Abstract
Managing the uncertainties that arise in disasters – such as ship fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd with hazard dynamics, using a ship fire as a proof-of-concept scenario. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior – both descriptive and normative (optimal). Descriptive modeling is based on studies of physical fire models, crowd psychology models, and corresponding flow models, while we identify optimal behavior using Ant-Based Colony Optimization (ACO). Simulation results demonstrate that the DNB model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Furthermore, the ACO provides safe paths, dynamically responding to current threats.
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References
World Fire Statistics Center.: World fire statistics bulletin. World Fire Statistics Bulletin, Valéria Pacella, Geneva (2012)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, inference and learning. PhD Dissertation, University of California, Berkeley (2002)
Charniak, E.: Bayesian networks without tears. AI Magazine 12(4), 50–63 (1991)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo (1988)
Eleye-Datubo, A.G., Wall, A., Saajedi, A., Wang, J.: Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Analysis 26(3), 695–721 (2006)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization - artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)
Jian, C., Marek, J.D.: AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks. Journal of Artificial Intelligence Research 13, 155–188 (2000)
Thompson, P.A., Marchant, E.W.: A computer model for the evacuation of large building populations. Fire Safety Journal 24(2), 131–148 (1995)
Thompson, P.A., Marchant, E.W.: Testing and application of the computer model simulex. Fire Safety Journal 24(2), 149–166 (1995)
Hamacher, H., Tjandra, S.: Mathematical modelling of evacuation problems – a state of the art. Pedestrian and Evacuation Dynamics 2002, 227–266 (2002)
Kim, D., Shin, S.: Local path planning using a new artificial potential function composition and its analytical design guidelines. Advanced Robotics 20(1), 115–135 (2006)
Braun, A., Bodmann, B., Musse, S.: Simulating virtual crowds in emergency situations. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 244–252. ACM (2005)
Liu, S., Yang, L., Fang, T., Li, J.: Evacuation from a classroom considering the occupant density around exits. Physica A: Statistical Mechanics and its Applications 388(9), 1921–1928 (2009)
Wang, J.H., Lo, S.M., Sun, J.H., Wang, Q.S., Mu, H.L.: Qualitative simulation of the panic spread in large-scale evacuation. Simulations 88, 1465–1474 (2012)
Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)
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Granmo, OC. et al. (2013). A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_7
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DOI: https://doi.org/10.1007/978-3-642-38577-3_7
Publisher Name: Springer, Berlin, Heidelberg
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