Russian Psychological Issues
JournalsTopicsAuthorsEditor's Choice For AuthorsAbout PsyJournals.ruContact Us

  Previous issue (2020. Vol. 13, no. 1)

Included in Web of Science СС (ESCI)


Experimental Psychology (Russia)

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2072-7593

ISSN (online): 2311-7036


License: CC BY-NC 4.0

Started in 2008

Published quarterly

Free of fees
Open Access Journal


Assessing the Aircraft Crew Actions with the Aid of a Human Factor Risk Model 53

Kuravsky L.S.
Doctor of Engineering, Dean of the Computer Science Faculty , Moscow State University of Psychology and Education , Moscow, Russia

Yuryev G.A.
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology & Education, Moscow, Russia

Zlatomrezhev V.I.
Head of Laboratory, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia

Yuryeva N.E.
PhD in Engineering, Research Fellow, Information Technology Center for Psychological-Ecological Studies of the Faculty Newsletter-Technologies, Research Associate, Moscow State University of Psychology and Education, Moscow, Russia

Presented is a human factor risk model when piloting an aircraft. This model is based on comparing representations of the evaluated crew actions with the comparable action representations of various types and performance quality, which form a representative sample and are contained in a pre-formed specialized database. The risk in question is represented by probabilistic estimates, which result from consistent applications of the Principal Component Analysis, Multidimensional Scaling, and Cluster Analysis to three types of characteristics, viz.: parameters of flights and states of aircraft systems, gaze movement trajectories and time series of oculomotor activity primary indexes. These steps form the clusters of flight fragments for various types and performance quality, including abnormal ones. The Discriminant Analysis provides calculating the probabilistic profile for belonging to certain target clusters, with a final conclusion being derived from this structure. Key elements of the approach presented are three new metrics used to compare crew actions and to ensure significant discrimination for flight fragments of various types and performance quality. Detailing flight parameters contributions in differences of the flight fragments in a given metric is carried out to provide meaningful analysis of the detected abnormality causes. With sufficient computational performance, the flight data analysis under consideration can be implemented in real time automatic mode.

Keywords: human factor risk model, Principal Component Analysis, Multidimensional Scaling, Cluster Analysis, oculomotor activity indexes

Column: Psychology of Labor


A Part of Article

An objective assessment of the piloting performance is important for assessing the risks associated with the Information Management Field (IMF) of the cockpit, optimizing IMF and training the crews. One of the critical issues in this regard is the development of evaluation criteria.

For Reference


This work was performed as a part of the “SAFEMODE” Project (Grant Agreement No 814961) with the financial support of the Ministry of Science and Higher Education of the Russian Federation (Project UID RFMEFI62819X0014).

