Modeling Air-Traffic Service Time Uncertainties for Queuing Network Analysis

Numerous factors influence the operational performance of the National Airspace System (NAS). In particular, the traffic efficiency is affected by uncertainties such as weather, navigation accuracy, aircraft performance and operational procedures, and air traffic management (ATM) actions. This study focuses on identifying various air traffic uncertainty sources and deriving the associated mathematical models of service time distributions. These models provide the distributions given air traffic uncertainties through analytical expressions without resorting to computationally expensive Monte-Carlo simulations.

[1]  J. H. Sinnott,et al.  National Airspace System demand and capacity modeling , 1989, Proc. IEEE.

[2]  Long Dou,et al.  Modeling Air Traffic Management Technologies With a Queuing Network Model of the National Airspace System , 1999 .

[3]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[4]  F. P. Wieland,et al.  Are air traffic models chaotic? , 2001, 20th DASC. 20th Digital Avionics Systems Conference (Cat. No.01CH37219).

[5]  W. R. Fried,et al.  Avionics Navigation Systems , 1969 .

[6]  Frederick P. Wieland.  Limits to growth: results from the detailed policy assessment tool [air traffic congestion] , 1997, 16th DASC. AIAA/IEEE Digital Avionics Systems Conference. Reflections to the Future. Proceedings.

[7]  Jamshed A. Modi.  A nested queue model for the analysis of air traffic control sectors , 1974 .

[8]  T. Pearcey,et al.  Delays in Landing of Air Traffic , 1948, The Journal of the Royal Aeronautical Society.

[9]  Aydan Cavcar,et al.  Impact of Aircraft Performance Characteristics on Air Traffic Delays , 2004 .

[10]  Frederick Wieland.  Investigating the Volume -Delay Relationship at Congested Airports , 2006 .

[11]  Jinwhan Kim,et al.  Trajectory Uncertainty Modeling for Queuing Analysis of the National Airspace System , 2008 .

[12]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[13]  Marvin K. Simon,et al.  Probability Distributions Involving Gaussian Random Variables: A Handbook for Engineers, Scientists and Mathematicians , 2006 .

[14]  W. Mendenhall,et al.  Statistics for engineering and the sciences , 1984 .

[15]  Victor H. L. Cheng,et al.  Queuing Network Models of the National Airspace System , 2008 .

[16]  Paul Schonfeld,et al.  Deterministic Models for Degraded Airside Capacity and Delays , 2004 .

[17]  Neil W. Polhemus.  The construction and use of continuous autoregressive models for traffic indices , 1980 .

[18]  G. E. Bell,et al.  Operational Research into Air Traffic Control , 1949, The Journal of the Royal Aeronautical Society.

[19]  Jay M. Rosenberger,et al.  Queueing Network Models of the National Airspace System , 2008 .

[20]  A. Basu,et al.  The Inverse Gaussian Distribution , 1993 .

[21]  Michael Kolonko,et al.  On the waiting time of arriving aircrafts and the capacity of airports with one or two runways , 2007, Eur. J. Oper. Res..

[22]  Harry G. Perros.  Queueing Networks with Blocking: Exact and Approximate Solutions , 1994 .

[23]  Adam H. Monahan,et al.  The Probability Distribution of Sea Surface Wind Speeds , 2006 .

[24]  Kapil Sheth,et al.  FACET: Future ATM Concepts Evaluation Tool , 2001 .

[25]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[26]  J. Leslie.  The Inverse Gaussian Distribution: Theory, Methodology, and Applications , 1990 .

[27]  F. Beichelt.  Stochastic processes in science, engineering, and finance , 2006 .

[28]  David R. Basco,et al.  Water Wave Mechanics for Engineers and Scientists , 1985 .

[29]  Jorma Rissanen.  Optimal Estimation , 2011, ALT.