A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.

[1]  Harri Ehtamo,et al.  Modeling Evacuees' Exit Selection with Best Response Dynamics , 2010 .

[2]  Ana L. C. Bazzan,et al.  An agent-based simulation of pedestrian dynamics: from lane formation to auditorium evacuation , 2006, AAMAS '06.

[3]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[4]  Enrico Ronchi,et al.  The evaluation of different evacuation models for assessing road tunnel safety analysis , 2012 .

[5]  Harri Ehtamo,et al.  Pedestrian behavior and exit selection in evacuation of a corridor – An experimental study , 2012 .

[6]  Hai-Jun Huang,et al.  Static floor field and exit choice for pedestrian evacuation in rooms with internal obstacles and multiple exits. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Christos Giachritsis,et al.  The GETAWAY Project - Improving Passenger Evacuation Techniques in Railway Stations (and Other Transport Hubs) , 2014 .

[8]  Edwin R. Galea,et al.  Modelling occupant interaction with fire conditions using the buildingEXODUS evacuation model , 2001 .

[9]  Joakim Olander,et al.  Comparative Study of Dissuasive Emergency Signage , 2015 .

[10]  Ennio Cascetta,et al.  Transportation Systems Analysis: Models and Applications , 2009 .

[11]  J. Bailenson,et al.  The Use of Immersive Virtual Reality in the Learning Sciences: Digital Transformations of Teachers, Students, and Social Context , 2008 .

[12]  Alessandro Acquisti,et al.  Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook , 2006, Privacy Enhancing Technologies.

[13]  W. Greene,et al.  Modeling Ordered Choices: A Primer , 2010 .

[14]  Stefan Holl,et al.  Modeling the Dynamic Route Choice of Pedestrians to Assess the Criticality of Building Evacuation , 2011, Adv. Complex Syst..

[15]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[16]  M. Thelwall.  Social networks, gender, and friending: An analysis of MySpace member profiles , 2008 .

[17]  Guo Ren-Yong,et al.  Logit-based exit choice model of evacuation in rooms with internal obstacles and multiple exits , 2010 .

[18]  Enrico Ronchi,et al.  A Virtual Reality Experiment on Flashing Lights at Emergency Exit Portals for Road Tunnel Evacuation , 2015, Fire Technology.

[19]  Enrico Ronchi,et al.  Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization , 2015 .

[20]  Jun Yang,et al.  Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study , 2014, Reliab. Eng. Syst. Saf..

[21]  Ruggiero Lovreglio,et al.  A study of herding behaviour in exit choice during emergencies based on random utility theory. , 2016 .

[22]  Ruggiero Lovreglio,et al.  A discrete choice model based on random utilities for exit choice in emergency evacuations , 2014 .

[23]  Kemloh Wagoum,et al.  Route Choice Modelling and Runtime Optimisation for Simulation of Building Evacuation , 2018 .

[24]  Volker Schneider,et al.  Egress Route Choice Modelling—Concepts and Applications , 2010 .

[25]  Michael Kinsey,et al.  Guidance for the Model Developer on Representing Human Behavior in Egress Models , 2015, Fire Technology.

[26]  Erica D. Kuligowski,et al.  Review of Building Evacuation Models , 2005 .

[27]  Omid Ejtemai,et al.  Random Utility Models of Pedestrian Crowd Exit Selection based on SP-off-RP Experiments , 2014 .

[28]  Enrico Ronchi,et al.  A model of the decision-making process during pre-evacuation , 2015 .

[29]  Guylène Proulx,et al.  Panic or not in fire: Clarifying the misconception , 2012 .

[30]  Enrico Ronchi,et al.  Modelling the Impact of Emergency Exit Signs in Tunnels , 2012 .

[31]  Edwin R. Galea,et al.  High-Rise Building Evacuation Post 911 - Addressing the Issues , 2014 .

[32]  Xiaoping Zheng,et al.  Modeling crowd evacuation of a building based on seven methodological approaches , 2009 .

[33]  John M. Rose,et al.  Applied Choice Analysis: A Primer , 2005 .

[34]  Harri Ehtamo,et al.  Game Theoretic Best-Response Dynamics for Evacuees' Exit Selection , 2010, Adv. Complex Syst..

[35]  J. S. Cramer,et al.  Logit Models from Economics and Other Fields , 2003 .

