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Optimal Moore Neighborhood Approach of Cellular Automaton Based Pedestrian Movement: A Case Study on the Closed Area

  • Najihah IbrahimEmail author
  • Fadratul Hafinaz Hassan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

Closed area building is the most dangerous structure that caused major casualties compared to open space during panic situation due to the limited access of space. The unfamiliarity of the pedestrian and the unstructured arrangement of the space area (e.g. furniture, exit point, rooms, workspace and etc.) had caused the high physical collision that can cause casualties and heavy injuries. Hence, this research is to simulate the pedestrian movement to find the impact of building’s familiarity and space design towards the pedestrian movement speed. The familiarity of the pedestrian had been tested with the horizontal movement of Von Neumann approach and optimal criterion of Moore Neighborhood approach for a closed building area with the randomization of spatial layout obstacles arrangement. The optimal Moore Neighborhood is able to re-enact the real behavior of the pedestrian with high familiarization. Hence, this research had proven that the pedestrian with high familiarity of a building escape route had moved faster with higher speed. The pedestrian movement speed was improved with the feasible spatial layout design of the closed area building that is able to shape the flow of the pedestrian’s movement and reduce the physical collision.

Keywords

Cellular automata Pedestrian movement Moore neighborhood Von neumann Closed area building Spatial layout design 

Notes

Acknowledgment

Research experiment reported here is pursued under the Fundamental Research Grant Scheme (FRGS) by Ministry of Education Malaysia for “Enhancing Genetic Algorithm for Spatial Layout Design Optimization with Pedestrian Simulation in a Panic Situation” [203.PKOMP.6711534] and Bridging Grant by Universiti Sains Malaysia for “Pedestrian Simulation Model for Clogging Detection and Survival Prediction in a Fire Spreading Situation” [304.PKOMP.6316019]. The preliminary study of this research is supported under the Short Term Grant Scheme by Universiti Sains Malaysia for “Pedestrian Simulator and Heuristic Search Methods for Spatial Layout Design” [304.PKOMP.6313169].

References

  1. 1.
    Sime, J.D.: Crowd psychology and engineering. Saf. Sci. 21(1), 1–14 (1995)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Huixian, J., Shaoping, Z.: Navigation system design of fire disaster evacuation path in buildings based on mobile terminals. In: 2016 11th International Conference on Computer Science & Education (ICCSE) (2016)Google Scholar
  3. 3.
    Tcheukam, A., Djehiche, B., Tembine, H.: Evacuation of multi-level building: design, control and strategic flow. In: 2016 35th Chinese Control Conference (CCC) (2016)Google Scholar
  4. 4.
    Lu, X., et al.: Impacts of anxiety in building fire and smoke evacuation: modeling and validation. IEEE Robot. Autom. Lett. 2(1), 255–260 (2017)CrossRefGoogle Scholar
  5. 5.
    Jay, B.N.: Tahfiz did not have fire exit; bodies found piled on top of each other, in New Straits Times. New Straits Times Press, Berhad (2017)Google Scholar
  6. 6.
    Four in a Family Killed in Fire. The Star Online. Star Media Group Berhad (ROC 10894D) (2017)Google Scholar
  7. 7.
    Yamin, M., Al-Ahmadi, H.M., Muhammad, A.A.: Integrating social media and mobile apps into Hajj management. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (2016)Google Scholar
  8. 8.
    Konstantara, K., et al.: Parallel implementation of a cellular automata-based model for simulating assisted evacuation of elderly people. In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) (2016)Google Scholar
  9. 9.
    Ruiz, S., Hernández, B.: A parallel solver for markov decision process in crowd simulations. In: 2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) (2015)Google Scholar
  10. 10.
    Hassan, F.H.: Using microscopic pedestrian simulation statistics to find clogging regions. In: 2016 SAI Computing Conference (SAI) (2016)Google Scholar
  11. 11.
    Kihlstrom, J.F.: The person-situation interaction. In: The Oxford Handbook of Social Cognition, pp. 786–805 (2013)Google Scholar
  12. 12.
    Zong, X., Jiang, Y.: Pedestrian-vehicle mixed evacuation model based on multi-particle swarm optimization. In: 2016 11th International Conference on Computer Science & Education (ICCSE) (2016)Google Scholar
  13. 13.
    Wang, H., et al.: Simulation research based on evacuation ability estimation method. In: 2016 12th World Congress on Intelligent Control and Automation (WCICA) (2016)Google Scholar
  14. 14.
    Miao, Q., Lv, Y., Zhu, F.: A cellular automata based evacuation model on GPU platform. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (2012)Google Scholar
  15. 15.
    Wineman, J.D., Peponis, J.: Constructing spatial meaning: spatial affordances in museum design. Environ. Behav. 42(1), 86–109 (2010)CrossRefGoogle Scholar
  16. 16.
    Yue, H., et al.: Simulation of pedestrian flow on square lattice based on cellular automata model. Phys. A 384(2), 567–588 (2007)CrossRefGoogle Scholar
  17. 17.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer SciencesUniversiti Sains MalaysiaGeorge TownMalaysia

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