Predicting Congestions in a Ship Fire Evacuation: A Dynamic Bayesian Networks Simulation
In this paper, some new simulation results achieved from our proposed simulation model for analyzing congestions in ship evacuation are presented. To guarantee a safe evacuation, this model considers the most important real-life factors including, but not limited to, the passengers’ panic, the age and sex of the passengers, the structure of the ship, and so on. The qualitative factors have been quantized in order to compute the probability of congestion during the entire evacuation. We then utilize the dynamic Bayesian network (DBN) to predict congestion and to handle the non-stationarity of the scenario with respect to the time. Considering the most important scenarios and running the simulation, we demonstrate the distinct effects of these factors on congestion. The role of decision supports (DS), i.e. smartphone evacuation applications and rescue team presence on congestion is also studied. In addition, the impact of congested escape routes on the evacuation time is also investigated. The presented model and results of this paper are possible decision support tools for maritime organizations, emergency management sectors, and rescuers onboard the ships, which try to alleviate the human or property losses.
KeywordsCongestion Decision support systems Dynamic bayesian networks Evacuation time Simulation Ship fire
The research reported here has been partially funded by the research grant awarded to the SmartRescue project by Aust-Agder Utvikling- og Kompetansefond.
- 1.IMO, Interim guidelines for evacuation analyses for new and existing passenger ships, MSC/Circ. 1033 (2007)Google Scholar
- 4.H. Klüpfel, T. Meyer-König, J. Wahle, M, Schreckenberg, Microscopic simulation of evacuation processes on passenger ships. in ACRI, pp. 63–71 (2000)Google Scholar
- 6.A. Ferscha, K. Zia, On the efficiency of lifebelt based crowd evacuation, in Proceedings of the 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, pp. 13–20 (2009)Google Scholar
- 8.A. Fujihara, H. Miwa, Effect of traffic volume in real-time disaster evacuation guidance using opportunistic communications, in 2012 4th International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 457–462 (2012)Google Scholar
- 9.P. Sarshar, J. Radianti, O.-C. Granmo, J.J. Gonzalez, A dynamic Bayesian network model for predicting congestion during a ship fire evacuation, in Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science, WCECS 2013, San Francisco, USA, pp. 29–34, 23–25 Oct 2013Google Scholar
- 10.J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, San Mateo, 1988)Google Scholar
- 11.J.-L. Molina, D. Pulido-Velázquez, J.L. García-Aróstegui, M. Pulido-Velázquez. Dynamic Bayesian networks as a decision support tool for assessing climate change impacts on highly stressed groundwater systems. J. Hydrol. 479, 113–129 (2013) Google Scholar
- 12.D.S. Laboratory (1998) GeNIe & SMILE (Online), http://genie.sis.pitt.edu/about.html#genie
- 13.P. Sarshar, J. Radianti, J.J. Gonzalez, Modeling panic in ship fire evacuation using dynamic Bayesian network, in Innovative Computing Technology (INTECH), 2013 Third International Conference, 2013, pp. 301–307Google Scholar