Predicting Evacuation Capacity for Public Buildings

Pejman Kamkarian, Henry Hexmoor

Abstract

This paper demonstrates a solution for analyzing public space evacuation rates. Evacuating from a public building in a reasonable amount of time is reliant upon how safe the space is in terms of achieving a minimum time to move people outside. In order to increase the safety of evacuation in public spaces, we employed the Bayesian Belief Network method. To have a better estimation pattern, we have to focus on important physical environmental features as well as crowd formation and specifications in a public space.

References

  1. Barnett, G. O., Famiglietti, K. T., Kim, R. J., Hoffer, E. P., Feldman, M.J., 1998. DXplain on the Internet. In American Medical Informatics Association 1998 Annual Symposium.
  2. Berler, A., Shimony, S. E., 1997.Bayes Networks for Sonar Sensor Fusion.In Geiger, D, Shenoy, P (eds) 1997.Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence Morgan Kaufmann.
  3. Cooper, G. F., 1984. NESTOR: A computer-based medical diagnostic aid that integrates causal and probabilistic knowledge.In Ph.D. dissertation.Medical Information Sciences, Stanford University.
  4. Cooper, G. F., 1987. Probabiistic inference using belief networks is NP-hard.In Technical Report KSL-87-27, Medical Computer Science Group.Stanford University.
  5. Davies, T. R., Russell, S. J., 1987. A logical approach to reasoning by analogy. In IJCAI 10. pp. 264-270.
  6. Duda, R. O., Hart, P. E., Nilsson N. J., 1976.Subjective Bayesian methods for rule-based inference systems.In Proceedings of the 1976 National Computer Conference (AFIPS Press). pp. 1075-1082.
  7. Ezawa, K. J., Schuermann, T., 1995.Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures. In Besnard, P, Hanks, S (eds) Proceedings of the Eleventh Conference on Uncertainty. in Artificial Intelligence Morgan Kaufmann.
  8. Frey, B. J., 1998.Graphical Models for Machine Learning and Digital Communication.In MIT Press.
  9. Good, I.J., 1961-62.A causal calculus (I & II).In British Journal of the Philosophy of Science.pp. 305-318 12: 43-51.
  10. Helm, L., October 28, 1996.Improbable Inspiration.In Los Angeles Times.
  11. Howard, R. A., Matheson, J. E., 1984. Influence Diagrams. In Howard, R. A. and Matheson, J. E. (eds.).The Principles and Applications of Decision Analysis, (Strategic Decisions Group, Menlo Park, CA). pp. 721-762.
  12. Lauritzen, S. L., Spiegelhalter, D. J., 1988. Local computations with probabilities on graphical structures and their application to expert systems. In Journal of the Royal Statistical Society.Vol.50 No. 2 pp.157-224.
  13. Neil, M., Littlewood, B., Fenton, N., 1996.Applying Bayesian Belief Networks to Systems Dependability Assessment.In Proceedings of Safety Critical Systems Club Symposium, Leeds.6-8 February 1996 SpringerVerlag.
  14. Pearl, J., 1986. Fusion, propagation and structuring in belief networks. In Artificial Intelligence 29 241-288.
  15. Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: networks of plausible inference.In Morgan Kaufmann.
  16. Pires, T. T., 2005. An approach for modeling human cognitive behavior in evacuation models, In Fire Safety Journal, Vol. 40, No. 2, March 2005, Pages 177-189, Elsevier Pub.
  17. Roozenburg, N. F. M., Eekels, J., 1995. Product Design: Fundamentals and Methods.In Wiley.
  18. Rousseau, W. F., 1968. A method for computing probabilities. In complex situations, Technical Report 6252-2. Stanford University Center for Systems Research.
  19. Shachter, R. D., 1986.Intelligence Probabilistic inference.In Kanal, L. N., Lemmer, J. F. (eds.), Uncertainly in Artificial Intelligence (North-Holland, Amsterdam). pp. 371-382.
  20. Shendarkarb, A., Vasudevana, K., Leea, S., Son, Y., 2008. Crowd simulation for emergency response using BDI agents based on immersive virtual reality, Simulation Modelling Practice and Theory, Vol. 16, No. 9, Pages 1415-1429, Elsevier Pub.
  21. Still, G. K., 2000. Crowd dynamics, Doctoral dissertation. University of Warwick, U. K.
  22. Trautman, P., Krause, A., 2010. Unfreezing the Robot: Navigation in Dense, Interacting Crowds, IROS proceedings, pp. 797-803, IEEE press.
  23. Weiss, S. M., Kulikowski C. A., Amarel S. and Safir A.,1978. A model-based method for computer-aided medical decision making. In Artificial Intelligence.pp. 145-172.
Download


Paper Citation


in Harvard Style

Kamkarian P. and Hexmoor H. (2013). Predicting Evacuation Capacity for Public Buildings . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 433-440. DOI: 10.5220/0004217704330440


in Bibtex Style

@conference{icaart13,
author={Pejman Kamkarian and Henry Hexmoor},
title={Predicting Evacuation Capacity for Public Buildings},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={433-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004217704330440},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Predicting Evacuation Capacity for Public Buildings
SN - 978-989-8565-39-6
AU - Kamkarian P.
AU - Hexmoor H.
PY - 2013
SP - 433
EP - 440
DO - 10.5220/0004217704330440