Pythagorean Theorem for Shortest Distance in CA Based Pedestrian Simulation: A Case Study on the Closed Area
Cellular Automata (CA) model approach has become a well-known approach in demonstrating the microscopic movement of pedestrian for path finding and navigation to reach point of interest. One of the features in the CA movement model is to find the shortest path distance that will navigate the pedestrian movement towards point of interest in the fastest of time. Nowadays, there are many path navigation systems that were constructed by using the pre-defined movement simulation based on the static obstacles’ layout. However, the pre-defined movement simulation is not able to highlight the near-realistic movement of the pedestrian due to the dynamic obstacle’s avoidance and physical collision that will affect the pedestrian movement direction’s selection and safety. Hence, this research has simulated the enhanced transition direction of the CA based pedestrian movement using the optimal Moore Neighborhood transition method and has proposed the Pythagorean Theorem for the shortest distance path finding for assisting the pedestrian direction selection. This research has proven that the enhanced movement transition direction and the implementation of Pythagorean Theorem able to construct an intelligent and near-realistic pedestrian simulation that able to evacuate in fastest time (<25 s) from the selected spatial layout design compared to the transition method based pedestrian movement simulation.
KeywordsPythagorean theorem Shortest distance Cellular automata Moore neighborhood transition Pedestrian movement
Research experiment is pursued under the FRGS grant by Ministry of Education Malaysia [203.PKOMP.6711534], Bridging Grant by Universiti Sains Malaysia [304.PKOMP.6316019] and Research University Grant by Universiti Sains Malaysia [1001.PKOMP.8014073].
- 1.Chiang, P.N., Taaffe, K.: Analysis of passenger flow in airport terminal. In: 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2014)Google Scholar
- 3.Weronek, K.: The impact of indoor navigation systems for public malls—a comprehensive overview. In: Linnhoff-Popien, C., Schneider, R., Zaddach, M. (eds.) Digital Marketplaces Unleashed, pp. 499–511. Springer, Berlin (2018)Google Scholar
- 4.Haghpanah, F., Mitrani-Reiser, J., Schafer, B.W.: Performance evaluation of pedestrian navigation algorithms for city evacuation modeling (2018). arXiv:1801.09604
- 5.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
- 6.Gan, Y., Feng, Z.P.: The Cognitive Airport Signage System Design: Comparative Case Study Between American Airport and Chinese Airport. Springer International Publishing, Cham (2018)Google Scholar
- 7.Hassan, F.H.: Using microscopic pedestrian simulation statistics to find clogging regions. In: 2016 SAI Computing Conference (SAI) (2016)Google Scholar
- 9.Kihlstrom, J.F.: The person-situation interaction. The Oxford Handbook of Social Cognition, pp. 786–805 (2013)Google Scholar
- 11.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
- 13.Ibrahim, N., et al.: Features of microscopic horizontal transition of cellular automaton based pedestrian movement in normal and panic situation. J. Telecommun. Electron. Comput. Eng. (JTEC) 9(2–12), 163–169 (2017)Google Scholar
- 15.Tavakoli, Y., Javadi, H.H.S., Adabi, S.: A cellular automata based algorithm for path planning in multi-agent systems with a common goal. Int. J. Comput. Sci. Netw. Secur. 8(7), 119–123 (2008)Google Scholar