  1. Aaron M. Aircraft trajectory clustering techniques using circular statistics. Yellowstone Conference Center, Big Sky, Montana, 2016. IEEE.
  2. Bastani V., Marcenaro L., Regazzoni C. Unsupervised trajectory pattern classification using hierarchical Dirichlet Process Mixture hidden Markov model. International Workshop on Machine Learning for Signal Processing (MLSP) / IEEE. 2014, pp. 1—6.
  3. Borg, I., Groenen, P. J. F. Modern Multidimensional Scaling Theory and Applications. Springer, 2005. P. 140.
  4. Bress, Thomas J. Effective LabVIEW Programming: NTS Press, 2013. ISBN 1-934891-08-8.
  5. Cottrell M., Faure C., Lacaille J., Olteanu M. Anomaly Detection for Bivariate Signals. IWANN (1) 2019, pp. 162—173.
  6. Cramer H. Mathematical Methods of Statistics. Princeton: Princeton University Press. 1999. P. 575.
  7. Eerland W.J., Box S. Trajectory Clustering, Modelling and Selection with the focus on Airspace Protection. AIAA Infotech@ Aerospace. _ AIAA, 2016, pp. 1—14.
  8. Enriquez M. Identifying temporally persistent flows in the terminal airspace via spectral clustering. Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013) / Federal Aviation Administration (FAA) and EUROCONTROL. Chicago, IL, USA: 2013. June 10—13.
  9. Enriquez M., Kurcz C. A Simple and Robust Flow Detection Algorithm Based on Spectral Clustering. International Conference on Research in Air Transportation (ICRAT) / Federal Aviation Administration (FAA) and EUROCONTROL. Berkeley, CA, USA: 2012. May 22—25.
  10. Faure C., Bardet J.M., Olteanu M., Lacaille J. Using Self-Organizing Maps for Clustering and Labelling Aircraft Engine Data Phases. In: WSOM 2017, pp. 96—103.
  11. Gaffney S., Smyth P. Joint probabilistic curve clustering and alignment. In Advances in Neural Information Processing Systems. Vol. 17. Cambridge, MA: MIT Press, 2005, pp. 473—480.
  12. Gaffney S., Smyth P. Trajectory clustering with mixtures of regression models. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. 1999, pp. 63—72.
  13. Gariel M., Srivastava A., Feron E. Trajectory clustering and an application to airspace monitoring. IEEE Transactions on Intelligent Transportation Systems. 2011. Vol. 12, no. 4, pp. 1511—1524.
  14. GOST R ISO/MEHK 31010 — 2011 Menedzhment riska. Metody otsenki riska. [Risk management. Risk assessment methods]. Moscow: Standartov Pub-l, 2011. P.71.
  15. Grevtsov N. Synthesis of control algorithms for aircraft trajectories in time optimal climb and descent. Journal of Computer and Systems Sciences International. 2008. Vol. 47, no. 1, pp. 129—138.
  16. Hung C., Peng W., Lee W. Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. The VLDB Journal — The International Journal on Very Large Data Bases. 2015. Vol. 24, no. 2, pp. 169—192.
  17. Kohonen T. Self-Organizing Maps. Springer, 3th Ed., 2001. P. 501.
  18. Krasil’shchikov M.N., Evdokimenkov V.N., Bazlev D.A. Individual’no-adaptirovannye bortovye sistemy kontrolya tekhnicheskogo sostoyaniya samoleta i podderzhki upravlyayushchikh deistvii letchika [Individually adapted on-Board systems for monitoring the technical condition of the aircraft and supporting the pilot’s control actions]. Moscow: MAI Pub-l, 2011. P. 438.
  19. Kuravsky L.S., Artemenkov S.L., Yuriev G.A., Grigorenko E.L. New approach to computer-based adaptive testing. Experimental Psychology. 2017. Vol. 10. No. 3, pp. 33—45. doi:10.17759/exppsy.2017100303.
  20. Kuravskii L.S., Margolis A.A., Marmalyuk P.A., Panfilova A.S., Yur’ev G.A. Matematicheskie aspekty kontseptsii adaptivnogo trenazhera [Mathematical aspects of the concept of the adaptive motion trainer]. Psikhologicheskaya nauka i obrazovanie [Psychological Science and Education (Russia)], 2016, V. 21, № 2, pp. 84—95. (In Russ.; abstract in Engl.) doi: 10.17759/pse.2016210210
  21. Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Panfilova A.S., Yuryev G.A., Dumin P.N. A Probabilistic Model of Adaptive Training. Applied Mathematical Sciences, Vol. 10, 2016, no. 48, pp. 2369 — 2380. URL: (Accessed 15.03.2020).
  22. Kuravskii L.S., Marmalyuk P.A., Yur’ev G.A. Diagnostika professional’nykh navykov na osnove veroyatnostnykh raspredelenii glazodvigatel’noi aktivnosti [Diagnostics of professional skills based on probabilistic distributions of oculomotor activity]. Vestnik RFFI [Bulletin RFFI (Russia)], 2016, №3(91), pp. 72—82.
