Computer based models describing pedestrian behavior in an emergency evacuation play a vital role in the development of active strategies that minimize the evacuation time when a closed area, with a relatively small number of fixed exits, must be evacuated for a large number of people.
This Research Line proposes a hybrid structure where the dynamics of fire and smoke propagation are modeled by means of Cellular Automata and for simulating people behavior we are using Intelligent Agents. Each agent will possess certain characteristics psychological, physiological and social and based on information that is capable of receiving from its sensors, it may perceive that is happening around, and then take a decision that will reflect its ability to cope with the emergency evacuation, called in this project, behavior.
The simulation model consists of two sub-models, called pedestrian and environmental. As part of the pedestrian model, we have prototyped a methodology that is able to model some of the frequently observed human behaviors in evacuation exercises. The model is a process that consumes a significant amount of time to simulate a complete evacuation, maximum when the environment size and/or the number of people is considerable.
The emergence of multicore processors introduces a real challenge for parallel applications are to exploit such architecture at their potential. The proposal of this research aims to carrying out a parallel shared memory model, performing task-level parallelism (environmental & pedestrian sub models) and where multiples threads assigned of the same task assist in the resolution of disjoint areas of the grid in a data parallelism way. This leads us to develop a model to achieve a competitive performance.
- Simulating Behaviors To Face Up An Emergency Evacuation
- Evacuation Simulation Supporting High Level Behaviour-Based Agents
- A Hybrid Simulation Model to Test Behaviour Simulating Behaviours to face up an Emergency Evacuation.
- Implementing Sub Steps in a Parallel Automata Cellular Model. Also in Computer Science & Technology Series. Best paper in XVII Argentine Congress of Computer Science. ISBN: 978-950-34-0757-8. 2012.
- Multi-column Partitioning for Agent-based CA Model.
If you are interested in our EVAC Simulator Package (this software is based on Java under GNU Affero GPLv3) do not hesitate to contact with us.
- P. C. Tissera, A. M. Printista, M. Errecalde, Evacuation simulations using cellular automata , Journal of Computer Science & Technology 7 (1) (2007) pp. 14–20.
- V. J. Blue, J. L. Adler, Emergent fundamental pedestrian flows from cellular automata microsimulation , Transportation Research Record 1644 (1998) 29–36.
- H. Klupfel, T. Meyer-Konig, J. Wahle, M. Schreckenberg, Microscopic simulation of evacuation processes on passenger ships , International Conference on Cellular Automata for Research and Industry, Springer-Verlag, (2000), pp. 63–71.
- N. K. Pan, C. S. Han, K. Dauber, K. H. Law, A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations , AI and Society. The Journal of Human-Centred Systems 22 (2) (2007) pp. 113–132.
- S. Sarmady, F. Haron, A. Z. H. Talib, Multi-agent simulation of circular pedestrian movements using cellular automata , International Conference on Modelling & Simulation (AMS), IEEE Computer Society, (2008), pp. 654–659.
- D. Helbing, A. Johansson, Pedestrian, crowd and evacuation dynamics , Encyclopedia of Complexity and Systems Science, Springer, (2009), pp. 6476–6495.
- R. A. Brooks, J. H. Connell, Asynchronous distributed control system for a mobile robot , Storage and Retrieval for Image and Video Databases, (1986).
- R. A. Brooks, A robust layered control system for a mobile robot , IEEE Journal of Robotics and Automation (1986) pp. 14–23.
- P. Maes, The dynamics of action selection , IJCAI-89, MI, (2009), pp. 991–997.
- S. Russell, P. Norvig, J. Canny, I. Bratko, Artificial Intelligence: A Modern Approach , Pearson Education, Limited, (2005).
- M. J. Mataric, Behavior-based control: Examples from navigation, learning, and group behavior , Journal of Experimental and Theoretical Artificial Intelligence 9 (1997) pp. 323–336.
- M. N. Nicolescu, M. J. Mataric, A hierarchical architecture for behavior-based robots , international Joint Conference on Autonomous Agents and Multiagent Systems (I), AAMAS02, ACM, (2002), pp. 227–233.
- P. Pirjanian, Behavior coordination mechanisms state-of-the-art , Tech. rep., USC Robotics Research Laboratory, University of Southern California (1999).