Ant Colony Optimisation for Planning Safe Escape Routes

  • Morten Goodwin
  • Ole-Christoffer Granmo
  • Jaziar Radianti
  • Parvaneh Sarshar
  • Sondre Glimsdal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7906)


An emergency requiring evacuation is a chaotic event filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when a predefined escape route is blocked by a hazard, and there is a need to re-think which escape route is safest.

This paper addresses automatically finding the safest escape route in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected person — considering both dynamic spread of hazards and congestion avoidance.

The solution can be used both on an individual bases, such as from a personal smart phone of one of the evacuees, or from a remote location by emergency personnel trying to assist large groups.


Wireless Sensor Network Smart Phone Crisis Situation Escape Route Congestion Avoidance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Li, Q., Rus, D.: Navigation protocols in sensor networks. ACM Transactions on Sensor Networks (TOSN) 1(1), 3–35 (2005)CrossRefGoogle Scholar
  2. 2.
    Li, Q., De Rosa, M., Rus, D.: Distributed algorithms for guiding navigation across a sensor network. In: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, pp. 313–325. ACM (2003)Google Scholar
  3. 3.
    Radiante, J., Granmo, O.-C., Bouhmala, N., Sarshar, P., Yazidi, A., Gonzalez, J.: Crowd models for emergency evacuation: A review targeting human-centered sensing. In: Proceeding of 46th Hawaii International Conference for System Sciences (2013)Google Scholar
  4. 4.
    Thompson, P.A., Marchant, E.W.: A computer model for the evacuation of large building populations. Fire Safety Journal 24(2), 131–148 (1995)CrossRefGoogle Scholar
  5. 5.
    Thompson, P.A., Marchant, E.W.: Testing and application of the computer model simulex. Fire Safety Journal 24(2), 149–166 (1995)CrossRefGoogle Scholar
  6. 6.
    Hamacher, H.W., Tjandra, S.A.: Mathematical modelling of evacuation problems–a state of the art. Pedestrian and Evacuation Dynamics 2002, 227–266 (2002)Google Scholar
  7. 7.
    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)MathSciNetCrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Wang, J., Lo, S., Sun, J., Wang, Q., Mu, H.: Qualitative simulation of the panic spread in large-scale evacuation. Simulation (2012)Google Scholar
  11. 11.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar
  12. 12.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)Google Scholar
  13. 13.
    Gutjahr, W.: A graph-based ant system and its convergence. Future Generation Computer Systems 16(8), 873–888 (2000)CrossRefGoogle Scholar
  14. 14.
    Liu, B., Abbas, H., McKay, B.: Classification rule discovery with ant colony optimization. In: IEEE/WIC International Conference on Intelligent Agent Technology, IAT 2003, pp. 83–88. IEEE (2003)Google Scholar
  15. 15.
    Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Handbook of Metaheuristics, pp. 227–263 (2010)Google Scholar
  16. 16.
    Ducatelle, F., Di Caro, G.A., Gambardella, L.M.: An analysis of the different components of the antHocNet routing algorithm. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 37–48. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Murtala Zungeru, A., Ang, L., Phooi Seng, K.: Performance evaluation of ant-based routing protocols for wireless sensor networks (2012)Google Scholar
  18. 18.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Di Caro, G., Dorigo, M.: Antnet: Distributed stigmergetic control for communications networks. arXiv preprint arXiv:1105.5449 (2011)Google Scholar
  20. 20.
    Di Caro, G., Ducatelle, F., Gambardella, L.M.: Anthocnet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications 16(5), 443–455 (2005)CrossRefGoogle Scholar
  21. 21.
    Dressler, F., Koch, R., Gerla, M.: Path heuristics using ACO for inter-domain routing in mobile ad hoc and sensor networks. In: Suzuki, J., Nakano, T. (eds.) BIONETICS 2010. LNICST, vol. 87, pp. 128–142. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Selvan, G., Pothumani, S., Manivannan, R., Senthilnayaki, R., Balasubramanian, K.: Weakness recognition in network using Aco and mobile agents. In: 2012 International Conference on Advances in Engineering, Science and Management (ICAESM), pp. 459–462. IEEE (2012)Google Scholar
  23. 23.
    Zhan, A., Wu, F., Chen, G.: Sos: A safe, ordered, and speedy emergency navigation algorithm in wireless sensor networks. In: 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), pp. 1–6. IEEE (2011)Google Scholar
  24. 24.
    Li, S., Zhan, A., Wu, X., Yang, P., Chen, G.: Efficient emergency rescue navigation with wireless sensor networks. Journal of Information Science and Engineering 27, 51–64 (2011)Google Scholar
  25. 25.
    Hsiao, Y., Chuang, C., Chien, C.: Ant colony optimization for best path planning. In: IEEE International Symposium on Communications and Information Technology, ISCIT 2004, vol. 1, pp. 109–113. IEEE (2004)Google Scholar
  26. 26.
    Sim, K., Sun, W.: Ant colony optimization for routing and load-balancing: survey and new directions. Systems, Man and Cybernetics, Part A: Systems and Humans 33(5), 560–572 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Morten Goodwin
    • 1
  • Ole-Christoffer Granmo
    • 1
  • Jaziar Radianti
    • 1
  • Parvaneh Sarshar
    • 1
  • Sondre Glimsdal
    • 1
  1. 1.Department of ICTUniversity of AgderGrimstadNorway

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