Evacuation Planning Research Articles

Community Participation in Flood Management Systems in Aceh Utara Regency

Flooding remains a persistent challenge in Aceh Utara Regency, representing a complex, cross-sectoral issue. If not addressed through collaboration among various stakeholders, including community groups, it has the potential to lead to new societal problems. This research seeks to assess the effectiveness and impact of using a participatory model in flood management efforts in the region. A qualitative approach was employed, gathering data through in-depth interviews with stakeholders such as local communities, government authorities, and non-governmental organizations involved in flood mitigation programs. The findings emphasize the urgency of adopting a participatory model to enhance the effectiveness of flood management systems. Actively engaging the community in planning, execution, and evaluation processes leads to a deeper understanding of local conditions, needs, and suitable solutions. Key aspects of the participatory model include raising public awareness of flood risks and mitigation measures, improving flood management infrastructure based on community input, creating more accessible and effective evacuation plans, and strengthening the community’s capacity for emergency response and simple mitigation efforts. In conclusion, this study aims to further explore the relevance and effectiveness of the participatory model in flood management in Aceh Utara Regency, ensuring the long-term sustainability of the region's disaster management system.

Cost-oriented fire safety design of urban structures

Providing adequate fire safety for urban buildings is a life-saving goal, and can be addressed in different ways. Reported studies integrating fire safety and life cycle cost (LCC) of conventional urban structures are rare. To address this gap, a cost–performance framework was developed. Four- and seven-storey steel (with and without fireproofing coatings) and reinforced concrete (RC) structures were designed, first for conventional loads and then fireproofed to meet their required fire-resistance rating. A range of sprinkler spacings, from 3200 mm (based on NFPA 13) to 12 000 mm, was configured. Fire was simulated using a fire dynamic simulation tool and the results were used to monitor evacuation plans and structural responses. The steel structures were additionally analysed under the assumption that they were not fireproofed. Human and financial damage costs were then estimated and the LCC for every case/scenario was determined. Interestingly, the results showed that the optimum LCC was not achieved for cases designed based on NFPA 13; instead, a sprinkler spacing of 6000 mm led to the optimum LCC. It was also found that steel structures without fireproofing do not have a higher LCC if the sprinklers are placed over a shorter distance. This result could be particularly beneficial for existing structures that are not fireproofed.

Modelling study on pedestrian evacuation dynamics considering exit selection behaviour under flood disaster

Flood spreading in metro stations is a dangerous hazard that frequently causes casualties and property losses. However, pedestrian evacuation under flood spreading scenarios has not been fully investigated. To fill this gap,we propose an extend Floor-Field model incorporating flood spreading and exit selection behaviour of individuals. In the model, the dynamic flood spreading process on metro platforms is expressed by using a simulation software named Mike21. The optimal exit selected by pedestrians in each step is decided on three factors, that is pedestrian density around the exit, pedestrian distance to the exit, and water depth at the exit. Meanwhile, the movement speed of pedestrain also changes with the water depth at each time step. Based on this, the transition probability to neighbor cell including static field, dynamic field, height field, and water flow field is calculated. This results in a flood evacuation model considering the pedestrian's behaviour when choosing an exit. The effects of flooding, pedestrian density, different exit selection behaviours, water flow field, and inlet flow on evacuation were thoroughly analysed. The analysis demonstrates that the likelihood of flooding significantly impacts the evacuation of pedestrians from the subway station; the more flooding is taken into account, the more influential the impact. In addition, the effect of inlet flow on evacuation is also noteworthy; considering exit selection behaviour can significantly increase the effectiveness of evacuation. The study's findings can be used to develop evacuation plans for flooded metro stations.

