Selection and Evaluation of Air Traffic Complexity Metrics

This paper presents an original method to evaluate air traffic complexity metrics. In previous works, we applied a principal component analysis (PCA) to find the correlations among a set of 27 complexity indicators found in the literature. Neural networks were then used to find a relationship between the components and the actual airspace sector configurations. Assuming that the decisions to group or split sectors are somewhat related to the controllers workload, this method allowed us to identify which components were significantly related to the actual workload. We now focus on the subset of complexity indicators issued from these components, and use neural networks to find a simple relationship between these indicators and the sector status

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  David Gianazza.  Optimisation des flux de trafic aérien , 2004 .

[3]  Washington Y. Ochieng,et al.  The Factors Affecting Airspace Capacity in Europe: A Cross-Sectional Time-Series Analysis Using Simulated Controller Workload Data , 2004, Journal of Navigation.

[4]  Banavar Sridhar,et al.  Measures for air traffic controller workload prediction , 2001 .

[5]  S. Puechmorel,et al.  Air Traffic Complexity: Towards an Intrinsic Metric , 2000 .

[6]  Brian Hilburn,et al.  COGNITIVE COMPLEXITY IN AIR TRAFFIC CONTROL: A LITERATURE REVIEW , 2004 .

[7]  J H Crump,et al.  Review of stress in air traffic control: its measurement and effects. , 1979, Aviation, space, and environmental medicine.

[8]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[9]  Jean-Marc Alliot,et al.  Optimization of air traffic control sector configurations using tree search methods and genetic algorithms , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[10]  David Gianazza,et al.  Évaluation of air traffic complexity metrics using neural networks and sector status , 2006 .

[11]  Anthony J. Masalonis,et al.  DYNAMIC DENSITY AND COMPLEXITY METRICS FOR REALTIME TRAFFIC FLOW MANAGEMENT , 2003 .

[12]  Laudeman,et al.  DYNAMIC DENSITY: AN AIR TRAFFIC MANAGEMENT METRIC , 1998 .

[13]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[14]  S. Puechmorel,et al.  AIR TRAFFIC COMPLEXITY : TOWARDS INTRINSIC METRICS , 2000 .

[15]  P. Kopardekar.  1 MEASUREMENT AND PREDICTION OF DYNAMIC DENSITY , 2004 .

[16]  Banavar Sridhar,et al.  Airspace Complexity and its Application in Air Traffic Management , 1998 .

[17]  Jean-Marc Alliot,et al.  Optimal Combinations of Air Traffic Control Sectors using Classical and Stochastic Methods , 2002, IC-AI.

[18]  Arnab Majumdar.  The factors affecting airspace capacity in Europe : A Framework Methodology based on Cross Sectional Time-Series Analysis using Simulated Controller Workload Data , 2005 .

[19]  P. Averty,et al.  Evaluating a new index of mental workload in real ATC situation using psychophysiological measures , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[20]  Mustapha Mouloua,et al.  TOWARD A NON-LINEAR APPROACH TO MODELING AIR TRAFFIC COMPLEXITY Brian Hilburn Center for Human Performance Research The Hague, The Netherlands Geraldine Flynn EUROCONTROL Research Centre Brétigny s/Orge , France , 2005 .