Studying the Impact of Trained Staff on Evacuation Scenarios by Agent-Based Simulation

Human evacuation experiments can trigger distress, be unethical and present high costs. As a solution, computer simulations can predict the effectiveness of new emergency management procedures. This paper applies multi-agent simulation to measure the influence of staff members with diverse training levels on evacuation time. A previously developed and validated model was extended with explicit mechanisms to simulate staff members helping people to egress. The majority of parameter settings have been based on empirical data acquired in earlier studies. Therefore, simulation results are expected to be realistic. Results show that staff are more effective in complex environments, especially when trained. Not only specialised security professionals but, especially, regular workers of shopping facilities and offices play a significant role in evacuation processes when adequately trained. These results can inform policy makers and crowd managers on new emergency management procedures.

[1]  N. Badler,et al.  Crowd simulation incorporating agent psychological models, roles and communication , 2005 .

[2]  Tibor Bosse,et al.  An Intelligent System for Aggression De-Escalation Training , 2016, ECAI.

[3]  Karen Boyce,et al.  A study of evacuation from large retail stores , 2000 .

[4]  Karen Boyce,et al.  An Investigation Into Staff Behaviour In Unannounced Evacuations Of Retail Stores - Implications For Training And Fire Safety Engineering , 2005 .

[5]  Tibor Bosse,et al.  Simulating Crowd Evacuation with Socio-Cultural, Cognitive, and Emotional Elements , 2017, Trans. Comput. Collect. Intell..

[6]  C. Natalie van der Wal,et al.  An Adaptive Simulation Tool for Evacuation Scenarios , 2017, EPIA.

[7]  Tibor Bosse,et al.  An Agent-Based Evacuation Model with Social Contagion Mechanisms and Cultural Factors , 2017, IEA/AIE.

[8]  Michel C. A. Klein,et al.  Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions , 2013, Autonomous Agents and Multi-Agent Systems.

[9]  Robert A. Meyers Extreme Environmental Events , 2011 .

[10]  Craig A. Smith,et al.  Patterns of cognitive appraisal in emotion. , 1985, Journal of personality and social psychology.

[11]  James E. Bartlett,et al.  Organizational Research: Determining Organizational Research: Determining Appropriate Sample Size in Survey Research Appropriate Sample Size in Survey Research , 2001 .

[12]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[13]  Benigno E. Aguirre,et al.  A Critical Review Of Emergency Evacuation Simulation Models , 2004 .

[14]  D. L. Boswell,et al.  Pre-Warning Staff Delay: A Forgotten Component in ASET/RSET Calculations , 2011 .

[15]  Guylène Proulx How to initiate evacuation movement in public buildings , 1999 .

[16]  C. Natalie van der Wal,et al.  Simulating Collective Evacuations with Social Elements , 2017, ICCCI.

[17]  Tibor Bosse,et al.  Adaptive Training for Aggression de-Escalation , 2014, ALIA.

[18]  Tibor Bosse,et al.  Evaluation of a virtual training environment for aggression de-escalation , 2015, CGAMES 2015.

[19]  Jan Treur Network-Oriented Modeling , 2016 .

[20]  Dirk Helbing,et al.  Pedestrian, Crowd and Evacuation Dynamics , 2013, Encyclopedia of Complexity and Systems Science.

[21]  G Keith Still,et al.  Introduction to Crowd Science , 2014 .