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This briefing will cover general and NSSB worker response actions for an evacuation. Operations Centers, first responders (e.g., LANL Haz-Mat), workers with unique Building Emergency Procedure (BEP) guidance (other than NSSB), etc. are outside the scope of this briefing.
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Football is culturally and economically important in the United States, and football stadiums are part of the country's critical infrastructure, thus receiving government protection against hazard events. In this project, an agent-based evacuation model was implemented to optimize evacuation time from The University of Southern Mississippi's M.M. Roberts Stadium (football) by accounting for evacuees’ age, gender, physical fitness, alcohol consumption, and prior experience with hazard events. The findings revealed that (i) the age and gender of an individual impact his/her locomotion speed and (ii) evacuation route choice is influenced by evacuees’ perception of its safety and effectiveness. The estimated evacuation times for all evacuees to exit only the stadium and the stadium plus the surrounding campus were 20.82 and 165.01 minutes, respectively. Both of these times were shorter than the evacuation times determined by models employing location-unspecific locomotion speeds. One-way analysis of variance revealed that there were statistically significant differences between use of location-specific and location-unspecific within-stadium evacuation times (p ≤ 0.001 with α = 0.05). These results suggest that using local data is vital to accurately estimate evacuation time.
No-notice emergency events refer to unpredictable disasters such as earthquakes, chemical spills, or terrorist attacks, where it is impracticable to forewarn the public about their occurrence and design evacuation plans for them. This calls for an in-depth investigation of people’s evacuation behavior and identifying the most influential factors in their evacuation planning process to develop policy-sensitive pre-disaster plans for such events. As a response to this need, this study investigates individuals’ evacuation decision behavior in the context of no-notice emergency events. Since it is highly expected that people will have heterogenous decision behavior in such situations, we first apply a clustering algorithm to classify them into three maximally homogeneous clusters. Then, cluster-specific multivariate ordered probit models are developed to estimate the likelihood of selecting any of the three options of ignoring the situation, seeking shelter at the place, and evacuating to a safe place. The model estimation results indicate that a wide spectrum of factors affect the evacuation decision including individuals’ socio-economic attributes, disaster characteristics, built-environment factors, and government evacuation order. Further, variations of estimated coefficients across the population clusters highlight the significant dissimilarities in decision behavior of their associated members.
Disastrous events have been drastically increasing – both in frequency and destructive capacity – over the past few years. While advance-notice events have received a great deal of attention in the literature of disaster management, not much attention so far has been given to the no-notice events mainly because of the scarcity of data. As an attempt to address this critical gap, the current study proposes a disaggregate evacuation demand framework to understand evacuees’ travel behavior in case of no-notice emergency events. Here, the proposed framework comprises four main steps of evacuation decision, evacuation planning, tour formation, and activity schedule update. This article is dedicated to the introduction of the framework structure and elaboration on the tour formation step. In this step, we first estimate the total number of intermediate stops, travel time, and distance of the evacuation tours for those who decide to evacuate through a joint modeling structure and then, determine the type of each intermediate stop (if any). It is found that a broad range of factors including evacuees’ demographic profiles, built-environment attributes, and characteristics of the disastrous event plays a significant role in people’s evacuation behavior during no-notice emergency events. The findings of this study can assist responsible agencies in understanding evacuees’ complex behavior, and consequently, in devising effective strategies to alleviate economic damages and casualties resulted by such events.
To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.
Response strategies implemented in the first few hours to days after a nuclear detonation on the US homeland may save more than 100,000 lives. Effective planning and pre-event capability development can increase the response efficacy and so the number of lives saved. During such an event, the US Department of Defense, Joint Task Force Civil Support (DoD JTF-CS) provides command and control for the DoD forces supporting civil authority response operations. Among other activities, JTF-CS expects to support FEMA as well as State and local agencies in shelter and evacuation response activities. JTF-CS currently uses a manual method to generate evacuation decision making products. This method compares the dose that would be (a) acquired remaining at a given location to that (b) acquired during evacuation along a small number of routes. In these products, evacuation may be warranted when the projected “remain” dose is greater than the “evacuate” dose. This method only provides a limited consideration of the protection buildings provide their occupants. To inform the JTF-CS nuclear detonation response, the DoD Defense Threat Reduction Agency (DTRA) has tasked Lawrence Livermore National Laboratory (LLNL) to design a set of planning and response products to support shelter-evacuation activities. This report documents these products to facilitate DTRA and JTF-CS planning efforts. To provide context, we also provide appendices that summarize US planning guidance response zones, early (<72 h) response strategies, and other key topics.
Insulation materials with a thermal resistance per inch higher than 6h∙ft 2 ∙°F/Btu∙in or R6/in (42m∙K/W) are needed for building envelope retrofits in which solutions with slim profiles are required because of minimal available space or because real estate has a premium cost. These insulation materials will also be of benefit to prefabricated construction given that slim designs either reduce shipping costs or increase the floor space provided to the customer. Vacuum insulation panels (VIPs) are the current state-of-the-art insulation material with a resistance of about R35/in (243m∙K/W); however, their use in buildings is hindered by the fact that damages to their protective film decrease their performance to ≤R8/in (56m∙K/W). Given the lack of robust, highly-insulating materials, we are exploring methods to develop evacuated spheres that can act as a closed-cell vacuum insulation. The evacuated spheres could be assembled into boards that could attain ~R14/in (97m∙K/W). Advantages that evacuated spheres will offer over VIPs are that they will likely be more suitable for construction because punctures will only cause localized damage, and that the boards could be cut into customized shapes. We are currently investigating two processes to evacuate spheres to produce polymeric vacuum insulation spheres (PVISs) and coated and evacuated insulation spheres (CEISs). PVISs involve the extrusion of economical polymers, blowing agents, and gas barriers. CEISs use naturally-occurring or synthesized hollow particles with porous shells that are evacuated and coated with a gas impermeable thin film. Both manufacturing processes aim for very large throughputs to ensure cost-effectiveness and adoption by the construction industry. This paper discusses the manufacturing techniques that we are exploring and our progress to date.
