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An Induction Scheme of Fast Initiative-Evacuation Based on Social Graphs

  • Taiyo, Ichinose (Graduate School of Systems and Information Engineering, University of Tsukuba) ;
  • Tomoya, Kawakami (Graduate School of Engineering, University of Fukui)
  • Received : 2022.03.10
  • Accepted : 2022.07.08
  • Published : 2022.12.31

Abstract

Early evacuations reduce the damage caused by catastrophic events such as terrorism, tsunamis, heavy rains, landslides, and river floods. However, even when warnings are issued, people do not easily evacuate during these events. To shorten the evacuation time, initiative-evacuation and its executors, initiative evacuees, are crucial in inducing other evacuations. The initiative evacuees take the initiative in evacuating and call out to their surroundings. This paper proposes a fast method to induce initiative-evacuation based on social graphs. The candidates are determined in descending order of the number of links for each person. The proposed method was evaluated through simulations. The simulation results showed a significant reduction in evacuation time.

Keywords

Acknowledgement

This work was partially supported by JSPS KAKENHI (Grant No. JP22K12009), Hokuriku Regional Management Service Association, and Hoso Bunka Foundation.

References

  1. C. D. Wickens, S. Rice, D. Keller, S. Hutchins, J. Hughes, and K. Clayton, "False alerts in air traffic control conflict alerting system: Is there a "cry wolf" effect?," Human Factors, vol. 51, no. 4, pp. 446-462, 2009. https://doi.org/10.1177/0018720809344720
  2. K. Uchida, "A model evaluating effect of disaster warning issuance conditions on "cry wolf syndrome" in the case of a landslide," European Journal of Operational Research, vol. 218, no. 2, pp. 530-537, 2012. https://doi.org/10.1016/j.ejor.2011.10.050
  3. A. Rigos, E. Mohlin, and E. Ronchi, "The cry wolf effect in evacuation: a game-theoretic approach," Physica A: Statistical Mechanics and its Applications, vol. 526, article no. 120890, 2019. https://doi.org/10.1016/j.physa.2019.04.126
  4. J. Urata and E. Hato, "Dynamics of local interactions and evacuation behaviors in a social network," Transportation Research Part C: Emerging Technologies, vol. 125, article no. 103056, 2021. https://doi.org/10.1016/j.trc.2021.103056
  5. C. J. Kuhlman, A. Marathe, A. Vullikanti, N. Halim, and P. Mozumder, "Natural disaster evacuation modeling: the dichotomy of fear of crime and social influence," Social Network Analysis and Mining, vol. 12, article no. 13, 2022. https://doi.org/10.1007/s13278-021-00839-8
  6. M. Drobyshevskiy and D. Turdakov, "Random graph modeling: a survey of the concepts," ACM Computing Surveys, vol. 52, no. 6, pp. 1-36, 2019.
  7. T. Ichinose and T. Kawakami, "A fast induction method of initiative-evacuation based on social graphs," in Proceedings of the World IT Congress 2022, Jeju, Korea, 2022.
  8. J. M. Almendros-Jimenez, A. Becerra-Teron, and M. Torres, "The retrieval of social network data for Pointsof-Interest in Open-StreetMap," Human-centric Computing and Information Sciences, vol. 11, article no. 10, 2021. https://doi.org/10.22967/HCIS.2021.11.010
  9. H. Cao, "Personalized web service recommendation method based on hybrid social network and multiobjective immune optimization," Journal of Information Processing Systems, vol. 17, no. 2, pp. 426-439, 2021. https://doi.org/10.3745/JIPS.01.0071
  10. L. Sun, "POI recommendation method based on multi-source information fusion using deep learning in location-based social networks," Journal of Information Processing Systems, vol. 17, no. 2, pp. 352-368, 2021. https://doi.org/10.3745/JIPS.01.0068
  11. N. A. Christakis and J. H. Fowler, "Social network sensors for early detection of contagious outbreaks," PLoS One, vol. 5, no. 9, article no. e12948, 2010. https://doi.org/10.1371/journal.pone.0012948
  12. S. Tsugawa, H. Ohsaki, Y. Itoh, N. Ono, K. Kagawa, and K. Takashima, "Dynamic social network analysis with heterogeneous sensors in ambient environment," Social Networking, vol. 3, pp. 9-18, 2014. https://doi.org/10.4236/sn.2014.31002
  13. H. Shao, K. S. M. Hossain, H. Wu, M. Khan, A. Vullikanti, B. A. Prakash, M. Marathe, and N. Ramakrishnan, "Forecasting the flu: designing social network sensors for epidemics," 2016 [Online]. Available: https://arxiv.org/abs/1602.06866.
  14. P. Mei, G. Ding, Q. Jin, and F. Zhang, "Research on emotion simulation method of large-scale crowd evacuation under particle model," Human-centric Computing and Information Sciences, vol. 11, article no. 1, 2021. https://doi.org/10.22967/HCIS.2021.11.001
  15. J. Li, H. Zhang, and Z. Ni, "Improved social force model based on navigation points for crowd emergent evacuation," Journal of Information Processing Systems, vol. 16, no. 6, pp. 1309-1323, 2020. https://doi.org/10.3745/JIPS.04.0199
  16. Kozo Keikaku Engineering Inc., "artisoc Cloud," 2021 [Online]. Available: https://mas.kke.co.jp/en/.
  17. Y. Yang and J. Zhu, "Write skew and Zipf distribution: evidence and implications," ACM Transactions on Storage, vol. 12, no. 4, article no. 21, 2016.