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Forecasting pedestrian evacuation times by using swarm intelligence

Author

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  • Izquierdo, J.
  • Montalvo, I.
  • Pérez, R.
  • Fuertes, V.S.

Abstract

Many models have been developed to provide designers with methods for forecasting the time required for evacuation from various places under a variety of conditions. Particularly for high traffic buildings or buildings of cultural, governmental, or industrial importance, it is of paramount importance to properly evaluate and plan for the necessary evacuation time. To address this need, a number of models for pedestrian simulation, either considering the system as a whole or studying the behavior and decisions of individual pedestrians and their interactions with other pedestrians, have been developed over the years. In this work, a model for evacuation simulation and for estimating evacuation times is proposed. It is inspired by the so-called Particle Swarm Optimization (PSO). The multi-agent-based simulation characteristics of PSO and the way this technique combines individual and collective intelligence make it suitable for this problem. The PSO-based model presented here allows for assessment of the behavioral patterns followed by individuals during a rapid evacuation event. Evaluation of these behaviors can address a variety of public safety concerns, such as architectural design, evacuation protocol definition, and regulation of public space.

Suggested Citation

  • Izquierdo, J. & Montalvo, I. & Pérez, R. & Fuertes, V.S., 2009. "Forecasting pedestrian evacuation times by using swarm intelligence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1213-1220.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:7:p:1213-1220
    DOI: 10.1016/j.physa.2008.12.008
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    References listed on IDEAS

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    Cited by:

    1. Li, Lin & Yu, Zhonghai & Chen, Yang, 2014. "Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 157-172.
    2. Zheng, Yaochen & Chen, Jianqiao & Wei, Junhong & Guo, Xiwei, 2012. "Modeling of pedestrian evacuation based on the particle swarm optimization algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4225-4233.
    3. Qi, Jie & Rong, Zhihai, 2013. "The emergence of scaling laws search dynamics in a particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1522-1531.
    4. Abdelghany, Ahmed & Abdelghany, Khaled & Mahmassani, Hani, 2016. "A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 159-176.
    5. Zheng, Xiaoping & Li, Wei & Guan, Chao, 2010. "Simulation of evacuation processes in a square with a partition wall using a cellular automaton model for pedestrian dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2177-2188.
    6. Abdelghany, Ahmed & Abdelghany, Khaled & Mahmassani, Hani & Alhalabi, Wael, 2014. "Modeling framework for optimal evacuation of large-scale crowded pedestrian facilities," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1105-1118.
    7. Hassanpour, Sajjad & Rassafi, Amir Abbas & González, Vicente A. & Liu, Jiamou, 2021. "A hierarchical agent-based approach to simulate a dynamic decision-making process of evacuees using reinforcement learning," Journal of choice modelling, Elsevier, vol. 39(C).
    8. Moonsoo Ko & Taewan Kim & Keemin Sohn, 2013. "Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation," Transportation, Springer, vol. 40(1), pages 91-107, January.
    9. Rodriguez, E. & Espinosa-Paredes, G. & Alvarez-Ramirez, J., 2014. "Convection–diffusion effects in marathon race dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 498-507.

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