Wildfire is a growing global concern for rural and urban areas . Statistics show that the intensity and negative consequences of wildfire have increased in recent decades creating serious challenges for fire and emergency services, as well as communities in the wildland-urban interface [2,3,4]. As an example, 85 people lost their lives in California’s Camp fire, which made 2018 the deadliest US wildfire year in a century. To reduce the life safety risk of wildfire and to enhance the safety of communities threatened by wildfire, it is important to understand the physical and social dynamics characterizing wildfires . Such an understanding will help to improve the design of the built environment in communities (e.g. buildings and transportation infrastructure) and enhance emergency planning by incorporating actual household evacuation behaviour. This incorporation of novel knowledge on evacuation behaviour will ultimately facilitate safe and effective evacuation during wildfire emergencies. To address this challenge, several wildfire evacuation models have been proposed in the literature and a comprehensive review of the modelling approaches is provided in Ronchi et al. .
Existing literature on wildfire evacuation modelling can be divided into two categories: conceptual models and engineering models. Conceptual models [4,5,6,7,8,9] provide conceptual frameworks explaining the behavioural components and steps humans go through when assessing, deciding about, and responding to wildfire emergencies. Engineering models [5, 10, 11] include choice models and traffic models. Choice models are designed to investigate the factors affecting human behaviour and model the decision-making process. In a wildfire evacuation, they can be used to estimate how and/or when humans will respond to a wildfire and the time required to evacuate an area threatened by a wildfire, for example. Traffic models, on the other hand, are tools that allow for the simulation of microscopic or macroscopic traffic conditions during the wildfire emergencies. Thus, in the example of macroscopic simulations, traffic models require output from choice models to define trip generations, trip distributions, mode choices, and route assignments as an input of the traffic simulator.