Browsing by Author "Nilsson D"

Now showing 1 - 5 of 5
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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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    Enhancing egress drills: Preparation and assessment of evacuee performance
    (1/10/2019) Gwynne SMV; Kuligowski ED; Boyce KE; Nilsson D; Robbins AP; Lovreglio R; Thomas JR; Roy-Poirier A
    This article explores how egress drills—specifically those related to fire incidents—are currently used, their impact on safety levels, and the insights gained from them. It is suggested that neither the merits of egress drills are well understood, nor the impact on egress performance well characterized. In addition, the manner in which they are conducted varies both between and within regulatory jurisdictions. By investigating their strengths and limitations, this article suggests opportunities for their enhancement possibly through the use of other egress models to support and expand upon the benefits provided. It is by no means suggested that drills are not important to evacuation safety—only that their inconsistent use and the interpretation of the results produced may mean we (as researchers, practitioners, regulators, and stakeholders) are not getting the maximum benefit out of this important tool. © 2017 Her Majesty the Queen in Right of Canada. Fire and Materials StartCopText© 2017 John Wiley & Sons, Ltd.
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    Investigating Evacuation Behaviour in Retirement Facilities: Case Studies from New Zealand
    (1/05/2021) Rahouti A; Lovreglio R; Nilsson D; Kuligowski E; Jackson P; Rothas F
    Ageing populations are generating new challenges for the safe design of buildings and infrastructure systems in communities around the world. Elderly building occupants are more likely to have mobility impairments, and in turn, require longer times and increased assistance to evacuate buildings compared with able-bodied adults. To date, only a few studies have been carried out to assess the evacuation performance of elderly evacuees in retirement homes. Therefore, it is necessary to collect critical evacuation data, such as pre-evacuation times and evacuation speeds, for these occupancy types. This work investigates the evacuation behaviour of elderly evacuees and caretaking staff using video recordings of evacuation in retirement facilities. The paper presents three case studies. The first case study includes unannounced drills, which took place in communal areas of retirement homes during a live music exhibition and in a kitchen. The second case study is a series of unannounced drills, which took place in independent living apartment buildings of a retirement facility. The last case study is of a single announced evacuation drill, which took place in a communal area of a retirement building. Qualitative results indicate that the occupants’ behaviours depended on their role (i.e. resident or staff) and on the type of monitored area (i.e. apartment building or communal area). Pre-evacuation times measured in this study are in accordance with values stated in the literature, and walking speeds fall in the range of values reported in past studies of these types of building. Finally, results revealed that there is a significant gap between the data provided in this work and the SFPE design curves used for buildings, since the SFPE design curves do not explicitly account for adults with mobility impairments.
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    Modelling and interpreting pre-evacuation decision-making using machine learning
    (Elsevier BV, 2020-05) Zhao X; Lovreglio R; Nilsson D
    The behaviour of building occupants in the first stage of an evacuation can dramatically impact the time required to evacuate buildings. This behaviour has been widely investigated by scholars with a macroscopic approach fitting random distributions to represent the pre-evacuation time, i.e. time from noticing the first cue until deliberate movement. However, microscopic investigations on how building occupants respond to several social and environmental factors are still rare in the literature. This paper aims to leverage machine learning as a possible solution to investigate factors affecting building occupants' decision-making during pre-evacuation stage. In particular, we focus on applying interpretable machine learning to reveal the interactions among the input variables and to capture nonlinear relationships between the input variables and the outcome. As such, we use a well-established machine-learning algorithm—random forest—to model and predict people's emergency behaviour pre-evacuation. We then apply tools to interpret the black-box random forest model to extract useful knowledge and gain insights for emergency planning. Specifically, this algorithm is applied here to investigate the behaviour of 569 building occupants split between five unannounced evacuation drills in a cinema theatre. The results indicate that both social and environmental factors affect the probability of responding. Several independent variables, such as the time elapsed after the alarm has started and the decision-maker's group size, are presenting strong nonlinear relationships with the probability of switching to the response stage. Furthermore, we find interactions exist between the row number where the decision-maker sits and the number of responding occupants visible to her; the complex relationship between the outcome and these two variables can be visualized by using a two-dimensional partial dependence plot. An interesting finding is that a decision-maker is more sensitive to the proportion of responding occupants than the number of them; hence, the people sitting in the back are often responding more slowly than the people in the front.
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    Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires
    (Elsevier B.V., 2024-09-10) Zhang X; Zhao X; Xu Y; Nilsson D; Lovreglio R
    Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. To tackle this research gap, the study develops a new methodological framework for modeling highly granular spatiotemporal trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested using a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are weekend indicator, population change, evacuation order/warning information, and proximity to fire, which are consistent with behavioral theories and empirical findings. SA-MGCRN can be directly used in future wildfire events to assist real-time decision-making and emergency management.