Search Results (253)

Background: The COVID-19 pandemic triggered unprecedented mobility disruptions worldwide as governments imposed strict lockdowns to contain the spread of the virus. In India, prolonged restrictions severely affected economic activity, particularly for migrant workers, leading to a large-scale and unplanned exodus from urban employment centres to native places. This sudden population movement undermined containment efforts and contributed to the spatial diffusion of infections. Understanding evacuees’ behavioural responses during such crises is therefore critical for effective emergency logistics and evacuation planning. Methods: This study examines the determinants of transport mode and shelter choice decisions made by migrants during the COVID-19-induced evacuation in India. Using primary survey data, a multinomial logistic regression model is developed to analyze how socio-economic characteristics influence evacuees’ choices of travel mode and shelter type. Results: The results reveal significant heterogeneity in decision-making, highlighting the role of economic vulnerability and accessibility constraints in shaping evacuation behaviour. Conclusions: The findings offer actionable insights for policymakers and emergency planners to design inclusive evacuation strategies, improve crisis-responsive transportation planning, and enhance shelter provisioning in future pandemics or large-scale disruptions. The study contributes to the logistics and humanitarian operations literature by providing empirical evidence on evacuation behaviour under public health emergencies. Full article

Hospitals in earthquake-prone regions must evacuate heterogeneous occupants rapidly while preserving operational continuity under disrupted conditions. However, many hospital-evacuation studies still rely on static routing assumptions or narrowly defined behavioral rules, which limits their value for building-level resilience planning. This paper develops a comparative hospital-campus evacuation framework that combines GIS-based geodesic routing, heterogeneous agent-based modeling, and reinforcement-learning-based decision policies. Puge County People’s Hospital in Sichuan, China, is used as the case study. Six algorithms are evaluated: three rule-based baselines—Shortest Path (SP), Random Walk (RW), and the Social Force Model (SFM)—together with a training-free density-aware heuristic, Density-Aware Gradient Routing (DAGR), and two reinforcement-learning approaches, Density-Aware Q-Learning (DAQL) and SARSA. Experiments cover three population scales ($N\in \{50,100,200\}$), normal daytime conditions, staffing-variation scenarios, and a blocked-exit disruption scenario, with 30 independent runs for each main condition. The results show that the rule-based and training-free methods remain the most reliable under full multi-agent evaluation: the SFM and RW achieve the highest completion ratios (approximately 100% and 93.5%, respectively), while DAGR provides the strongest balance between completion and evacuation efficiency among the non-trained methods. In contrast, the trained RL agents perform substantially worse in direct multi-agent deployment with DAQL reaching approximately 37% completion and SARSA approximately 17%, highlighting a train–evaluation distribution shift associated with independent Q-learning. The ablation analysis further shows that collision avoidance is the most critical reward component, whereas density-avoidance shaping can unintentionally induce collective deadlock when all agents execute the learned policy simultaneously. Among the enhanced variants, DAQL_RoleAware yields the best overall improvement, increasing the completion ratio to approximately 52% and reducing the 90th-percentile evacuation time to approximately 363 s. Overall, this paper clarifies both the promise and the present limitations of density-aware reinforcement learning for hospital evacuation while providing a more building-centred and reproducible basis for future coordination-aware evacuation design and emergency-planning research. Full article