  23. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A. and Dumin P.N. A Numerical Technique for the Identification of Discrete-State Continuous-Time Markov Models. Applied Mathematical Sciences. Vol. 9, 2015, No. 8, pp. 379—391. URL: 2015.410882 (Accessed 15.03.2015).
  24. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Belyaeva O.B. and Prokopieva O.Yu. Mathematical Foundations of Flight Crew Diagnostics Based on Videooculography Data. Applied Mathematical Sciences, Vol. 10, 2016, no. 30, pp. 1449—1466. URL: (Accessed 15.03.2020).
  25. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Dumin P.N., Panfilova A.S. Probabilistic modeling of CM operator activity on the base of the Rasch model. In: Proc. 12th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Oxford, UK, June 2015.
  26. Kuravsky L.S., Yuriev G.A. Probabilistic method of filtering artefacts in adaptive testing. Experimental Psychology, Vol.5, No. 1, 2012, pp. 119—131.
  27. Kuravskii L.S., Yur’ev G.A. Svidetel’stvo o gosudarstvennoi registratsii programmy dlya EHVM №2018660358 Intelligent System for Flight Analysis v1.0 (ISFA#1.0) / Pravoobladateli Kuravskii L.S., Yur’ev G.A. (Russia). — Zayavka №2018617617; Zayav. 18.07.2018; Zaregistr. 22.08.2018.—(ROSPATENT).
  28. Kuravsky L.S., Yuryev G.A. Detecting abnormal activities of operators of complex technical systems and their causes basing on wavelet representations. International Journal of Civil Engineering and Technology (IJCIET). Vol. 10(2). Issue 02, February 2019, pp. 724—742. URL: UploadFolder/IJCIET_10_02_070/IJCIET_10_02_070.pdf (Accessed 17.09.2019).
  29. Kuravsky L.S., Yuryev G.A. On the approaches to assessing the skills of operators of complex technical systems. In: Proc. 15th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Nottingham, UK, September 2018. P. 25.
  30. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I. New approaches for assessing the activities of operators of complex technical systems. Eksperimental’naya psikhologiya [Experimental psychology (Russia)], 2019, vol. 12, no. 4, pp. 27—49. (In Russ.; abstract in Engl.) doi:10.17759/exppsy.2019120403
  31. Laxhammar R., Falkman G. Online learning and sequential anomaly detection in trajectories. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014. Vol. 36, no. 6, pp. 1158—1173.
  32. Li Z., et al. Incremental clustering for trajectories. Database Systems for Advanced Applications. Lecture Notes in Computer Science. 2010. Vol. 5982, pp. 32—46.
  33. Liu H., Li J. Unsupervised multi-target trajectory detection, learning and analysis in complicated environments. 21st International Conference on Pattern Recognition (ICPR) / IEEE. 2012, pp. 3716— 3720.
  34. Markovskie modeli v zadachakh diagnostiki i prognozirovaniya [Markov models in the problems of diagnostics and forecasting]: Ucheb. posobie /Pod red. L.S. Kuravskogo. 2-e izd., dop. Moscow: MGPPU Pub-l, 2017. P. 197.
  35. Neal P.G. Multiresolution Analysis for Adaptive Refinement of Multiphase Flow Computations. University of Iowa, 2010. P. 116.
  36. René Vidal, Yi Ma, Shankar Sastry. Generalized Principal Component Analysis. Springer-Verlag: New York, 2016. URL: us/book/9780387878102 (Accessed 15.03.2020).
  37. Rintoul M., Wilson A. Trajectory analysis via a geometric feature space approach. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2015.
  38. Trevor F. Cox, M.A.A. Cox. Multidimensional Scaling, Second Edition. Chapman & Hall. CRC, 2001. P. 299.
  39. Wei J., et al. Design and Evaluation of a Dynamic Sectorization Algorithm for Terminal Airspace. Journal of Guidance, Control, and Dynamics. 2014. Vol. 37, no. 5, pp. 1539—1555.
  40. Wilson A., Rintoul M., Valicka C. Exploratory Trajectory Clustering with Distance Geometry. International Conference on Augmented Cognition. Springer. 2016, pp. 263—274.
  41. Xiangyu Kong, Changhua Hu, Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2017. P. 322. URL: (Accessed 15.03.2020).

© 2007–2020 Portal of Russian Psychological Publications. All rights reserved in Russian

Publisher: Moscow State University of Psychology and Education

Catalogue of academic journals in psychology & education MSUPE

Creative Commons License Open Access Repository

RSS Psyjournals at facebook Psyjournals at Twitter Psyjournals at Youtube ??????.???????