Modelling self-evacuation archetypes to improve wildfire evacuation traffic simulations: A regional case study

Abstract

: Wildfires are a serious threat in many regions of the world, including Australia. The risk of these fires is expected to continue to increase due to climate change, putting more people and communities in harm’s way. One approach to reducing the risk to lives in such fires is to plan and prepare for community evacuations. Researchers have been exploring the use of self-evacuation archetypes, clustering self-reported individual behaviours in past fires, to gain insights into who evacuates, why they do so, and when. Self-evacuation archetypes encompass a range of factors, including demographic characteristics, risk perception, social networks, and prior experience. By understanding these factors, researchers can create more realistic models of decision-making during a wildfire event. In Australia, evacuations are not mandatory, and while the understanding of the decision to leave or shelter in place has advanced, much less is understood about how these decisions play out as traffic on the transport network. For instance, intermediate trips, which are trips to destinations other than the evacuation place, can constitute a significant proportion of trips following an evacuation recommendation, and can lead to different outcomes compared to those of a coordinated evacuation. Therefore, modelling the diversity of decisions and their contribution to traffic is vital to understanding local evacuation concerns and planning safe community evacuations. In this work, we present an agent-based decision-making model and scenario for the town of Castlemaine, located in the state of Victoria, Australia. Our model is based on self-evacuation archetypes, applied to a synthetic population representing the demographics of residents of the region. The model provides a framework for understanding how different individuals are likely to respond during a wildfire event, and allows exploration of the potential impact of different interventions. We believe that our approach provides a more realistic and nuanced picture of traffic during a wildfire event and can help emergency services plan more effective response strategies.