Scenario Based Modeling for Very Large Scale Simulations

In order to develop complexity science based modeling, prediction and simulation methods for large scale socio-technical systems in an Ambient Intelligence (AmI) based smart environment, we propose a scenario based modeling approach. With a case study on AmI technology to support the evacuation from emergency scenarios, i.e. the Life Belt, a wearable computing systems for vibro-tactile directional guidance, we introduce the concept of model scaling from a micro to a macro level. Aligned with the scenario, we present how crowd simulation strategies encoded into a small scale simulation setup can be extended to a mixed-level simulation based on combining model aspects also coming from the large scale model. The experimental results of a real evacuation trail at a local railway station are incorporated to compare the evacuation efficiency for three strategies: (i) Potential Map, (ii) Evacuees familiarity of the exits and (iii) Exits usage optimization. A comparison with the earlier results from small scale simulation suggest that a real large scale simulation results may not be similar to that of small scale simulation due to dynamics of crowd built up and complexity of building structure.

[1]  Alois Ferscha,et al.  LifeBelt: Silent Directional Guidance for Crowd Evacuation , 2009, 2009 International Symposium on Wearable Computers.

[2]  Jonathan D. Sime,et al.  Affiliative behaviour during escape to building exits , 1983 .

[3]  Gerhard Tröster,et al.  Decentralized Detection of Group Formations from Wearable Acceleration Sensors , 2009, 2009 International Conference on Computational Science and Engineering.

[4]  Alois Ferscha,et al.  On the Efficiency of LifeBelt Based Crowd Evacuation , 2009, 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications.

[5]  Lizhong Yang,et al.  Occupants’ behavior of going with the crowd based on cellular automata occupant evacuation model , 2008 .

[6]  Benigno E Aguirre,et al.  Emergency Evacuations, Panic, and Social Psychology , 2005, Psychiatry.

[7]  C. Neumann,et al.  Evacuation Simulation at Linz Central Station — Usefulness during design, approval and start-up , 2007 .

[8]  A. Mawson.  Understanding Mass Panic and Other Collective Responses to Threat and Disaster , 2005, Psychiatry.

[9]  Paul Lukowicz,et al.  Ad-Hoc Information Spread between Mobile Devices: A Case Study in Analytical Modeling of Controlled Self-organization in IT Systems , 2010, ARCS.

[10]  Jian Li,et al.  Simulation of the kin behavior in building occupant evacuation based on Cellular Automaton , 2005 .

[11]  Alois Ferscha,et al.  A Simulation Study of Exit Choice Based on Effective Throughput of an Exit Area in a Multi-exit Evacuation Situation , 2009, 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications.