Search Results (110)

Early evacuation during bushfires remains the safest strategy; however, in many realistic scenarios, timely evacuation is challenging, making safe sheltering a last-resort option to reduce risk compared to late evacuation attempts. However, most Australian homes in bushfire-prone areas are neither designed nor retrofitted to provide adequate protection against extreme bushfires, raising safety concerns. This study addresses this gap by investigating the concept of retrofitting a part of the residential buildings as attached safe rooms for sheltering and protection of valuables, providing a potential last-resort solution for bushfire-prone communities. Numerical simulations were conducted using the Fire Dynamics Simulator to assess heat transfer and internal temperature conditions in a representative residential building under bushfire exposure conditions. The study investigated the impact of the placement of the safe room relative to the fire front direction, failure of vulnerable building components, and the effectiveness of steel shutters in response to internal temperatures. The results showed that the strategic placement of safe rooms inside the building, along with adequate protective measures for windows, can substantially reduce internal temperatures. The findings emphasised the importance of maintaining the integrity of openings and the external building envelope, demonstrating the potential of retrofitted attached safe rooms as a last-resort solution for existing residential buildings in bushfire-prone areas where the entire building was not constructed to withstand bushfire conditions. Full article

Chemical leakage accidents in chemical industrial parks pose significant threats to personnel safety, particularly during evacuation processes, where individual behavior and evacuation strategies have a considerable impact on overall efficiency. This study takes a leakage incident at an alkylation unit as a case study. First, ALOHA5.4.7 software was used to simulate the influence of meteorological conditions across different seasons on the dispersion range of toxic gases, thereby generating an annual comprehensive risk zone distribution map. Subsequently, different evacuation scenarios were constructed in Pathfinder2024.1.0605, with the integration of trigger mechanisms to simulate individual behaviors during evacuation, such as variations in risk perception and peer influence. Furthermore, this study expanded the conventional application scope of Pathfinder—typically limited to small-scale building evacuations—by successfully adapting it for large-scale evacuation simulations in chemical industrial parks. The feasibility of such simulations was thereby demonstrated, highlighting the software’s potential. According to the simulation results, exit configuration, shelter placement, and individual behavior modeling significantly affect the total evacuation time. This study provides both theoretical insights and practical guidance for emergency response planning in chemical industrial parks. Full article

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments. Full article

The growing complexity of natural disasters, intensified by climate change, has amplified the challenges of managing emergency shelter demand. Accurate shelter demand forecasting is crucial to optimize resource allocation, prevent overcrowding, and ensure evacuee safety, particularly during concurrent disasters like hurricanes and pandemics. Real-time decision-making during evacuations remains a significant challenge due to dynamic evacuation behaviors and evolving disaster conditions. This study introduces a spatiotemporal modeling framework that leverages connected vehicle data to predict shelter demand using data collected during Hurricane Sally (September 2020) across Santa Rosa, Escambia, and Okaloosa counties in Florida, USA. Using Generalized Additive Models (GAMs) with spatial and temporal smoothing, integrated with GIS tools, the framework captures non-linear evacuation patterns and predicts shelter demand. The GAM outperformed the baseline Generalized Linear Model (GLM), achieving a Root Mean Square Error (RMSE) of 6.7791 and a correlation coefficient (CORR) of 0.8593 for shelters on training data, compared to the GLM’s RMSE of 12.9735 and CORR of 0.1760. For lodging facilities, the GAM achieved an RMSE of 4.0368 and CORR of 0.5485, improving upon the GLM’s RMSE of 4.6103 and CORR of 0.2897. While test data showed moderate declines in performance, the GAM consistently offered more accurate and interpretable results across both facility types. This integration of connected vehicle data with spatiotemporal modeling enables real-time insights into evacuation dynamics. Visualization outputs, like spatial heat maps, provide actionable data for emergency planners to allocate resources efficiently, enhancing disaster resilience and public safety during complex emergencies. Full article

