Modified decision support model for emergency evacuation in fuzzy conditions

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

A modified decision support model for emergency evacuation under fuzzy conditions has been developed based on a Gaussian mixture model. This model enables the formation of clusters of evacuees based on their spatial location, behavioral characteristics, and other factors relevant to the evacuation process. An algorithm for the Gaussian mixture model for evacuation under fuzzy conditions is described. А series of simulation experiments was conducted.

Full Text

Restricted Access

About the authors

Y. A. Kravchenko

Southern Federal University

Email: yakravchenko@sfedu.ru

Dr. of Tech. Sc., Professor

Russian Federation, Taganrog

V. V. Kureichik

Southern Federal University

Email: vkur@sfedu.ru

Dr. of Tech. Sc., Professor

Russian Federation, Taganrog

E. V. Kuliev

Southern Federal University

Author for correspondence.
Email: elmar_2005@mail.ru

Cand. of Tech. Sc., Associate Professor

Russian Federation, Taganrog

D. V. Zaruba

Southern Federal University

Email: dvzaruba@sfedu.ru

Cand. of Tech. Sc., Associate Professor

Russian Federation, Taganrog

S. V. Ignatieva

Southern Federal University

Email: signateva@sfedu.ru

Master’s Student

Russian Federation, Taganrog

References

  1. Chen G., Zhang X. Fuzzy-based methodology for performance assessment of emergency planning and its application Journal of Loss Prevention in the Process Industries, 2009, vol. 22, no. 2, pp. 125—132, doi: 10.1016/j.jlp.2008.10.003.
  2. Bryan J. L. А review of the examination and analysis of the dynamics of human behavior in the fire at the MGM grand hotel, clark county, Nevada as determined from a selected questionnaire population, Fire Safety Journal, 1983, vol. 5, no. 3—4, pp. 233—240, doi: 10.1016/0379-7112(83)90021-8.
  3. Xiao Q., Li T., Jiang C., Ding N., Wang J. А Social Force Evacuation Model with Guides Based on the Integration of Exit Information and Crowd Movement International Journal of Environmental Research and Public Health, 2022, vol. 19 (21), pp. 14015, doi: 10.3390/ijerph192114015.
  4. Islas-Toski A., Cuevas E., Pérez-Cisneros M., Escobar J. Agent-Based Evacuation Modeling: Enhancing Building Safety in Emergency Scenarios, Modelling, 2024, vol. 7 (6), p. 123—135, doi: 10.3390/modelling7060123.
  5. Ye J., Liu Z., Liu T., Wu Y., Wang Y. Crowd evacuation simulation based on hierarchical agent decision and agent motion model, Computer Animation and Virtual Worlds, 2024, vol. 35 (3—4), doi: 10.1002/cav.2263.
  6. Xiao Q., Li J. А Social Force Evacuation Model with Guides Based on Fuzzy Clustering and a Two-Layer Fuzzy Inference, Computational Intelligence and Neuroscience, 2022, 16 p., doi: 10.1155/2022/7700511.
  7. Dudorov V. E. Evacuation of the population: planning, organization, implementation, Scientific Leader, 2025, no. 50 (251), pp. 23—25.
  8. Sarkisov S. V., Ruzmanov M. D., Kondratyev S. A., Petrov D. K. ssues of people evacuation during fires and emergency situations, Actual problems of military scientific research, 2021, no. 6 (18), pp. 451—459.
  9. Gerasimenko E. M., Kureichik V. V., Rodzin S. I., Kukharenko А. P. Application of fuzzy logic for making decisions about evacuation during floods, Izvestiya SFU. Technical Sciences, 2022, no. 4 (228), pp. 15—29 (in Russian).
  10. Rodzin S. I., Bozhenyuk A. V., Kravchenko Yu. А., Rodzina O. N. Methods of fuzzy multi-criteria group decision-making for evacuation problems in emergency situations, Izvestiya SFU. Technical Sciences, 2023, no. 2 (232), pp. 186—200 (in Russian)
  11. Gerasimenko E. M., Kravchenko D. Yu., Kravchenko Yu. А., Kuliev E. V. Support for decision-making on the prevention and elimination of consequences of emergency situations based on a fuzzy method of information structuring, Izvestiya SFU. Technical Sciences, 2023, no. 2 (232), pp. 201—212 (in Russian)
  12. Kureichik V. V., Gerasimenko E. M. Integrated algorithm for multi-criteria group decision-making for evacuation problems, Computer Science and Automation, 2025, vol. 24, no. 5, pp. 1284—1332, doi: 10.15622/ia.24.5.6.
  13. Danilchenko E. V., Kureichik V. M. Definition of fuzzy conditions and analysis of existing solutions in the evacuation problem under emergency situations, Izvestiya SFU. Technical Sciences, 2022, no. 6, pp. 53—60 (in Russian).
  14. Kacprzyk J., Bozhenyuk A., Gerasimenko E. Lexicographic maximum dynamic evacuation modeling with partial lane reversal based on hesitant fuzzy TOPSIS, Applied Soft Computing, 2023, vol. 144, art. 110435, doi: 10.1016/j.asoc.2023.110435
  15. Kureichik V. V., Danilchenko V. I. Adaptive calibration of fuzzy models for early warning of emergency situations, Informacionnye Tehnologii, 2026, vol. 32, no. 3, pp. 115, doi: 10.17587/it.32.115-126.
  16. Kureichik V. V., Danilchenko V. I. Modified evolutionary algorithm MOLE-RAT with an adaptive mechanism for dynamic obstacle avoidance in emergency situations, Informacionnye Tehnologii, 2024, vol. 30, no. 7, pp. 342—349 (in Russian), doi: 10.17587/it.30.342-349.
  17. Pourrahmani E., Delavar M. R., Mostafavi M. A. Optimization of an evacuation plan with uncertain demands using fuzzy credibility theory and genetic algorithm, International journal of disaster risk reduction, 2015, vol. 14, pp. 357—372, doi: 10.1016/j.ijdrr.2015.08.008.
  18. Pidd M., De Silva F. N., Eglese R. W. А simulation model for emergency evacuation, European Journal of operational research, 1996, vol. 90, no. 3, pp. 413—419, doi: 10.1016/0377-2217(95)00117-4.
  19. Ye J. Group decision-making strategy based on aggregation operators of linguistic confidence interval neutrosophic numbers in a linguistic neutrosophic multivalued scenario, Engineering Applications of Artificial Intelligence, 2025, vol. 141, doi: 10.1016/j.engappai.2024.109823.
  20. Kuliev E. V., Kravchenko Yu. А., Zaporozhets D. Yu., Semenova M. M. Analysis of the risks of heavy rains and floods using the hybrid PSO-SVR algorithm, News of higher educational institutions. North Caucasus region. Technical sciences, 2023, no. 1 (217), pp. 23—29 (in Russian).
  21. Gerasimenko E. M., Kravchenko D. Yu., Kravchenko Yu.A., Kureichik V. V., Kuliev E. V., Rodzin S. I. Modified bioinspired method of decision support for the prevention and elimination of consequences of emergency situations, Informacionnye Tehnologii, 2023, vol. 29, no. 8, pp. 423—436 (in Russian), doi: 10.17587/it.29.423-436.
  22. Alvear D. et al. Decision support system for emergency management: Road tunnels, Tunnelling and Underground Space Technology, 2013, vol. 34, pp. 13—21, doi: 10.1016/j.tust.2012.10.003.
  23. Zhou L. et al. Emergency decision making for natural disasters: An overview, International journal of disaster risk reduction, 2018, vol. 27, pp. 567—576, doi: 10.1016/j.ijdrr.2017.09.037.
  24. Dell’Orco M., Marinelli M., Ottomanelli M. Simulation of crowd dynamics in panic situations using a fuzzy logic-based behavioural model, Computer-based Modelling and Optimization in Transportation, 2014, pp. 237—250.
  25. Chen J., Shi T., Li N. Pedestrian evacuation simulation in indoor emergency situations: Approaches, models and tools, Safety science, 2021, vol. 142, p. 105378, doi: 10.1016/j.ssci.2021.105378.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Hall layout