Introduction: Early warning systems reduce losses when risk knowledge, forecasting, communication, and response planning operate as an end-to-end chain, yet Gulf Coast warning practice often treats hazard dynamics, ecosystem change, and social vulnerability as separate domains. This study mapped operational early warning systems for climate-relevant hazards across Louisiana, Texas, Mississippi, Alabama, and Florida and examined whether ecosystem protective functions and social vulnerability were integrated into warning thresholds, dissemination design, and preparedness planning. Methods: I conducted a scoping review using the Web of Science Core Collection and Scopus for publications from 2020 through 18 January 2026 and targeted searches of NOAA/NWS/NHC, FEMA IPAWS, CDC/ATSDR SVI, IOOS/GCOOS, USGS, and state coastal agency portals between 15 September 2025 and 18 January 2026. Of 861 identified records, 440 duplicates were removed, 421 titles and abstracts were screened, 121 full texts were assessed, and 25 sources were included in the final charting and synthesis. Results: The review identified 11 operational systems and related platforms spanning the four early warning pillars, but routine socio-ecological integration remained limited. Louisiana showed the strongest documentation of ecosystem monitoring through CPRA and CRMS, while Florida and Texas showed more developed evacuation and dissemination interfaces. Mississippi and Alabama were represented by thinner monitoring and implementation records in the included sample. Across states, ecosystem loss and social vulnerability were used more often as planning context than as repeatable inputs to thresholds, message tailoring, or assistance triggers. Discussion: Gulf Coast practices can be strengthened through formal protocols that connect ecosystem condition and vulnerability indicators to impact-based briefings, multilingual and accessible alert workflows, and tract-sensitive preparedness actions. The findings indicate that implementation can advance by linking existing datasets to defined operational decisions and by evaluating warning performance through reach, accessibility, comprehension, and action feasibility, as well as technical accuracy. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Hurricanes create compound hazards such as storm surge, flooding, and wind-driven debris that can degrade roadway capacity, fragment network connectivity, and hinder evacuation and shelter operations. From a sustainability perspective, improving evacuation planning is essential for reducing disaster-related losses, protecting vulnerable populations, and strengthening the resilience of coastal communities facing intensifying climate-driven hazards. This paper develops a micro-scale, agent-based evacuation modeling framework to assess evacuation performance under baseline and compound-hazard conditions, with emphasis on municipal decision support. The framework is demonstrated for Westbrook, Connecticut, at the census block-group scale in AnyLogic by integrating household locations, vehicle availability, road-network connectivity, and shelter capacities from publicly available datasets. Evacuation propensity and destination choice are parameterized using survey data, enabling empirically grounded decisions for in-town versus out-of-town evacuation among household-vehicle agents. Compound disruptions are represented through flood-related road closures derived from SLOSH storm-surge outputs and stochastic wind-related disruptions that dynamically constrain accessibility during the simulation. Scenarios are evaluated for Saffir–Simpson Category 1–2 and Category 3–4 hurricanes under baseline and compound conditions. Model outputs quantify normalized evacuation time, congestion and critical intersections, shelter demand and unmet capacity, evacuation failure, and spatial heterogeneity across block groups. Results indicate that compound flooding substantially increases evacuation times and failure rates, with the largest performance degradation concentrated in higher-vulnerability areas. Optimization experiments further compare the effectiveness of behavioral shifts, shelter-capacity expansion, and earlier departure timing in reducing delays and unmet shelter demand. Overall, the proposed framework provides transparent, reproducible, and scalable analytics that town engineers and emergency planners can use to evaluate evacuation readiness under compound hurricane impacts. Full article

Virtual reality (VR)-based evacuation training provides a safe and immersive environment for participants to experience disaster scenarios. However, existing systems often prioritize the experience itself, leaving the critical stage of reflection—essential for refining and stabilizing evacuation knowledge—under-supported. This study presents a qualitative pilot investigation into an extended reflection support function for a VR-based evacuation training system. Unlike traditional replay functions that only visualize avatar movements, our system synchronizes spatialized environmental sounds and recorded verbal utterances, i.e., voices of the user and non-player characters (NPCs), with the visual replay. A preliminary experiment involving eight university students was conducted to evaluate how these auditory clues influence the reflection-on-action process. Qualitative results indicate that audio clues help participants recall their internal decision-making processes and provide essential context for understanding the actions of others (NPCs). The findings suggest that the integration of auditory information facilitates evacuation knowledge refinement, i.e., the transition from mere experience to the formulation of concrete survival concepts. Although limited by a small sample size, this study highlights the potential of multi-modal reflection support in VR-based evacuation training. Full article

The adoption of electric vehicles (EVs) has posed new challenges to fire safety, especially when multiple EVs are transported on electric trailers, as limited studies exist on heavy electric vehicle transportation and little research has been conducted on fire development during EV tunnel transport. The aim of this study is to investigate the temperature, smoke, and tenability conditions produced by an electric trailer transporting eight EVs, where a fire initiates and spreads to all eight EVs, under two scenarios: natural ventilation and longitudinal tunnel ventilation. The Fire Dynamics Simulator (FDS) was used, and the combined peak heat release rate (HRR) of the vehicles was found to exceed 76 MW. Air temperatures around the fire source exceeded 1100 °C, while temperatures above 950 °C were recorded at the tunnel ceiling. The simulations captured thermal behaviour, smoke propagation, and the accumulation of carbon dioxide (CO₂) and carbon monoxide (CO). Longitudinal ventilation was shown to reduce upstream smoke spread and help maintain tenable conditions for evacuation and emergency response. These findings raise critical safety concerns regarding EV transportation in tunnels and support improved decision-making for tunnel infrastructure design and emergency responders. Full article