Natural disasters often occur unexpectedly, catching people off guard, such as the Hualien earthquake on 3 April 2024. Many of the evacuation-monitoring systems currently in place lack real-time updates of shelter capacities, which raises the risk of overcrowding under wartime scenarios. This study developed a system for the targeted assignment of evacuation sites during air raids. The DBSCAN algorithm was used to group data based on pedestrian flow patterns and an LSTM model was used to enhance the prediction speed and accuracy. Weighted Voronoi diagrams delineated regions to identify optimal evacuation points, while real-time SMS notifications through base station positioning disseminated evacuation information to the public. The experiment results demonstrated the effectiveness of the proposed system in facilitating safe evacuations across a broad range of geographic regions while reducing the number of LSTM models. Dynamic updates on the shelter capacities make it possible for citizens to make informed decisions during air raid emergencies. Full article

Flood incident management involves taking actions to save lives and reduce damages during a flood. Agent-based modelling tools have recently been developed to simulate the dynamic interactions between people and floodwater as a flood event unfolds. To date, these have only been applied in locations with a wealth of data, relying upon bespoke local or national datasets. Although informal settlements have a concentration of vulnerable people and are often more exposed to natural hazards, data availability is often limited, posing challenges for planning and implementing flood incident management actions. In this study, a model that was first applied in the UK is adapted and applied to simulate flood evacuations in Kibera, a densely populated informal settlement in Nairobi. Although data quality limits some of the model’s potential, the results reproduce patterns of observed behaviour. Evacuation shelters in the Northwest, North, and Northeast are shown to perform best. A major exit route to the South, a bridge crossing, and a river path are shown to be especially prone to congestion during evacuations. This paper reports on the first application of an agent-based model to an informal settlement, Kibera. The demonstration is an important step towards an operational tool for flood incident management planning in informal settlements around the world. Full article

Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. Full article

With the continued recognition of the devastating effects of natural hazards, the construction of shelters has become essential in urban disaster preparedness planning systems. After analyzing the deficiency of the conventional spatial allocation model of shelters and the hierarchy of evacuation assignments, this study proposes a bi-level and two-phase spatial configuration methodology of shelters. The first hierarchy aims to evacuate refugees from demand blocks to both emergency shelters and resident emergency congregate shelters. The second hierarchy aims to transfer refugees from selected shelters in the first hierarchy to resident emergency congregate shelters. Each hierarchy contains two phases of optimizing calculations. The optimization objects for the first phase and second phase are minimizing the number of new shelters and the evacuation time, respectively. A genetic algorithm and exhaustive approach are programmed to determine the solution of the model in the first and second phases, respectively. The evacuation assignment rule is proposed based on the gravity model, which distributes evacuees proportionally to nearby shelters. This study uses the deterministic user equilibrium problem to present the evacuation traffic flow allocation, which improves the scientificity of the location model of shelters. The refuge demands differentiate the population between daytime and nighttime through mobile signaling data and improve the accuracy from the plot scale to the building scale. Using mobile signaling data to differentiate refuge demands between day and night populations enhances the model’s precision. Finally, to validate the proposed methodology, this study selected the main area of Changshu City, Jiangsu Province, China, which has a population of 1.6 million, as a case study area. Full article

