Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

Browse

Subject: search.filters.subject.Human behaviour in fire

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A Virtual Reality Exit Choice Experiment to Assess the Impact of Social Influence and Fire Wardens in a Metro Station Evacuation
    (Springer Nature, 2025-05-19) Lu S; Rodriguez M; Feng Z; Paes D; Daemei AB; Vancetti R; Mander S; Mandal T; Rao KR; Lovreglio R
    Assessing evacuation time is a fundamental task in fire engineering. One of the key decisions made in evacuation dynamics is exit choice. In this work, we propose a new immersive virtual reality (VR) experiment to assess the effects of social influence and fire wardens’ instructions on the exit chosen. We also investigate if and how the perceived level of authority of the fire wardens (i.e., metro staff members or firefighters) can affect these decisions. The proposed immersive VR experiment includes 12 different scenarios during a fire evacuation in an underground metro station. A sample of 131 participants took part in the experiment, making 1048 choices. We estimate a discrete choice model to quantify if and how these factors affect the participants’ decisions. The results show that both instructions by fire wardens and social influence significantly affect exit choice and that the impact of fire wardens can change depending on their perceived level of authority.
  • Thumbnail Image
    Item
    Determinants of Gaps in Human Behaviour in Fire Research
    (Springer Nature, 2024-08-08) Ronchi E; Kapalo K; Bode N; Boyce K; Cuesta A; Feng Y; Galea ER; Geoerg P; Gwynne S; Kennedy EB; Kinateder M; Kinsey M; Kuligowski E; Köster G; Lovreglio R; Mossberg A; Ono R; Spearpoint M; Strahan K; Wong SD
    This short communication presents the findings of the work conducted by the human behaviour in fire permanent working group of the International Association for Fire Safety Science. Its aim is to identify determinants of research gaps in the field of human behaviour in fire. Two workshops were conducted in 2023 in which research gaps were identified and discussed by twenty experts. The workshops led experts through a series of questions to determine the reasons (or determinants) for these gaps in human behaviour in building fires and wildfires. Through the questions, the primary identified determinants were (1) researchers’ literacy in the variety of methods adopted in the field, (2) difficulties associated with recruitment of study participants, (3) multi-disciplinary barriers across different research sub-domains, and (4) issues in obtaining funding for addressing fundamental human behaviour in fire research questions. Two key issues emerged from an open discussion during the workshops, namely the difficulties in attracting and training new people in the field (given the limited educational offers around the world on the topic) and the need for more regular opportunities for the community to meet.
  • Thumbnail Image
    Item
    Modelling and interpreting pre-evacuation decision-making using machine learning
    (Elsevier BV, 2020-05) Zhao X; Lovreglio R; Nilsson D
    The behaviour of building occupants in the first stage of an evacuation can dramatically impact the time required to evacuate buildings. This behaviour has been widely investigated by scholars with a macroscopic approach fitting random distributions to represent the pre-evacuation time, i.e. time from noticing the first cue until deliberate movement. However, microscopic investigations on how building occupants respond to several social and environmental factors are still rare in the literature. This paper aims to leverage machine learning as a possible solution to investigate factors affecting building occupants' decision-making during pre-evacuation stage. In particular, we focus on applying interpretable machine learning to reveal the interactions among the input variables and to capture nonlinear relationships between the input variables and the outcome. As such, we use a well-established machine-learning algorithm—random forest—to model and predict people's emergency behaviour pre-evacuation. We then apply tools to interpret the black-box random forest model to extract useful knowledge and gain insights for emergency planning. Specifically, this algorithm is applied here to investigate the behaviour of 569 building occupants split between five unannounced evacuation drills in a cinema theatre. The results indicate that both social and environmental factors affect the probability of responding. Several independent variables, such as the time elapsed after the alarm has started and the decision-maker's group size, are presenting strong nonlinear relationships with the probability of switching to the response stage. Furthermore, we find interactions exist between the row number where the decision-maker sits and the number of responding occupants visible to her; the complex relationship between the outcome and these two variables can be visualized by using a two-dimensional partial dependence plot. An interesting finding is that a decision-maker is more sensitive to the proportion of responding occupants than the number of them; hence, the people sitting in the back are often responding more slowly than the people in the front.