Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T21:41:53.387Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimisation of interval management – speed planning using SMPSO

Published online by Cambridge University Press:  13 August 2020

T. Riedel Open the ORCID record for T. Riedel [Opens in a new window] ,
M. Takahashi  and
E. Itoh Open the ORCID record for E. Itoh [Opens in a new window]
T. Riedel*
Affiliation:
Keio University, Graduate School of Science and Technology, Yokohama, Japan Electronic Navigation Research Institute, Air Traffic Management Department, Tokyo, Japan
M. Takahashi
Affiliation:
Keio University, Graduate School of Science and Technology, Yokohama, Japan
E. Itoh
Affiliation:
Electronic Navigation Research Institute, Air Traffic Management Department, Tokyo, Japan
*
timo.riedel@keio.jp
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent research on Flight-deck Interval Management (FIM), a modern technology for increasing safety and improving airspace and runway utilisation through self-spacing, has led to the development of a new rule-based logic for FIM, namely Interval Management – Speed Planning (IM-SP). In an initial benchmark study, IM-SP showed good spacing performance with a significant reduction in speed commands, a major area of concern with previous FIM logics, resulting in a lower burden on the flight crew during FIM operation. Nevertheless, there remains scope for improvement in other aspects, such as fuel burn. In this study, the internal cost function of IM-SP is further analysed and optimised using speed-constrained multi-objective particle swarm optimisation to improve the performance of IM-SP under the multiple objectives of FIM. The optimisation renders new settings that address the problem areas, improve the speed commands and enhance the overall quality of IM-SP. Two distinctive solutions, viz. a spacing performance optimised setting and a fuel burn optimised setting, are further analysed and discussed, and directions for follow-up research are explored.

Keywords

air traffic managementinterval managementparticle swarm optimisation
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1281 , November 2020 , pp. 1819 - 1845
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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

