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Emergency Evacuation Planning via the Point of View on the Relationship Between Crowd Density and Moving Speed

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Abstract

Emergency evacuation is concerning to both industry and academics. This work applied the technology of a wireless sensor network to design and propose a nonlinear mathematical model and heuristic approach to fire emergency evacuation problem. The proposed model first considers basic factors including the distribution of people in each area, possible safe exit locations, possible hazard locations, rooms, open spaces, doors, and junctions. Based on these common factors and the proposed heavy smoke diffusion, the objective of the proposed model is to evacuate all people in the shortest time through the concept of load balance coming from the relationship between crowd density and moving speed. The algorithm of simulated annealing is finally applied to solve the evacuation planning problem derived from the proposed model. Experimental results verify the efficiency of the proposed model and the resulting solution.

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References

  1. Radianti, J., Granmo, O.-C., Bouhmala, N., Sarshar, P., Yazidi, A., & Gonzalez, J. (2013) Crowd models for emergency evacuation: A review targeting human-centered sensing. In Proceedings of the 46th annual Hawaii international conference on system sciences (HICSS ’13) (pp. 156–165). IEEE.

  2. Barnes, M., Leather, H., & Arvind, D. K. (2007). Emergency evacuation using wireless sensor networks. In Proceedings of the 32nd IEEE conference on local computer networks (LCN 2007) (pp. 851–857).

  3. Tabirca, T., Brown, K. N., & Sreenan, C. J. (2009). A dynamic model for fire emergency evacuation based on wireless sensor networks. In Proc. int. symp. parallel distrib. comput. (pp. 29–36).

  4. Hadzic, T., Brown, K. N., & Sreenan, C. J. (2011). Real-time pedestrian evacuation planning during emergency. In Proc. IEEE int. conf. tools artif. intell. (pp. 597–604).

  5. Chen, L.-W., Cheng, J.-H., & Tseng, Y.-C. (2015). Optimal path planning with spatial-temporal mobility modeling for individual-based emergency guiding. IEEE Transactions Systems, Man, And Cybernetics: Systems, 45(12), 1491–1501.

    Article  Google Scholar 

  6. Chen, L.-W., Cheng, J.-H., & Tseng, Y.-C. (2016). Distributed emergency guiding with evacuation time optimization based on wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 27(2), 419–427.

    Article  Google Scholar 

  7. Escolar, S., Villa, D., Villanueva, F., Cantarero, R., & Lopez, J. (2016). An adaptive emergency protocol for people evacuation in high-rise buildings. In 2016 IEEE symposium on computers and communication (ISCC). https://doi.org/10.1109/iscc.2016.7543767

  8. Wagner, N., & Agrawal, V. (2014). An agent-based simulation system for concert venue crowd evacuation modelling in the presence of a fire disaster. Expert Systems with Applications, 41(6), 2807–2815.

    Article  Google Scholar 

  9. Wang, J., Zhang, L., Shi, Q., Yang, P., & Hu, X. (2015). Modelling and simulating for congestion pedestrian evacuation with panic. Physica A: Statistical Mechanics and Its Applications, 428, 396–409.

    Article  Google Scholar 

  10. Zhao, X., Ren, G., & Huang, Z. (2016). Optimizing one-way traffic network reconfiguration and lane-based non-diversion routing for evacuation. Journal of Advanced Transportation, 50(4), 589–607.

    Article  Google Scholar 

  11. Ma, J., Smith, B. L., & Zhou, X. (2016). Personalized real-time traffic information provision: Agent-based optimization model and solution framework. Transportation Research Part C Emerging Technologies, 64, 164–182.

    Article  Google Scholar 

  12. Vermuyten, H., Beliën, J., De Boeck, L., Reniers, G., & Wauters, T. (2016). A review of optimisation models for pedestrian evacuation and design problems. Safety Science, 87, 167–178. https://doi.org/10.1016/j.ssci.2016.04.001

    Article  Google Scholar 

  13. Yi, W., Nozick, L., Davidson, R., Blanton, B., & Colle, B. (2017). Optimization of the issuance of evacuation orders under evolving hurricane conditions. Transportation Research Part B, 95, 285–304.

    Article  Google Scholar 

  14. Yan, X., Liu, X., & Song, Y. (2018). Optimizing evacuation efficiency under emergency with consideration of social fairness based on a cell transmission model. PLoS ONE, 13(11), e0207916.

    Article  Google Scholar 

  15. Lu, X., Yang, Z., Cimellaro, G. P., & Xu, Z. (2019). Pedestrian evacuation simulation under the scenario with earthquake-induced falling Debris. In Safety science (vol. 114, pp. 61–71). Elsevier B.V.