Rescue guiders layout study based on a two-layer optimization framework

In an emergency evacuation, the free evacuation of pedestrians can make the entire evacuation process slow and dangerous. To limit the free behavior of pedestrians and reduce the interaction between pedestrians, a reasonable layout of the guider can improve the efficiency and safety of evacuation. How to set the number, location, and exit allocation of guiders requires further investigation. In the current study, we transform the evacuation into a multi-objective optimization problem. A two-layer optimization framework is developed. In the upper level, the improved NSGA-II multi-objective algorithm is introduced to generate the favorable guider layout, and a chromosome fragment deletion operator is added to improve the optimization efficiency. In the lower layer, the agent movement simulation model is used to simulate the evacuation dynamic of crowd under the favorable guider layout. The variables of this multi-objective solution model in the upper layer are the number and location of the guiders. The evacuation time and agent movement cost are calculated by the lower layer simulation as the objective values of the solution sample, and guide the iterative search process to obtain more reasonable optimization results. The developed model is verified and then applied to a fictional scenario. The number, initial position and exit allocation of guiders are obtained by optimizing the iterative process. The results show that the near optimal solution can be applied in various visibility conditions, and the evacuation efficiency is much higher than that of unguided evacuation. This optimization framework can provide theoretical and methodological support for emergency evacuation planning.

Agent-based modelling of evacuation scenarios for a landslide-generated tsunami in Milford Sound, New Zealand

Agent-based modelling is a useful tool for evacuation planning as it can increase understanding of the factors affecting potential evacuation outcomes. In New Zealand, Milford Sound has been shown to have a high risk from landslide-generated tsunami, with an estimated 1-in-1000-year wave runup of ∼17 m arriving on shore within 2–7 min. With an annual average of >1500 people visiting a day, there is potential for widespread loss of life. However, the number of people present varies substantially with time of day and season, yet how this affects the ability to evacuate remains unknown. This research developed an agent-based model to understand how many people can be safely evacuated in Milford Sound and explored how the number of people initially exposed affected the evacuation outcome alongside the effect of potential changes to evacuation messaging. Assuming a 17 m wave, the results suggest that currently no one can safely evacuate before the shortest wave arrival time regardless of the number of people present. Altering evacuation messaging results in minimal gains, with only ∼5 % of the exposed population reaching safety in time. This work demonstrates the importance of evacuation modelling for understanding risk in isolated tourism destinations where the population exposure can fluctuate dramatically across multiple timescales. Accounting for changing population exposure is essential to understand whether evacuation is a suitable risk treatment and can provide valuable information for determining safe levels of population exposure in locations with high hazard but limited evacuation options.

Multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning

AbstractIn this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters.

A novel extended social force model for studying the impact of the heterogeneity of pedestrian physical fitness on emergency evacuation efficiency

Emergencies in public places, particularly confined crowded areas, will disrupt the stability of dense crowds and consequently lead to accidents. To promote public emergency safety, there is a pressing need for efficient modeling methods to investigate the evacuation mechanism in these places and improve the social public safety. This study proposes a Physical Fitness Heterogeneity based Social Force Model (PFH-SFM) that takes into account the heterogeneous desired evacuation velocity caused by the heterogeneity of pedestrian physical fitness, by means of developing the normalized desired velocity ratio. Then, we use PFH-SFM to investigate the relationships between the escape rate and the desired velocity, and between the evacuation duration and the desired velocity in terms of various group sizes with heterogeneous physical fitness, the relationship between the percentage of reduction in evacuation duration and desired velocity when including weak pedestrians, the pedestrian distribution in the evacuation process, the relationship between the total evacuation duration and the desired velocity in terms of various proportions of weak pedestrians and the relationship between the evacuation duration and the desired velocity in terms of various normalized starting and ending velocity ratios by considering various group sizes, respectively. The findings of this study show that the existence of a certain small proportion of pedestrians with weak physical fitness can promote global evacuation dynamics, especially in the case of high crowded density, and can reduce evacuation duration by up to 20% in our experiments. Additionally, when the percentage of pedestrians with weak physical fitness is relatively high, they tend to have a detrimental effect on the evacuation efficiency. Furthermore, there exists a moderate normalized desired starting velocity ratio that maximizes the overall evacuation efficiency; on the other hand, the lower the normalized desired ending velocity ratio is, the more efficient the evacuation is. To the best of the authors’ knowledge, this study is the first time to introduce the concepts of normalized desired starting and ending velocity ratios and innovatively analyzes the impact of the continuously changing desired velocity of pedestrians on the evacuation efficiency in multi-exit scenarios. The results offer valuable insights for relevant stakeholders to formulate effective evacuation plans, so as to enhance urban emergency capacity and minimize social and economic losses.