Deciding the locations of shelters and how to assign evacuees to these locations is crucial for effective disaster management. However, the inherent uncertainty in evacuation demand makes it challenging to make optimal decisions. Traditional stochastic or robust optimization models tend to be either too aggressive or overly conservative, failing to strike a balance between risk reduction and cost. In response to these challenges, this research proposes a multi-objective distributionally robust optimization (MODRO) model tailored for shelter location and evacuation allocation. First, an ambiguity set (moment-based or distance-based) is constructed to capture the uncertainty in evacuation demand, reflecting the possible range of outcomes based on demand data from a disaster simulation model. Then, the distributionally robust optimization model considers the “worst-case” distribution within this ambiguity set to minimize construction cost, travel distance, and unmet demand/unused capacity, balancing the trade-off between overly conservative and overly optimistic approaches. The model aims to ensure that shelters are optimally located and evacuees are efficiently allocated, even under the most challenging scenarios. Furthermore, Pareto optimal solutions are obtained using the augmented ε-constraint method. Finally, a case study of Ogu, a wooden density built-up area in Tokyo, Japan, compares the DRO model with stochastic and robust optimization models, demonstrating that the cost obtained by the DRO model is higher than a stochastic model while lower than the worst-case robust model, indicating a more balanced approach to managing uncertainty. This research provides a practical and effective framework for improving disaster preparedness and response, contributing to the resilience and safety of urban populations in earthquake-prone areas. Full article

The extent of casualty and property loss due to chemical accidents depends on how well the emergency action plan was established in advance and how fast the warning notice and evacuation orders are given to the public. Assistant methods for the establishment of protective action plans have been developed for several decades. However, the currently developed decision trees are complicated, so they may require a detailed analysis, and previous decision matrices do not consider the indoor and outdoor concentration directly and hence do not allow a change in evacuation order. In this study, five key parameters, report time, toxic cloud arrival/removal time and indoor/outdoor concentration, are selected for the evacuation decision, and the effectiveness of leakage and wind speed on five parameters is investigated. CFD simulations are performed for the various values of mass flow rate and wind speed. Near the release point of toxic gas, the maximum concentration is unaffected by wind speed, but the mass flow rate significantly influences it at low wind speeds. In the far field, the maximum concentration decreases with increasing wind speed. The termination time for shelter-in-place, suggesting a shift to evacuation, decreases with both higher mass flow rate and wind speed. For smaller mass flow rates ($\dot{m}=0.1\mathrm{kg}/\mathrm{s}$), indoor concentration exceeds outdoor levels after 25.9 min, while for larger mass flow rates ($\dot{m}=2.0\mathrm{kg}/\mathrm{s}$), this time shortens to 15.2 min. Increasing wind speed from 0.5 $\mathrm{m}/\mathrm{s}$ to 5.0 $\mathrm{m}/\mathrm{s}$ decreases the equilibrium concentration from 13.9 $\mathrm{ppm}$ to 3.4 $\mathrm{ppm}$ and reduces the escape time from 48.9 min to 16.0 min. Overall, higher mass flow rates and wind speeds shorten the equilibrium and escape times, improving toxic cloud removal efficiency. Based on the simulation results, a new evacuation decision matrix is developed which minimizes the total exposure concentration. This study provides the proper evacuation time along distance which eventually prevents traffic congestion because of the simultaneous escape rush. Full article

This paper reports on research undertaken for the Building Resilience for Bushfire-Affected Communities in Noosa Shire project, funded by the Australian Government. Being evacuated from a home in the path of a bushfire can be traumatic. Therefore, it is important for evacuees to have safe places to stay, both physically and psychologically. Using a qualitative approach, we aimed to (a) understand the experiences of people who were displaced from their homes and sheltered at evacuation centres during the Noosa Shire bushfires and (b) understand what support is needed during disasters, such as bushfires, to help create positive experiences for future evacuees. Twelve participants displaced by bushfires in Noosa, Australia, in 2019 recalled their experiences in semi-structured interviews (conducted in 2022–2023). Inductive thematic analysis using NVivo 13 identified three themes that can inform government and public disaster preparation and response: planning, support, and communication. Findings from this study centred around building community resilience and offer valuable insights for organising disaster evacuation processes and evacuation centres on a broader scale. For individuals, it involves planning optimal evacuation routes, gathering necessary personal items, feeling safe and calm in evacuation centres, and receiving regular and accurate communication from authorities during disaster events. Full article

