Augmented reality for pedestrian evacuation research: Promises and limitations

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Automation in Fire Safety Engineering Using BIM and Generative Design

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Evacuation Data from a Hospital Outpatient Drill The Case Study of North Shore Hospital

Assessing the fire safety of buildings is fundamental to reduce the impact of this threat on their occupants. Such an assessment can be done by combining existing models and existing knowledge on how occupants behave during fires. Although many studies have been carried out for several types of built environment, only few of those investigate healthcare facilities and hospitals. In this study, we present a new behavioural data-set for hospital evacuations. The data was collected from the North Shore Hospital in Auckland (NZ) during an unannounced drill carried out in May 2017. This drill was recorded using CCTV and those videos are analysed to generate new evacuation model inputs for hospital scenarios. We collected pre-movement times, exit choices and total evacuation times for each evacuee. Moreover, we estimated pre-movement time distributions for both staff members and patients. Finally, we qualitatively investigated the evacuee actions of patients and staff members to study their interaction during the drill. The results show that participants were often independent from staff actions with a majority able to make their own decision

Evacuation Data from a Hospital Outpatient Drill The Case Study of North Shore Hospital

Assessing the fire safety of buildings is fundamental to reduce the impact of this threat on their occupants. Such an assessment can be done by combining existing models and existing knowledge on how occupants behave during fires. Although many studies have been carried out for several types of built environment, only few of those investigate healthcare facilities and hospitals. In this study, we present a new behavioural data-set for hospital evacuations. The data was collected from the North Shore Hospital in Auckland (NZ) during an unannounced drill carried out in May 2017. This drill was recorded using CCTV and those videos are analysed to generate new evacuation model inputs for hospital scenarios. We collected pre-movement times, exit choices and total evacuation times for each evacuee. Moreover, we estimated pre-movement time distributions for both staff members and patients. Finally, we qualitatively investigated the evacuee actions of patients and staff members to study their interaction during the drill. The results show that participants were often independent from staff actions with a majority able to make their own decision.falseBorå

Evacuation response behaviour of occupants in a large theatre during a live performance

This paper presents the results of an unannounced theatre evacuation involving 1200 occupants. The evacuation took place towards the end of a live theatre performance in the Marlowe Theatre in Kent, UK. In particular, Response Phase behaviours are discussed, and response time data is presented. A significant finding of this work which is different to other reported work is that the occupant response time distribution, while following the typical log-normal distribution is related to the geometrical positioning of the occupants relative to proximity to exit aisles and exits. Response time is found to increase relative to seat distance from the exit aisles and distance of the seat row to an exit. The identified trends in response time distribution will have a profound impact on the analysis of evacuation times and congestion levels determined using agent based evacuation models and so should be represented within these models. Based on these findings, a generalised methodology is proposed to distribute response time within a theatre for use in evacuation simulation applications. Further experimental analysis is required to determine whether these observations can be generalised and applied to other seated venues such as cinemas, music venues and sports arenas

Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires

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. Therefore, this study develops and tests a new methodological framework for modeling trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. The proposed methodology aims at forecasting evacuation trips and other types of trips. 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 in this study for 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 evacuation order/warning information, proximity to fire, and population change, which are consistent with behavioral theories and empirical findings