Preparing for the worst: Making evacuation plans accessible

Preparing for the worst: Making evacuation plans accessible

Fire emergency management of large shopping malls: IoT-based evacuee tracking and dynamic path optimization

As urbanization accelerates and buildings become more complex, fire emergency evacuation has become increasingly challenging. Traditional evacuation plans often struggle with slow response times and suboptimal path planning in real-time dynamic and complex fire scenarios. To address these issues, this study proposes the IoT-based DWM-Evac model for fire emergency evacuation path planning. The model leverages IoT technology by using various sensors placed inside buildings to monitor fire incidents and spread in real-time, collecting critical data such as temperature, smoke concentration, and flame location. It integrates Dynamic Graph Neural Networks (DGNN), Whale Optimization Algorithm (WOA), and Markov Decision Process (MDP) to enhance path efficiency and safety. Experimental results indicate that the DWM-Evac model achieves an average evacuation time of 315 s in a virtual mall environment, 25 s shorter than traditional plans, with an average path length of 255 meters and a path safety score of 0.92, higher than the traditional plan’s 0.88. The application of IoT in fire emergency management not only improves response speed but also optimizes path planning, significantly enhancing personnel safety.

Revolutionizing crowd safety: A breakthrough hybrid whale-bat chaotic algorithm (WOABCM) for optimized evacuation simulations

The Whale-Bat Chaotic Algorithm (WOABCM) is a revolutionary tool for optimizing pathfinding navigation in crowd evacuation scenarios. Conventional evacuation plans rely on static routes, causing traffic jams and decreased safety. WOABCM uses the erratic behavior of whales and bats to optimize escape routes in real time, making it particularly useful in large crowd evacuations. The algorithm can adapt to different scenarios, ensuring its continued efficacy across real-world conditions. Iteratively iterating through the evacuation environment, WOABCM iteratively finds the most effective ways for agents to reach exits. In this experiment, the results show notable improvements in evacuation times, with WOABCM determining and directing agents through the shortest and least crowded paths in a room setting and assigning agents to exits in more complicated scenarios. The algorithm's flexibility also allows it to swiftly recompute pathways for agents, avoiding obstructions or congestion points. This groundbreaking approach to crowd evacuation simulation demonstrates how chaos theory-inspired algorithms can be used to solve practical problems

Deep-TCP: Multi-source data fusion for deep learning-powered tropical cyclone intensity prediction to enhance urban sustainability

Tropical cyclones (TC) exert a profound impact on cities, causing extensive damage and losses. Thus, TC Intensity Prediction is crucial for creating sustainable cities as it enables proactive measures to be taken, including evacuation planning, infrastructure reinforcement, and emergency response coordination. In this study, we propose a Deep learning-powered TC Intensity Prediction (Deep-TCP) framework. In particular, Deep-TCP contains a data constraint module for fusing data features from multiple sources and establishing a unified global representation. To capture the spatiotemporal attributes, a Spatial-Temporal Attention (ST-Attention) module is built to distill insights from environmental variables. To improve the robustness and stability of the predictions, an encoder-decoder module that utilizes the ConvGPU unit is introduced to enhance feature maps. Then, a novel feature enhancement module is built to bolster the generalization capability and solve the dependency attenuation. The results demonstrate that the Deep-TCP framework significantly outperforms various benchmarks. Additionally, it effectively predicts multiple TC categories within the 6–24 h timeframe, showing strong capability in predicting changing trends. The reliable prediction results are potentially beneficial for disaster management and urban planning, significantly enhancing urban sustainability by improving preparedness and response strategies.

3D printing indoor evacuation plans for visually impaired people

3D printing indoor evacuation plans for visually impaired people

Optimization of personnel evacuation routes for sudden nuclear accidents based on hybrid genetic-grey wolf optimizer algorithm

To address the emergency situation resulting from a sudden nuclear accident, it is imperative to develop an optimal emergency response plan that ensures the prompt and safe evacuation of affected individuals from the hazardous area.The paper proposed the hybrid genetic-grey wolf optimizer algorithm (GA-GWO).This algorithm integrated radiation environment and road network to plan evacuation paths that minimizes the radiation dose. This algorithm enhanced the convergence and search capabilities of the genetic algorithm (GA) by incorporating wolf pack characteristics and internal competitive behavior. This improvement allowed for more efficient exploration of potential evacuation routes. The feasibility of GA-GWO was validated through a visualization simulation utilizing Web Geographic Information Systems (WebGIS). The findings indicated that GA-GWO generated evacuation paths that minimize radiation dose, providing safe routes for the affected population. This approach demonstrates significant potential for application in emergency response planning for sudden nuclear accidents, ensuring both prompt and safe evacuations.

