Modelling and interpreting pre-evacuation decision-making using machine learning - Zhao X; Lovreglio R; Nilsson D (Elsevier BV, 2020)
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.
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Keywords: Random forest; Machine learning; Interpretation; Nonlinearity; Human behaviour in fire; Pre-evacuation; 09 Engineering; 12 Built Environment and Design