Network analysis reveals patterns behind air safety events

Complex networks have been extensively used to study the topological and dynamical characteristics of transportation systems, although far less attention has been devoted to the analysis of specific problems arising in everyday operations. In this work, the use of a network representation is proposed for studying the appearance of Loss of Separation events, a kind of safety occurrence in which two aircraft violate the minimal separation while airborne. The topological analysis of networks representing the structure of traffic flows allows identifying situations in which the probability of appearance of such events is increased. Beyond these specific results, this work demonstrates the usefulness of the complex network approach in the analysis of operational patterns and occurrences.

[1]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[2]  Henk A. P. Blom,et al.  Human cognition performance model to evaluate safe spacing in air traffic , 2002 .

[3]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[4]  Alessandro Vespignani,et al.  The role of the airline transportation network in the prediction and predictability of global epidemics , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  Julio M. Ottino,et al.  Complex networks , 2004, Encyclopedia of Big Data.

[7]  K. Gurney,et al.  Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence , 2008, PloS one.

[8]  Chonghui Guo,et al.  Entropy optimization of scale-free networks’ robustness to random failures , 2005, cond-mat/0506725.

[9]  J. E. Freund,et al.  Modern Elementary Statistics. , 1953 .

[10]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[11]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Massimiliano Zanin,et al.  Optimizing Functional Network Representation of Multivariate Time Series , 2012, Scientific Reports.

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Michael S. Nolan,et al.  Fundamentals of Air Traffic Control , 1990 .

[15]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[16]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[17]  S. Boccaletti,et al.  Complex networks analysis of obstructive nephropathy data. , 2011, Chaos.

[18]  P. Anderson More is different. , 1972, Science.

[19]  Lucas Antiqueira,et al.  Analyzing and modeling real-world phenomena with complex networks: a survey of applications , 2007, 0711.3199.

[20]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[21]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[22]  G. K. Singh,et al.  Preventing runway incursions and conflicts , 2004 .

[23]  Fabrizio Lillo,et al.  Modelling the air transport with complex networks: A short review , 2013, The European Physical Journal Special Topics.