Evacuation planning for tourists: Lessons from cyclone Fani (2019), India

The evacuation of tourists before hurricanes or cyclones is grossly understudied compared to the evacuation of residents, especially in developing countries. To address this gap, the challenges encountered while evacuating tourists from Puri in India before Cyclone Fani in 2019 were studied. Key informant interviews were conducted with stakeholders to gain a comprehensive understanding of the evacuation operation and their individual roles in it. The major administrative issues identified were: a) inefficiencies in risk communication and b) reactive approach to transportation supply management. Delayed actions led to confusion, insufficient transport supply and delayed the evacuation operation. Tourists were reported to show resistance against evacuation, when the authorities were enforcing the evacuation order. Their resistance stemmed from regret for their unfinished holiday, faith in Lord Jagannath as the savior of Puri, and eagerness to experience the cyclone for its thrill. According to the hoteliers' observations, the presence of elderly and child members in tourist groups motivated evacuation, while higher leftover stay duration deterred them from evacuating. To improve the timeliness of evacuation operations and manage behavioral issues, three categories of interventions are advocated.

Analysis and Geovisualization of Tsunami Hazard and Evacuation Routes in the Opak-Progo Coastal Area

The southern coastal area of Java has a high risk of tsunamis. Additionally, the presence of rivers flowing in the south of Java Island poses a greater tsunami threat because these rivers can act as "toll roads" for tsunami waves to enter the land. Therefore, this study was conducted in the coastal area of the Opak-Progo watershed to determine the level of tsunami hazards and plan effective evacuation routes. The hazard map was created using the Berryman method, which employs parameters such as slope, surface roughness coefficient, coastline, and tsunami run-up height scenario; a 3-metre scenario was used in this study. Network analysts also determined evacuation routes using the nearest facility method. Network analysis was used to identify an optimised route with four evacuation sites. This research has the potential to significantly contribute to tsunami mitigation and evacuation planning in the coastal areas of the Opak-Progo watershed.

A novel multi-source information fusion method for emergency spatial resilience assessment based on Dempster-Shafer theory

In the midst of today's intricate and ever-changing natural and social landscape, this study is committed to proposing a novel multi-source information fusion method for assessing the spatial resilience of urban emergency evacuation and rescue. Its overarching aim is to uncover latent challenges and contribute to the enhancement of a city's capacity to respond to emergencies. To achieve this, we have devised a comprehensive assessment indicator system, capable of not only quantifying a city's spatial resilience but also offering indispensable guidance for urban emergency evacuation and rescue spatial planning. Furthermore, we have introduced an innovative evaluation methodology framework. This framework includes the establishment of the basic probability assignment (BPA) model, a pioneering method for generating BPA for observed values, and the novel application of hierarchical weighting and fusion techniques. By extending the Dempster-Shafer theory, we have not only enhanced the method's ability to express and integrate uncertain information but also significantly improved the precision of the evaluation. Ultimately, we conducted a quantitative assessment using Shenzhen, China, as a case study, identifying existing issues and proposing highly-targeted improvement strategies. These research findings not only provide robust support for the augmentation of urban emergency capabilities in Shenzhen but also offer pioneering insights for the quantitative assessment and advancement of emergency spatial resilience in cities across the globe.

Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning

Rapid prediction of Road Network Functionality (RNF) during extreme rainfall-induced flooding is crucial for supporting proactive and real-time emergency planning, such as rescue, evacuation planning, and emergency supply distribution. Unlike normal operational conditions, extreme rainfall events introduce complex non-stationary, non-Euclidean characteristics to RNF due to intricate meteorological and hydrological processes, as well as the role of a community's road network in emergency response planning. Conventional physics-based flood simulations and flow-based road network analyses typically lack the computational efficiency required for real-time RNF predictions, hindering timely risk mitigation decisions. This study leverages the accuracy of physics-based simulations and the efficacy of deep-learning technologies to develop a deep learning-based surrogate model for Rain-to-RNF (R2R) predictions. This model couples Long Short-Term Memory (LSTM) networks with Spatial-Temporal Graph Convolutional Networks (ST-GCNs) to uniquely capture the spatiotemporal dynamics of RNF under extreme rainfall events. The predictive accuracy, stability, and versatility of the R2R surrogate model are demonstrated in four flood-prone communities in Zhejiang Province. Its implementation during Typhoon Fitow (2013) over a 30-hour intense rainfall showcases its promising predictive capacity and unparalleled computational efficiency. This research advances disaster management, enhancing the resilience and responsiveness of community infrastructure during extreme weather events.