FAA, NextGen Implementation Plan 2016. https://www.faa.gov/nextgen/media/NextGen_Implementation_Plan-2016.pdf, 2016 [Accessed 31 March 2019].Google Scholar
SESAR, European ATM Master Plan 2015 Edition. https://www.atmmasterplan.eu/downloads/202, 2015 [Accessed 31 March 2019].Google Scholar
Study Group for the Future Air Traffic Systems, Long-term Vision for the Future Air Traffic Systems CARATS, Collaborative Actions for Renovations of Air Traffic Systems. https://www.mlit.go.jp/common/000128185.pdf, 2010 [Accessed 31 March 2019]Google Scholar
RTCA, Minimal Operational Performance Standards (MOPS) for Flight-deck Interval Management (FIM), RTCA DO-361, 2015.Google Scholar
RTCA, Safety, Performance and Interoperability Requirements Document for Airborne Spacing – Flight Deck Interval Management (ASPA-FIM), RTCA DO-328, 2011.Google Scholar
Bone, R.S. and Mendolia, A.S., Pilot and Air Traffic Controller Use of Interval Management During Terminal Metering Operations, MITRE Technical Report MTR170570, 2018.Google Scholar
Abbott, T.S. An Overview of a Trajectory-Based Solution for En Route and Terminal Area Self-Spacing: Seventh Revision, NASA/CR–2015-218794, 2015.Google Scholar
Baxley, B.T., Johnson, W.C., Scardina, J. and Shay, R.F. Air Traffic Management Technology Demonstration-1 Concept of Operations (ATD-1 ConOps), Version 3.0, NASA/TM−2016-219213, 2016.Google Scholar
Swieringa, K.A., Wilson, S.R., Baxley, B.T., Roper, R.D., Abbott, T.S., Levitt, I. and Scharl, J. Flight Test Evaluation of the ATD-1 Interval Management application, 17th AIAA Aviation Technology, Integration, and Operations Conference, AIAA AVIATION Forum, Denver, CO, 2017.CrossRefGoogle Scholar
Baxley, B.T., Swieringa, K.A., Wilson, S.R., Roper, R.D., Hubbs, C., Goess, P. and Shay, R. Flight Crew Survey Responses from the Interval Management (IM) Avionics Phase 2 Flight Test, 17th AIAA Aviation Technology, Integration, and Operations Conference, AIAA AVIATION Forum, Denver, CO, 2017.CrossRefGoogle Scholar
Baxley, B.T., Swieringa, K.A., Roper, R.D., Hubbs, C., Goess, P. and Shay, R. Recommended Changes to Interval Management to Achieve Operational Implementation, 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, 2017.CrossRefGoogle Scholar
Riedel, T., Takahashi, M. and Itoh, E.Reducing speed commands in interval management using speed planning, Aeronaut J, 124, (1272), pp 189–215, 2020. doi: 10.1017/aer.2019.124CrossRefGoogle Scholar
Nebro, A.J., Durillo, J.J., GarcÍa-Nieto, J.,Coello Coello, C.A., Luna, F. and Alba, E. SMPSO: A New PSO-based Metaheuristic for Multi-objective Optimization, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), Nashville, TN, 2009. doi: 10.1109/MCDM.2009.4938830CrossRefGoogle Scholar
Rahil, H., Abou El Majd, B. and Bouchoum, M.Optimized Air Routes Connections for Real Hub Schedule Using SMPSO Algorithm. Oper Res Comput Sci Interfaces Ser, 62, pp 369–384, 2018. doi: 10.1007/978-3-319-58253-5_21Google Scholar
Kennedy, J. and Eberhart, R. Particle Swarm Optimization, Proceedings of ICNN’95 - IEEE International Conference on Neural Networks, pp 1942–1948, 1995. https://doi.org/10.1109/ICNN.1995.488968CrossRefGoogle Scholar
Shi, Y. and Eberhart, R. A Modified Particle Swarm Optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp 69–73, 1998. https://doi.org/10.1109/ICEC.1998.699146CrossRefGoogle Scholar
Sierra, M.R. and Coello Coello, C.A. Improving PSO-Based Multi-objective, Optimization Using Crowding, Mutation and ε-Dominance, Evolutionary Multi-Criterion Optimization (EMO 2005), pp 505–519, 2005. https://doi.org/10.1007/978-3-540-31880-4_35CrossRefGoogle Scholar
Clerc, M. and Kennedy, J.The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space, IEEE Trans Evol Comput, 6, (1), pp 58–73, 2002. doi: 10.1109/4235.985692CrossRefGoogle Scholar
De Gelder, N., Bussink, F.J.L., Knapen, E.G. and in ‘t Veld, A.C. Interval Management Operations in the Terminal Airspace of Amsterdam Airport Schiphol, AIAA Guidance, Navigation, and Control Conference, San Diego, CA, 2016.CrossRefGoogle Scholar
Eurocontrol Experimental Centre, User Manual for the Base of Aircraft Data (BADA) Revision 3.12, EEC Technical/Scientific Report No. 14/04/24-44, 2014.Google Scholar
Itoh, E., Fukushima, S., Hirabayashi, H., Wickramasinghe, N.K. and Toratani, D.Evaluating energy-saving arrivals of wide-body passenger aircraft via flight-simulator experiments, J Aircr, 55, (6), pp 2427–2443, 2018. http://arc.aiaa.org/doi/abs/10.2514/1.C034348CrossRefGoogle Scholar
Itoh, E., Wickramasinghe, N.K., Hirabayashi, H. and Fukushima, S.Feasibility study on fixed fight-path angle descent for wide-body passenger aircraft, CEAS Aeronaut J, 6, (2), pp 589–612, 2018. doi: 10.1007/s13272-018-0337-9Google Scholar
Riedel, T., Takahashi, M., Tatsukawa, T. and Itoh, E.Evaluating applied flight-deck interval management using Monte Carlo simulations on the K-Supercomputer, Trans Jpn Soc Aeronaut S, 62, (6), pp 299–309, 2019CrossRefGoogle Scholar
Hadka, D. MOEA Framework – A Free and Open Source Java Framework for Multiobjective Optimization. https://moeaframework.org [Accessed 01 May 2019].Google Scholar