenario goes through a better understanding of the actual air traffic network, and this will be done in the present paper by analyzing traffic data within the framework of complex network analysis. In this paper we will consider flights trajectory data from the Data Demand Repository database. In a first investigation, we perform a network study of the air traffic infrastructure starting from the airports and then refining our analysis at the level of navigation points in order to understand what are the main features that may help explaining why some nodes of the network happen to be found in the same community, i.e. cluster. In a second investigation we perform a study at the level of flight trajectories with the aim of identify statistical regularities in the spatio-temporal deviations of flights between their planned and actual 4D trajectories.
KW - air traffic management
KW - complex networks
KW - air traffic management
KW - complex networks
UR - http://hdl.handle.net/10447/69363
UR - http://www.sesarinnovationdays.eu/files/SIDs/2012/SID%202012-21.pdf
M3 - Other
ER -