Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking.

Wildfires are increasing in scale, frequency and longevity, and are affecting new locations as environmental conditions change. This paper presents a dataset collected during a community evacuation drill performed in Roxborough Park, Colorado (USA) in 2019. This is a wildland-urban interface community including approximately 900 homes. Data concerning several aspects of community response were collected through observations and surveys: initial population location, pre-evacuation times, route use, and arrival times at the evacuation assembly point. Data were used as inputs to benchmark two evacuation models that adopt different modelling approaches. The WUI-NITY platform and the Evacuation Management System model were applied across a range of scenarios where assumptions regarding pre-evacuation delays and the routes used were varied according to original data collection methods (and interpretation of the data generated). Results are mostly driven by the assumptions adopted for pre-evacuation time inputs. This is expected in communities with a low number of vehicles present on the road and relatively limited traffic congestion. The analysis enabled the sensitivity of the modelling approaches to different datasets to be explored, given the different modelling approaches adopted. The performance of the models were sensitive to the data employed (derived from either observations or self-reporting) and the evacuation phases addressed in them. This indicates the importance of monitoring the impact of including data in a model rather than simply on the data itself, as data affects models in different ways given the modelling methods employed. The dataset is released in open access and is deemed to be useful for future wildfire evacuation modelling calibration and validation efforts. Supplementary Information: The online version contains supplementary material available at 10.1007/s10694-023-01371-1.

Guidance for the Model User on Representing Human Behavior in Egress Models.

Structures are currently designed and constructed in accordance with prescriptive and performance-based (PBD) methodologies to ensure a certain level of occupant safety during fire emergencies. The performance-based approach requires the quantification of both ASET (Available Safe Egress Time) and RSET (Required Safe Egress Time) to determine the degree of safety provided. This article focuses on the RSET side of the equation, for which a fire protection or fire safety engineer would use some type of egress modelling approach to estimate evacuation performance. Often, simple engineering equations are applied to estimate the RSET value. Over time, more sophisticated computational tools have appeared-that go beyond basic flow calculations; e.g. simulating individual agent movement. Irrespective of the approach adopted, appropriate and accurate representation of human behavior in response to fire within these approaches is limited, mainly due to the lack of a comprehensive conceptual model of evacuee decision-making and behavior during fire emergencies. This article initially presents the set of behavioral statements, or mini-theories, currently available from various fire and disaster studies, organized using the overarching theory of decision-making and human behavior in disasters. Once presented, guidance is provided on how these behavioral statements might be incorporated into an evacuation model, in order to better represent human behavior in fire within the safety analysis being performed. The intent here is to improve the accuracy of the results produced by performance-based calculations and analyses.

Risk perception in fire evacuation behavior revisited: definitions, related concepts, and empirical evidence.

Risk perception (RP) is studied in many research disciplines (e.g., safety engineering, psychology, and sociology). Definitions of RP can be broadly divided into expectancy-value and risk-as-feeling approaches. In the present review, RP is seen as the personalization of the risk related to a current event, such as an ongoing fire emergency; it is influenced by emotions and prone to cognitive biases. We differentiate RP from other related concepts (e.g., situation awareness) and introduce theoretical frameworks relevant to RP in fire evacuation (e.g., Protective Action Decision Model and Heuristic-Systematic approaches). Furthermore, we review studies on RP during evacuation with a focus on the World Trade Center evacuation on September 11, 2001 and present factors modulating RP as well as the relation between perceived risk and protective actions. We summarize the factors that influence perception risk and discuss the direction of these relationships (i.e., positive or negative influence, or inconsequential) and conclude with presenting limitations of this review and an outlook on future research.