Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-25T04:49:20.416Z Has data issue: false hasContentIssue false
  • English
  • Français

On The Vertical Speeds Of Airways Traffic

Published online by Cambridge University Press:  21 October 2009

S. A. N. Magill
S. A. N. Magill
Affiliation:
(Defence Research Agency)
Get access Rights & Permissions [Opens in a new window]

Abstract

Knowledge of the statistics of aircraft vertical speeds is important both for the construction of realistic traffic simulators and for the development of trajectory prediction tools for use in future air traffic control (ATC) systems. This paper reports on the analysis of radar data recordings for nearly 10000 civil flights on airways. Results are presented for the means and spreads of vertical speeds as functions of altitude. Evidence is presented that roughly half of the observed spreads arise from fluctuations within each aircraft's trajectory, as opposed to variation from one aircraft to another. A simple procedure is proposed for simulating vertical speed data which has statistics similar to those obtained from the radar recordings. Some consequences of the results for the development of trajectory prediction tools for use in future ATC systems are discussed. The results suggest that the provision of accurate trajectory prediction tools is not as straightforward as it might at first appear to be.

Keywords

1. Vertical speed.2. Air traffic control.3. Simulation.
Type
Research Article
Information
The Journal of Navigation , Volume 49 , Issue 1 , January 1996 , pp. 58 - 71
Copyright
Copyright © The Royal Institute of Navigation 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1Tofukuji, N. (1993). An enroute ATC simulation experiment for sector capacity estimation. IEEE trans, on Control Systems Theory, Vol. 1, No. 3, September.Google Scholar
2ten Have, J. M. (1993). The development of the NLR ATC research simulator (Narsim): design philosophy and potential ATM research. Simulation Practice and Theory, Vol. 1, No. 1.CrossRefGoogle Scholar
3Magill, S. N.Finch, W.Tyler, A. C. F. and Wilkinson, E. T. (1986). A real-time simulator which provides an environment for air traffic control experiments. Proc. SCS European Simulation Congress, Antwerp, September.Google Scholar
4Magill, S. N. (1990). Simulation of options for future air traffic systems. Proc. UKSC'90 Computer Simulation Conference, Brighton.Google Scholar
5Philipp, W. and Gainche, F. (1994). Air traffic flow management in Europe. International Seminar on ‘Advanced Techniques for Air Traffic Flow Management’ organized by DLR in Bonn, April. Procs. edited by Winter, H. and Nusser, H-G published by Springer-Verlag.Google Scholar
6Benoit, A. and Swierstra, S. (1984). A cost-efficient control procedure for the benefit of all airspace users. AGARD Symposium on ‘Cost-Effective and Affordable Guidance and Control Systems’.Google Scholar
7Procs. of a special meeting on ‘Aircraft Performance Models for Present and Future ATC Needs’. Eurocontrol Experimental Centre, Bretigny-sur-Orge, November 1986, Eurocontrol Doc. 872002, January 1987.Google Scholar
8Byrne, J. (1994). User Manual for the Base of Aircraft Data (BADA). Revision 2.1, Issue 1. Eurocontrol Experimental Centre, Bretigny-sur-Orge.Google Scholar
9Visscher, H. and Van Blokland, W. V. (1984). Improvements in computing flight paths and flight times for ATC. International Journal of Aviation Safety, September.Google Scholar
10Jain, R. and Chlamtac, I. (1985). The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations. Comm ACM, Vol. 28, No. 10, October.Google Scholar