  16. Zhang, H., Song, X., Song, X., Huang, D., Ning, Xu., Shibasaki, R., & Liang, Y. (2019). Ex-ante online risk assessment for building emergency evacuation through multimedia data. PLoS ONE. https://doi.org/10.1371/journal.pone.0215149

    Article  Google Scholar 

  17. Mahmood, I., Nadeem, T., Hu, X., & Bibi, F. (2019). Analyzing emergency evacuation strategies for large buildings using crowd simulation framework. In Proceedings of the 2019 winter simulation conference (pp. 3116–3127).

  18. Datta, S., & Behzadan, A. H. (2019). Modeling and simulation of large crowd evacuation in hazard impacted environments. Advances in Computational Design, 4(2), 91–118.

    Google Scholar 

  19. Buratti, C., Conti, A., Dardari, D., & Verdone, R. (2009). An overview on wireless sensor networks technology and evolution. Sensors, 2009(9), 6869–6896.

    Article  Google Scholar 

  20. Kocakulak, M., & Butun, I. (2017). An overview of wireless sensor networks towards internet of things. In IEEE 7th annual computing and communication workshop and conference (CCWC) 2017.

  21. Prakash, V., Pandey, S., & Singh, A. K. (2019). Basic introduction of wireless sensor network. In 2nd international conference on advanced computing and software engineering (ICACSE-2019) 2019.

  22. Chang, C.-L., Chang, C.-Y., Chen, S.-T., Tu, S.-Y., & Ho, K.-Y. (2019). Optimisation-based time slot assignment and synchronisation for TDMA MAC in industrial wireless sensor network. IET Communications, 13(18), 2932–2940.

    Article  Google Scholar 

  23. Chang, C.-L., Chen, C.-J., Lee, H.-T., Chang, C.-Y., & Chen, S.-T. (2020). Bounding the sensing data collection time with ring-based routing for industrial wireless sensor networks. Journal of Internet Technology, 21(3), 673–680.

    Google Scholar 

  24. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Math, 1(1), 269–271.

    Article  MathSciNet  Google Scholar 

  25. Perkins, C. E., & Bhagwat, P. (1994). Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. In Proc. conf. commun. archit. protocols appl. (SIGCOMM) (pp. 234–244).

  26. Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing. In Proc. 2nd IEEE workshop mobile computing syst. appl. (WMCSA) (pp. 99–100).

  27. He, T., Ren, F., Lin, C., & Das, S. (2008). Alleviating congestion using traffic-aware dynamic routing in wireless sensor networks. In Proc. 5th annu. IEEE commun. soc. conf. sensor. mesh ad hoc commun. netw. (SECON) (pp. 233–241).

  28. Cao, Q., Abdelzaher, T., He, T., & Kravets, R. (2007). Cluster-based forwarding for reliable end-to-end delivery in wireless sensor networks. In Proc. 26th IEEE int. conf. comput. commun. (INFOCOM) (pp. 1928–1936).

  29. http://www.abri.gov.tw/tw/research/dl/54/1

  30. 2011 Taiwan Highway Capacity Manual. http://mail.tku.edu.tw/yinghaur/lee/hwy-new/2011%E5%B9%B4%E8%87%BA%E7%81%A3%E5%85%AC%E8%B7%AF%E5%AE%B9%E9%87%8F%E6%89%8B%E5%86%8A(PBA039).pdf

  31. Granville, V., Krivanek, M., & Rasson, J.-P. (1994). Simulated annealing: A proof of convergence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 652–656.

    Article  Google Scholar 

  32. Bouttier, C., & Gavra, I. (2019). Convergence rate of a simulated annealing algorithm with noisy observations. Journal of Machine Learning Research, 20, 1–45.

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

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Funding

This work was financially supported by the “Intelligence Recognition Industry Service Research Center (IR-IS Research Center)” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan, R.O.C.

    Ching-Lung Chang, Yi-Lin Tsai, Chuan-Yu Chang & Shuo-Tsung Chen

  2. Intelligence Recognition Industry Service Research Center (IR-IS Research Center), National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan, R.O.C.

    Ching-Lung Chang & Chuan-Yu Chang

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  1. Ching-Lung Chang
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  2. Yi-Lin Tsai
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Contributions

Conceptualization, C-LC and S-TC; methodology, C-LC and S-TC; software, Y-LT and C-YC; validation, S-TC and Y-LT; writing—review and editing, S-TC; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shuo-Tsung Chen.

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Chang, CL., Tsai, YL., Chang, CY. et al. Emergency Evacuation Planning via the Point of View on the Relationship Between Crowd Density and Moving Speed. Wireless Pers Commun 119, 2577–2602 (2021). https://doi.org/10.1007/s11277-021-08345-y

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