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

Natural and human-made hazards are increasing due to global warming and human activities. Occupant evacuation in complex buildings remains challenging due to unfamiliar building layouts, communication failures, and unpredictable occupant behavior. Therefore, this study aims to explore how integrating digital technologies enhances emergency preparedness, supports occupant decision-making during evacuation, and improves occupants’ situational awareness. We conducted a PRISMA-guided systematic literature review across Scopus, IEEE Xplore, and ProQuest Discover, analyzing 31 high-quality journal articles relevant to the research. The focus was on integrating digital technologies to support occupant situational awareness and evacuation outcomes. This review explores the integration of Internet of Things (IoT), Building Information Modeling (BIM), Virtual Reality (VR) /Augmented Reality (AR), Artificial Intelligence (AI), and Digital Twins (DTs) for emergency preparedness, supporting real-world applications. This review highlights three research questions: (1) Evaluate how current digital technologies affect occupant emergency preparedness in buildings; (2) Identify the challenges that limit the effectiveness of digital technologies across key emergency preparedness stages; (3) Understand how digital technologies can support occupant emergency preparedness. The review compiles evidence and presents a conceptual framework to support the integration of digital technologies into occupant-focused emergency preparedness, providing practical guidance for the future direction of risk management research. Full article

This paper presents an optimization framework for determining safe and efficient evacuation paths in complex industrial facilities. The proposed approach models the evacuation process through a timed flow network that captures both the structural characteristics of the layout and the temporal evolution of emergency conditions. The formulation accommodates real-time updates, enabling dynamic re-routing when certain areas or connections become inaccessible. Computational experiments on large-scale instances demonstrate the scalability of the model and its ability to provide complete evacuation plans under increasing demand. The results confirm predictable relationships between population size, time horizon, and evacuation completion, supporting its use as a decision support tool for both strategic planning and operational response. Full article

We modeled water inrush propagation in mines and the optimization of evacuation routes. By constructing a water flow model, the propagation process of water flow through the tunnel network is simulated to explore branching, superposition, and water level changes. The model was constructed based on breadth-first search (BFS) and a time-stepping algorithm. Furthermore, by integrating Dijkstra’s algorithm with a spatio-temporal expanded graph, miners’ evacuation routes were planned, optimizing travel time and water level risk. In scenarios with multiple water inrush points, we developed a multi-source asynchronous model that enhances route safety and real-time performance, enabling efficient emergency response during mine water disasters. For Problem 1 defined in this study, a graph structure and BFS algorithm were used to calculate the filling time of tunnels at a single water inrush point. For Problem 2, we combined the water propagation model with dynamic evacuation route planning, realizing dynamic escape via a spatio-temporal state network and Dijkstra’s algorithm. For Problem 3, we constructed a multi-source asynchronous water inrush dynamic network model to determine the superposition and propagation of water flows from multiple inrush points. For Problem 4, we established a multi-objective evacuation route optimization model, utilizing a time-expanded graph and a dynamic Dijkstra’s algorithm to integrate travel time and water level risk for personalized evacuation decision-making. Full article

Improving the efficiency of stair evacuation plays a crucial role in emergency management, which may be shaped by pedestrians’ lane-selection behavior. However, most existing studies describe pedestrians’ lane-selection preferences during stair evacuation, while the mechanisms behind these preferences are not yet well understood. To solve this issue, a stair evacuation observation experiment and a questionnaire survey were carried out to investigate pedestrian stair lane-selection preferences. Based on 1793 pieces of experimental data and 397 questionnaires, it is found that (1) pedestrians in the middle lane are more inclined to proactively change lanes based on their personal preference when sufficient space is available. (2) The primary factors influencing pedestrians’ lane-selection preferences are perceived safety, shortest path, and behavioral habit. (3) As the distance to the wall increases, the preference for the wall-side lane gradually decreases. Notably, the rate of decline accelerates at first, then slows down as the wall becomes farther away. This study deeply deconstructs pedestrians’ stair lane-selection preferences which helps understand the interactions among pedestrians, between pedestrians and their surroundings. It offers a basis for the optimization of evacuation strategies, the design of emergency evacuation plans, and the calibration of evacuation simulation models. Full article

