Research Output
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 the escape route. The choice of a route may involve local decisions between alternative exits from an enclosed environment. This work investigates the influence 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 1,503 participants is obtained and a Mixed Logit Model is calibrated using these data. The model shows that 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 points out 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 contribution of this work is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.

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  • Date:

    01 August 2016

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  • Library of Congress:

    TH Building construction

  • Dewey Decimal Classification:

    690 Buildings

  • Funders:

    Pontificia Universidad Catolica de Chilefor; University of Edinburgh; Lunds Universitet


Lovreglio, R., Fonzone, A., & dell’Olio, L. (2016). A mixed logit model for predicting exit choice during building evacuations. Transportation Research Part A: Policy and Practice, 92, 59-75.



Evacuation modelling; exit choice; social influences; behavioural uncertainty; random utility theory; efficient design;

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