Download (23KB)
Indexing metadata
3. Fig. 2. Diagram of the initial placement of evacuees using the nearest exit method (t = 0 s)

Download (46KB)
Indexing metadata
4. Fig. 3. Diagram of the placement of evacuees after 5 seconds using the nearest exit method

Download (35KB)
Indexing metadata
5. Fig. 4. Diagram of the initial placement of evacuees using the central method (t = 0 s)

Download (39KB)
Indexing metadata
6. Fig. 5. Diagram of evacuee distribution using the central method after 5 seconds

Download (36KB)
Indexing metadata
7. Fig. 6. Diagram of the initial placement of evacuees by the Gaussian mixture model (t = 0 s)

Download (40KB)
Indexing metadata
8. Fig. 7. Diagram of the distribution of evacuees by the Gaussian mixture method after 5 seconds

Download (25KB)
Indexing metadata
9. Fig. 8. Number of people evacuated at each exit with an even initial distribution of evacuees. Nearest exit method

Download (25KB)
Indexing metadata
10. Fig. 9. Number of people evacuated at each exit with an even initial distribution of evacuees. Center method

Download (32KB)
Indexing metadata
11. Fig. 10. Number of evacuees at each exit with an even initial distribution of evacuees. Gaussian mixture model

Download (26KB)
Indexing metadata

Copyright (c) 2026 Informacionnye Tehnologii


СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № 77 - 15565 от 02 июня 2003 г.