Browsing by Author "Cova TJ"

Now showing 1 - 3 of 3
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    A highway vehicle routing dataset during the 2019 Kincade Fire evacuation.
    (Springer Nature Limited, 2022-10-07) Xu Y; Zhao X; Lovreglio R; Kuligowski E; Nilsson D; Cova TJ; Yan X
    As the threat of wildfire increases, it is imperative to enhance the understanding of household evacuation behavior and movements. Mobile GPS data provide a unique opportunity for studying evacuation routing behavior with high ecological validity, but there are little publicly available data. We generated a highway vehicle routing dataset derived from GPS trajectories generated by mobile devices (e.g., smartphones) in Sonoma County, California during the 2019 Kincade Fire that started on October 23, 2019. This dataset contains 21,160 highway vehicle routing records within Sonoma County from October 16, 2019 to November 13, 2019. The quality of the dataset is validated by checking trajectories and average travel speeds. The potential use of this dataset lies in analyzing and modeling evacuee route choice behavior, estimating traffic conditions during the evacuation, and validating wildfire evacuation simulation models.
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    Analyzing Risk Perception, Evacuation Decision and Delay Time: A Case Study of the 2021 Marshall Fire in Colorado
    (Elsevier B.V., 2023-12-11) Forrister A; Kuligowski ED; Sun Y; Yan X; Lovreglio R; Cova TJ; Zhao X
    Climate change is increasing the threat of wildfires to populated areas, especially those within the wildland-urban interface (WUI). The 2021 Marshall fire forced the evacuation of over 30,000 people in Boulder, Jefferson and Adams Counties in Colorado, US. To improve our understanding of wildfire evacuation response, we surveyed individuals affected by the Marshall fire to analyze their evacuation decisions and resulting behavior. We used linear and logistic regression models to determine the factors influencing individuals’ risk perceptions, their decisions to evacuate or stay, and the associated evacuation delay times. We found higher levels of risk perception at the time of the evacuation decision were associated with higher levels of pre-fire perceived risk, having mid-level household income, the receipt of fire cues and having a medical condition. Increased pre-event risk perception increased the likelihood of evacuating, along with gender (female-identified), being aged between 55 and 64 years, and having a higher household income. On the other hand, having a prior awareness of wildfires had a negative effect on evacuation likelihood. Additionally, having previous experience with fire damage, owning their home, having a larger household size and being alerted later in the fire event reduced the delay time; whereas engaging in preparation activities and having children in the home led to longer delay times. These research findings can be used by emergency managers to better prepare WUI communities for future wildfire events.
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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.