Transparent and insulating materials having evacuated capsules are provided. According to an aspect of the invention, a method includes forming evacuated capsules within a solution, and dispersing and suspending the evacuated capsules within the solution such that a packing density of the evacuated capsules within the solution is greater than 30%, and a visible light transmission of the solution including the evacuated capsules is greater than 75%. According to another aspect of the invention, a layer includes a plurality of evacuated capsules distributed within a dried sol-gel. A thermal conductivity of the layer is between 0.02 W/m-K and 0.001 W/m-K, and the layer has a visible light transmission of greater than 30%.
The electric vehicle (EV) market has significantly expanded because EVs have lower operational costs while leaving less environmental footprint than internal combustion engine vehicles. However, EVs also come with drawbacks, including long charging time and short operational ranges. With these drawbacks and limited charging facilities, efficient long-distance EV evacuation management is challenging and has not been properly addressed. Without an efficient evacuation plan, serious congestion could happen at charging facilities, the evacuation process would be excessively long, and thus human lives may be put at risk. This study is motivated to investigate the optimal mass evacuation planning for EVs considering limited charging facilities. A three-stage method is proposed to efficiently approach this problem. A case study of Florida hurricane evacuation is conducted. Here, the method's effectiveness is verified by comparing it with a benchmark. Management insights and policy indications are drawn through sensitivity analysis of key parameters.
The evacuated tube collector with U shape copper absorber tube is considered for the analysis. The experimental investigation is conducted on parabolic trough collector with U shape tube as absorber tube. The effect of the sudden fluctuations in the solar radiation on the performance of the collector is reduced by means of evacuated tube collector filled with thermic fluids. The analysis is performed with different thermic fluids such as dowtherm, therminol66, glycol water and ethylene glycol, are filled in the annular space between inner glass tube and U shape copper absorber tube. The experimentation is carried out at various mass flow rates from 20 to 100 LPH with the step-up flow rate of 20 LPH. A comparative study is carried out on various parameters such as effect of mass flow rate over instantaneous efficiency, useful heat gain and work input, etc. The characteristic curve of cylindrical parabolic trough collector (PTC) is also discussed. Experimental results show that, ethylene glycol gives better efficiency over mass flow rate and therminol66 gives best power heat ratio. Heat transfer mediums and its properties [specific heat capacity, thermal conductivity and dynamic viscosity] for all specified heat transfer fluids are also discussed. The results obtained with various specified heat transfer fluids filled in the annulus space of evacuated tube are compared with plain evacuated tube. It is observed that there is significant enhancement of overall instantaneous collection efficiency of the parabolic trough collector.
In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.
On April 1, 1946, an earthquake off Unimak Island, Alaska, generated a tsunami that raced across the Pacific. One of the islands in danger was Kwajalein, the logistical hub for Operation Crossroads. Commodore Ben Wyatt, the U.S. naval commander of the Marshall Islands, ordered the evacuation of “10 Red Cross workers, 19 USO women, 17 native women, 16 male [hospital] patients, four tiny babies, one small boy, and two navy doctors,” to the USS Rockbridge, which then steamed in deep water off the atoll until the danger passed. One of the Red Cross workers, Reva Hurwitz, played bridge in the ship’s wardroom until 2 a.m., a luxury since the island curfew, strictly enforced, was 10:30 pm. The tsunami bypassed Kwajalein, and everyone returned to the island less than twenty-four hours after being evacuated. Although the evacuation proved unnecessary, the women were grateful, said Hurwitz, because they had the opportunity “to use hot water and eat fresh fruit.”
There is substantial interest in understanding the ecological impacts of the nuclear accidents at the Chernobyl and Fukushima Daiichi nuclear power plants. However, population‐level data for large mammals have been limited, and there remains much speculation regarding the status of wildlife species in these areas. Using a network of remote cameras placed along a gradient of radiological contamination and human presence, we collected data on population‐level impacts to wildlife (that is, abundance and occupancy patterns) following the 2011 Fukushima Daiichi nuclear accident. We found no evidence of population‐level impacts in mid‐ to large‐sized mammals or gallinaceous birds, and show several species were most abundant in human‐evacuated areas, despite the presence of radiological contamination. These data provide unique evidence of the natural rewilding of the Fukushima landscape following human abandonment, and suggest that if any effects of radiological exposure in mid‐ to large‐sized mammals in the Fukushima Exclusion Zone exist, they occur at individual or molecular scales, and do not appear to manifest in population‐level responses.
This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.
Augmented Reality is a promising technology to support safe pedestrian evacuation from buildings during earthquakes. The widespread availability of phone cameras makes this idea feasible. QR codes, for instance, can place virtual three-dimensional objects to guide people along a prescribed route. In this work, a prototypical system based on this premise was created in response to the 2020 sudden surge in seismic activity in Puerto Rico. The results point towards feasibility as well as to a series of areas that require improvement for a real-life implementation.