Recent projections by the Intergovernmental Panel on Climate Change indicate that global warming will turn permanent and further intensify the severity and frequency of extreme weather events (heat waves, rain, and intense droughts), with coastal regions being the most vulnerable to extreme events. Therefore, the risk of natural disasters and the associated regional impacts on water, food, energy, social, and health security represents one of the world’s greatest challenges of this century. However, conventional methodologies for monitoring these regions during extreme events are usually not available to managers and decision-makers with the necessary urgency. The aim of this study was to present a framework concept for assessing extreme flood event impacts in coastal zones using a suite of field data combined with numerical (hydrological, meteorological, and hydrodynamic) and computational (flooding) models in a virtual environment that provides a replica of a natural environment—the Patos Lagoon Digital Twin. The study case was the extreme flood event that occurred in the southernmost region of Brazil in May 2024, considered the largest flooding event in 125 years of data. The hydrodynamic model calculated the water levels around Rio Grande City (MAE ± 0.18 m). These results fed the flooding model, which projected the water over the digital elevation model of the city and produced predictions of flooding conditions on every street (ranging from a few centimeters up to 1.5 m) days before the flooding happened. The results were further customized to attend specific demands from the security forces and municipal civil defense, who evaluated the best alternatives for evacuation strategies and infrastructure safety during the May 2024 extreme flood event. Flood Safety Maps were also generated for all the terminals in the Port of Rio Grande, indicating that the terminals were 0.05 to 2.5 m above the flood level. Overall, this study contributes to a better understanding of the strengths of digital twin models in simulating the impacts of extreme flood events in coastal areas and provides valuable insights into the potential impacts of future climate change in coastal regions, particularly in southern Brazil. This knowledge is crucial for developing targeted strategies to increase regional resilience and sustainability, ensuring that adaptation measures are effectively tailored to anticipated climate impacts. Full article

After large-scale disasters, swift and robust humanitarian logistics are crucial to provide timely assistance to injured people and displaced individuals. This study proposes a bi-objective optimization model for humanitarian logistics network design to simultaneously consider the facility location-allocation decisions, along with the transportation operation issues under uncertainty. The framework addresses the needs of both severely and mildly injured casualties and homeless populations. A hybrid robust optimization approach is accordingly developed that incorporates scenario-based, box-type, and polyhedral uncertainty representations to handle the uncertainty of factors such as casualty volume, travel times, facility failures, and demands for resources. More recently, machine learning methods have been applied to classify casualties and displaced individuals with respect to their geographic distribution and severity, further improving demand estimates and operational efficacy. This study seeks to develop a data-driven and robust optimization framework for designing humanitarian logistics networks under uncertainty, enabling decision-makers and emergency planners to gain insights into enhancing casualty evacuation, medical treatment, and shelter allocation in disaster response operations. The case of the Kermanshah earthquake in Iran is used for assessing the applicability of the model. The computational experiments and comparative analyses conducted show that the developed model exhibits high efficiency and robustness. The results are useful for guiding disaster preparedness and strategic decisions in humanitarian logistics. Besides operational performance, the model optimizes sustainability in the area of emergency response based on cost efficiency and social fairness, as underlined by SDGs 3 and 11. Full article

The efficient and timely management of human evacuation during emergency events is an important area of research where the Internet of Things (IoT) can be of great value. Significant areas of application for optimum evacuation strategies include buildings, sports arenas, cultural venues, such as museums and concert halls, and ships that carry passengers, such as cruise ships. In many cases, the evacuation process is complicated by constraints on space and movement, such as corridors, staircases, and passageways, that can cause congestion and slow the evacuation process. In such circumstances, the Internet of Things (IoT) can be used to sense the presence of evacuees in different locations, to sense hazards and congestion, to assist in making decisions based on sensing to guide the evacuees dynamically in the most effective direction to limit or eliminate congestion and maximize safety, and notify to the passengers the directions they should take or whether they should stop and wait, through signaling with active IoT devices that can include voice and visual indications and signposts. This paper uses an analytical queueing network approach to analyze an emergency evacuation system, and suggests the use of the Pull Policy, which employs the IoT to direct evacuees in a manner that reduces downstream congestion by signalling them to move forward when the preceding evacuees exit the system. The IoT-based Pull Policy is analyzed using a realistic representation of evacuation from an existing commercial cruise ship, with a queueing network model that also allows for a computationally very efficient comparison of different routing rules with wide-ranging variations in speed parameters of each of the individual evacuees.Numerical examples are used to demonstrate its value for the timely evacuation of passengers within the confined space of a cruise ship. Full article