Navigating dual hazards: Managing hurricane evacuation and sheltering operations Amidst COVID-19

Hurricane Laura made landfall in August of 2020 as a Category 4 storm, heavily affecting the coastlines of Texas and Louisiana. At the same time, both areas were coping with a surge in COVID-19 cases absent a vaccine. For this study, we explored how emergency management stakeholders navigated evacuation, sheltering, and re-entry planning and operations during a dual hazard threat where hurricane risk reduction measures contradicted COVID-19 risk reduction measures. We conducted 35 semi-structured interviews with emergency management stakeholders in Texas and Louisiana in the spring of 2021. Participants were recruited using a purposive sampling strategy designed to identify agency representatives and other officials involved with planning for hurricane season and with the Hurricane Laura response effort. We used thematic analysis to code and analyze the data. Findings suggest that while the process for planning for hurricanes had to shift to an online approach, the plan for addressing hurricane season was largely unchanged from the perspective of most participants until days before impact, when the decision was made to move to noncongregate sheltering. This led to adaptive and creative improvisation on the part of participants managing the evacuation and sheltering operations. Likewise, we found that caring for and communicating with evacuees in a noncongregate setting presented unanticipated challenges that required improvisation to address. Given the continued threat posed by COVID-19 and other respiratory illnesses, this study provides insights that emergency management stakeholders could apply to current and future planning and response efforts for hazards that require evacuation and sheltering.

Rapid prediction for the transient dispersion of leaked airborne pollutant in urban environment based on graph neural networks

Rapidly predicting airborne pollutant dispersion in urban is vital for ventilation design and evacuation planning. Computational fluid dynamics (CFD) simulations are commonly used to provide accurate predictions, but the computational cost is too high. Although graph neural networks (GNNs) provide fast predictions of flow fields by manipulating unstructured mesh on GPU, they suffer from high memory usage and accuracy decreases when applied to large-scale urban scenes. Moreover, it is difficult for GNNs to learn the coupled relationship between wind field and pollutant concentration field. We propose a multi-objective GNN model as CFD surrogate to rapidly predict the transient dispersion of airborne pollutant under the influence of complex wind field patterns in urban environment. Based on random urban layouts generated by a 2D bin packing algorithm, we employ a validated CFD model to construct a sample dataset of wind fields and concentration fields. We leverage graph pooling and multi-scale feature fusion to improve prediction accuracy, and subgraph partitioning of both wind field and concentration field to reduce GPU memory usage. The results show that our GNN model at its best runs 1–2 orders of magnitude faster than CFD simulation with accuracy evaluation metrics R2=0.92, and achieves 70 % GPU memory reduction.

The Creation of an Individualized School Plan for Optimal Inclusion of Students with Osteogenesis Imperfecta

Aims The aims of this study were to: (1) synthesize existing evidence regarding the integration of students with osteogenesis imperfecta (OI) into the school setting, (2) tabulate existing school integration tools for OI, and (3) create an individualized school plan to facilitate school integration. Methods Guided by the process of developing evidence-informed guidelines, an international, interprofessional, expert task force was convened. The process entailed: (1) reviewing of the literature, (2) developing recommendations, and (3) creating a clinically meaningful, person-focused plan to facilitate the integration and promotion of school inclusivity. The 13-member task force relied on empirical studies, grey literature, and their experiential knowledge (from clinical, teaching or patient experiences) to devise the plan. Results Over a series of eight meetings and five drafts, the Task Force prioritized 14 core items for inclusion. These items consisted of general student information, fracture response protocol, student inclusion recommendations, mobility considerations, transfer considerations, toileting protocol, physical education recommendations, fieldtrip information, transportation considerations, evacuation plan, environmental and scholarly considerations, consent and authorization, and an annual renewal document. Conclusion Further research is recommended to pilot the plan, solicit ongoing feedback, implement and evaluate the plan into routine education and health care practices.