An open multi-physics framework for modelling wildland-urban interface fire evacuations

Abstract

Fire evacuations at wildland-urban interfaces (WUI) pose a serious challenge to the emergency services, and are a global issue affecting thousands of communities around the world. This paper presents a multi-physics framework for the simulation of evacuation in WUI wildfire incidents, including three main modelling layers: wildfire, pedestrians, and traffic. Currently, these layers have been mostly modelled in isolation and there is no comprehensive model which accounts for their integration. The key features needed for system integration are identified, namely: consistent level of refinement of each layer (i.e. spatial and temporal scales) and their application (e.g. evacuation planning or emergency response), and complete data exchange. Timelines of WUI fire events are analysed using an approach similar to building fire engineering (available vs. required safe egress times for WUI fires, i.e. WASET/WRSET). The proposed framework allows for a paradigm shift from current wildfire risk assessment and mapping tools towards dynamic fire vulnerability mapping. This is the assessment of spatial and temporal vulnerabilities based on the wildfire threat evolution along with variables related to the infrastructure, population and network characteristics. This framework allows for the integration of the three main modelling layers affecting WUI fire evacuation and aims at improving the safety of WUI communities by minimising the consequences of wildfire evacuations.

The Modelling of Pedestrian Vehicle Interaction for Post-Exiting Behaviour

“…Other research has been carried out looking at developing a framework for integrating fire, pedestrian and vehicle models for Wildland-Urban Interfaces [14]. While others have considered the vehicles as stationary obstacles for the evacuation [15].…”

Piloted Ignition of Cylindrical Wildland Fuels Under Irradiation

“…Wildfires refer to the unmanageable and unpredictable fires which are free to spread and expand, thus causing severe damage and loss to the economy, society and environment (Jolly et al, 2015;Leuenberger et al, 2018;Montiel Molina et al, 2019). For example, serious wildfires in 2017 swept across British Columbia, Canada; California, USA; Southern Europe, especially in Portugal and Italy, causing more than 100 fatalities in July (Ronchi et al, 2017). Dead vegetation particles can constitute a bulk of surface fuel loads, so they are closely related to the fire risks and hazards in wildlands (Moghtaderi et al, 1997).…”

Using Artificial Intelligence for Safe and Effective Wildfire Evacuations

“…

Wildfire is a growing global concern for rural and urban areas [1]. 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.

…”

“…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 [4].…”

“…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.…”