Background: On 1 January 2024, a 7.6 magnitude earthquake struck the Noto Peninsula. We entered the disaster area to provide relief and set up a makeshift clinic in an evacuation center to evaluate the quality and quantity of food provided there. Methods: This cross-sectional study, of mainly older adults, was conducted to analyze the amino acid and fatty acid composition of evacuation shelter meals in comparison with the results of the Japan National Survey, mainly focused on older adults. (1) We analyzed 11 evacuation foods using the “Duplicated Combination” Model and the digestible amino acid score (DIAAS) in relation to the half-life determined by the N-terminal amino acid proteins. (2) Linoleic acid (LA) and alpha-linolenic acid (ALA) levels were compared with European Food Safety Authority (EFSA) recommendations (3). The national survey of emergency food stocks in 198 hospitals and 189 social care institutions conducted in Jan 2024 was analyzed. Results: (1) DIAAS was less than 1.00 for all 11 foods provided and was considered inadequate, (2) the half-life of the protein, whose N-terminal valine has a half-life of 100 h, must be considered a possible deficiency when living in a shelter for more than a week, (3) LA and ALA levels were less than 40% of EFSA recommended, (4) the nationwide survey found that 80% of people have a three-day supply and data on amino acids and fatty acids were not available due to a lack of questionnaires. Conclusion: Analysis of food in evacuation shelters after the Noto Peninsula earthquake revealed the quality of amino acids involved in shelter meals using DIAAS and the lack of LA and ALA for older adults. The “Duplicated Combination” model used in this analysis could be beneficial for developing improved nutrition plans in similar future scenarios, mainly for older adults. Full article

Predicting the distribution of people in the time window approaching a disaster is crucial for post-disaster assistance activities and can be useful for evacuation route selection and shelter planning. However, two major limitations have not yet been addressed: (1) Most spatiotemporal prediction models incorporate spatiotemporal features either directly or indirectly, which results in high information redundancy in the parameters of the prediction model and low computational efficiency. (2) These models usually incorporate certain basic and external features, and they can neither change spatiotemporal addressed features according to spatiotemporal features nor change them in real-time according to spatiotemporal features. The spatiotemporal feature embedding methods for these models are inflexible and difficult to interpret. To overcome these problems, a lightweight population density distribution prediction framework that considers both basic and external spatiotemporal features is proposed. In the study, an autoencoder is used to extract spatiotemporal coded information to form a spatiotemporal attention mechanism, and basic and external spatiotemporal feature attention is fused by a fusion framework with learnable weights. The fused spatiotemporal attention is fused with Resnet as the prediction backbone network to predict the people distribution. Comparison and ablation experimental results show that the computational efficiency and interpretability of the prediction framework are improved by maximizing the scalability of the spatiotemporal features of the model by unleashing the scalability of the spatiotemporal features of the model while enhancing the interpretability of the spatiotemporal information as compared to the classical and popular spatiotemporal prediction frameworks. This study has a multiplier effect and provides a reference solution for predicting population distributions in similar regions around the globe. Full article

The determination of the appropriate traffic signal timing plans for no-notice evacuations in densely populated areas is a noteworthy challenge. The objective of this study was to evaluate alternatives that could optimize evacuee traffic flow in a no-notice evacuation of areas near an oil refinery. This simulation case study focused on a residential area in the City of Wood River, Illinois, and used Synchro 8.0 and VISSIM 7.0. This case study was different from existing evacuation literature because of the high left-turn demand from evacuating traffic. The study methods were unique because of the application of dynamic traffic assignment, a left-turn movement on the evacuation route, and the simulation of fully-actuated traffic signals. These scenarios evaluated the following: (1) existing traffic infrastructure; (2) flexible shelter choice; and (3) optimized traffic signal timing with flexible shelter choice. The results suggested that optimizing the signal timing and allowing drivers’ flexibility in choosing evacuation routes achieved the fastest evacuation. These findings indicated that a longer cycle length and an extended left-turn phase were important factors in reducing traffic delay in the network. Overall, these findings underscore the importance of operating intersections efficiently during no-notice evacuations. Full article

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS. Full article