[36]  K. Train,et al.  Estimation on stated-preference experiments constructed from revealed-preference choices , 2008 .

[37]  Guylène Proulx,et al.  A stress model for people facing a fire , 1993 .

[38]  Ruggiero Lovreglio,et al.  The Role of Herding Behaviour in Exit Choice During Evacuation , 2014 .

[39]  Enrico Ronchi,et al.  Virtual reality for fire evacuation research , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[40]  M. Wedel,et al.  Designing Conjoint Choice Experiments Using Managers' Prior Beliefs , 2001 .

[41]  David V. Canter,et al.  The decision to evacuate: a study of the motivations which contribute to evacuation in the event of fire , 1985 .

[42]  Daniel Nilsson,et al.  Fire Evacuation in Underground Transportation Systems: A Review of Accidents and Empirical Research , 2013 .

[43]  Enrico Ronchi,et al.  The need of latent variables for modelling decision-making in evacuation simulations , 2015 .

[44]  M. Bierlaire,et al.  ESTIMATION OF VALUE OF TRAVEL-TIME SAVINGS USING MIXED LOGIT MODELS , 2005 .

[45]  Edwin R. Galea,et al.  Adaptive Decision-making In Building Exodus In Response To Exit Congestion , 2000 .

[46]  R. Cialdini.  Influence: The Psychology of Persuasion , 1993 .

[47]  E. Hall,et al.  The Hidden Dimension , 1970 .

[48]  J. Sime.  Movement toward the Familiar , 1985 .

[49]  Enrico Ronchi,et al.  The validation of evacuation simulation models through the analysis of behavioural uncertainty , 2014, Reliab. Eng. Syst. Saf..

[50]  Daniel Nilsson,et al.  Influencing Exit Choice in the Event of a Fire Evacuation , 2008 .

[51]  Edwin R. Galea,et al.  A review of the methodologies used in evacuation modelling , 1999 .

[52]  Enrico Ronchi,et al.  Basic Concepts and Modelling Methods , 2016 .

[53]  Enrico Ronchi,et al.  A mixed-ordered Logit approach to investigate correlations among different affordances in fire evacuation , 2015 .

[54]  John M. Rose,et al.  Designing efficient stated choice experiments in the presence of reference alternatives , 2008 .

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

[56]  Xia Zhang,et al.  A probabilistic occupant evacuation model for fire emergencies using Monte Carlo methods , 2013 .

[57]  Edwin R. Galea,et al.  Wayfinding Behavior within Buildings - An International Survey , 2011 .

[58]  Christina L. Bloebaum,et al.  Incorporation of decision, game, and Bayesian game theory in an emergency evacuation exit decision model , 2014 .

[59]  Enrico Ronchi,et al.  A Method for the Analysis of Behavioural Uncertainty in Evacuation Modelling , 2014 .

[60]  Enrico Ronchi,et al.  Movement speed and exit choice in smoke-filled rail tunnels , 2013 .

[61]  Enrico Ronchi,et al.  Evacuation modelling in road tunnel fires , 2012 .

[62]  Daniel Nilsson,et al.  Evacuation experiment in a road tunnel: A study of human behaviour and technical installations , 2009 .

[63]  Daniel Alvear,et al.  Evacuation Modeling Trends , 2016 .

[64]  Hani S. Mahmassani,et al.  Exit Choice Decisions during Pedestrian Evacuations of Buildings , 2012 .

[65]  J. Loomis,et al.  Interpersonal Distance in Immersive Virtual Environments , 2003, Personality & social psychology bulletin.

[66]  Edwin R. Galea,et al.  Active dynamic signage system: A full-scale evacuation trial , 2015 .

[67]  Daniel Nilsson,et al.  Exit choice in fire emergencies - Influencing choice of exit with flashing lights , 2009 .

[68]  Siuming Lo,et al.  A game theory based exit selection model for evacuation , 2006 .

[69]  Jim Blascovich,et al.  A theoretical model of social influence for increasing the utility of collaborative virtual environments , 2002, CVE '02.

[70]  Jen-Jia Lin,et al.  Valuing user external benefits and developing management strategies for metro system underground arcades , 2008 .

[71]  Daniel Nilsson,et al.  Social influence during the initial phase of a fire evacuation—Analysis of evacuation experiments in a cinema theatre , 2009 .