Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Lovreglio RSubject: search.filters.subject.Wildfire

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Now showing 1 - 4 of 4
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    Human behaviour in fire: Knowledge foundation and temporal evolution
    (Elsevier B.V., 2024-02-13) Haghani M; Lovreglio R; Button ML; Ronchi E; Kuligowski E
    Understanding human behaviour in fires (HBiF), whether in building or wildland fire contexts, is crucial for saving lives and managing evacuations. However, existing research lacks a comprehensive analysis of HBiF knowledge from both perspectives. To address this issue, we examined nearly 1900 HBiF-related research papers and their references, identifying around 6600 frequently cited references as the HBiF knowledge foundation. We focused on highly prominent items using metrics like citation frequency, burst, and centrality. By analysing co-citation patterns among these references, we unveiled current trends and waning areas of HBiF research. This study identifies knowledge gaps and potential future directions for the field, enabling both mapping of the research concerning our fundamental understanding of behavioural decision-making in fires as well as developing more effective life-saving strategies.
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    Determinants of Gaps in Human Behaviour in Fire Research
    (Springer Nature, 2024-08-08) Ronchi E; Kapalo K; Bode N; Boyce K; Cuesta A; Feng Y; Galea ER; Geoerg P; Gwynne S; Kennedy EB; Kinateder M; Kinsey M; Kuligowski E; Köster G; Lovreglio R; Mossberg A; Ono R; Spearpoint M; Strahan K; Wong SD
    This short communication presents the findings of the work conducted by the human behaviour in fire permanent working group of the International Association for Fire Safety Science. Its aim is to identify determinants of research gaps in the field of human behaviour in fire. Two workshops were conducted in 2023 in which research gaps were identified and discussed by twenty experts. The workshops led experts through a series of questions to determine the reasons (or determinants) for these gaps in human behaviour in building fires and wildfires. Through the questions, the primary identified determinants were (1) researchers’ literacy in the variety of methods adopted in the field, (2) difficulties associated with recruitment of study participants, (3) multi-disciplinary barriers across different research sub-domains, and (4) issues in obtaining funding for addressing fundamental human behaviour in fire research questions. Two key issues emerged from an open discussion during the workshops, namely the difficulties in attracting and training new people in the field (given the limited educational offers around the world on the topic) and the need for more regular opportunities for the community to meet.
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    Social vulnerabilities and wildfire evacuations: A case study of the 2019 Kincade fire
    (Elsevier B.V., 2024-05-31) Sun Y; Forrister A; Kuligowski ED; Lovreglio R; Cova TJ; Zhao X
    Vulnerable populations (e.g., populations with lower income or disabilities) are disproportionately impacted by natural hazards like wildfires. It is crucial to develop equitable and effective evacuation strategies to meet their unique needs. While existing studies offer valuable insights, we need to improve our understanding of how vulnerabilities affect wildfire evacuation decision-making, as well as how this varies spatially. The goal of this study is to conduct an in-depth analysis of the impacts of social vulnerabilities on aggregated evacuation decisions, including evacuation rates, delay in departure time, and evacuation destination distance by leveraging large-scale GPS data generated by mobile devices. Specifically, we inferred evacuation decisions at the level of the census block group, a geographic unit defined by the U.S. Census, utilizing GPS data. We then employed ordinary least squares and geographically weighted regression models to investigate the impacts of social vulnerabilities on evacuation decisions. We also used Moran's I to test if these impacts were consistent across different block groups. The 2019 Kincade Fire in Sonoma County, California, was used as the case study. The impacts of social vulnerabilities on evacuation rates show significant spatial variations across block groups, whereas their effects on the other two decision types do not. Additionally, unemployment, a factor under-explored in previous studies, was identified as contributing to both an increased delay in departure time and a reduction in destination distance of evacuees at the aggregate level. Furthermore, upon comparing the significant factors across different models, we observed that some of the vulnerabilities contributing to evacuation rates for all residents differed from those affecting the delay in departure time and destination distance, which only applied to evacuees. These new insights can guide emergency managers and transportation planners to enhance equitable wildfire evacuation planning and operations.
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    Calibrating the Wildfire Decision Model using hybrid choice modelling
    (Elsevier Ltd, 2020-11) Lovreglio R; Kuligowski E; Walpole E; Link E; Gwynne S
    Wildfire occurrences is creating serious challenges for fire and emergency response services and a diverse range of communities around the world due to the increment of the occurrence of these disasters. As such, understanding the physical and social dynamics characterizing wildfires events is paramount to reduce the risk of these natural disasters. As such, one of the main challenges is to understand how households perceive wildfires and respond to them as part of the evacuation process. In this work, the Wildfire Decision Model originally proposed in Lovreglio et al. [1] is calibrated using a hybrid choice model formulation. The Wildfire Decision Model is a newly developed behavioural choice model for large-scale wildfire evacuations based on the estimation of the risk perceived by households and the impact that this has on the decision-making process. This model is calibrated using a hybrid choice modelling solution and survey data collected after the 2016 Chimney Tops 2 wildfire in Tennessee, USA. The proposed model shows good agreement with the preliminary findings available in the wildfire evacuation literature; namely, the perceived risk is affected by both external factors (i.e., warnings and fire cues) and internal factors (i.e., education, previous wildfire evacuation experience and time of residency in a property).