Real-time Evacuation Research Articles

Information retrieval and classification of real-time multi-source hurricane evacuation notices

In the United States, tracking time-sensitive critical information such as evacuation notices for approaching natural hazards like hurricanes poses a significant challenge. This difficulty arises from the rapid issuing and distribution of these notices by numerous local authorities that may span multiple states. Additionally, evacuation notices often undergo frequent updates, and they are distributed through various online portals lacking standard formats. Unlike weather warnings, there is no centralized database for live (or past) evacuation notices. To meet the demand of data for crisis-related government notices, we developed an approach to detect and retrieve locally issued hurricane evacuation notices in real time. Evacuation-related text data were collected mainly through spatially targeted information retrieval. These data were then manually labeled and used to train natural language processing (NLP) models. The NLP models in turn classified all text data into one of the three notice categories, i.e., mandatory evacuation notices, voluntary evacuation notices, and not an evacuation notice. The classification of mandatory evacuation notices achieved a very high accuracy (recall = 96 %). These NLP models, when applied to future hurricanes, will provide real-time evacuation notices for situation awareness to higher-level government agencies and news outlets. The archived evacuation notices serve as a valuable resource for scholars to study government responses to weather warnings and individual behaviors influenced by evacuation history. Our research framework may extend to other disaster types, allowing for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.

Coupled simulation-optimization model for pedestrian evacuation guidance planning

Effective evacuation guidance can guarantee people's safety by facilitating their swiftly exit hazardous areas during an emergency. However, pre-determined guidance plans based solely on distance comparisons to exits may not always be the most effective due to unstable accessibility conditions and uneven crowd distribution. Therefore, it is imperative to incorporate real-time optimal guidance information in the plan. Coupling simplified CTM (Cell Transmission Model)-based simulation, this study proposed a computationally efficient DRF (Directed Rooted Forest)-encoded planning for developing evacuation guidance plan. Taking them as a holistic model, the simulator predicts evacuation dynamics at a constant computational cost regardless of crowd size, while the planning module optimizes the guidance plan directionally by leveraging the simulation's intermediate and final outputs. Numerical tests have demonstrated that the tight coupling between optimization and simulation module has substantially enhanced the model's capacity to fine-tune the guidance plan and optimization efficiency. The proposed model may serve as the foundation for developing real-time evacuation guidance plans for large-scale crowded buildings, either on the premise of accelerated simulation or as an efficient generator of training data for machine learning models.

A real-time automatic fire emergency evacuation route selection model based on decision-making processes of pedestrians

After a fire occurs, it is imperative that people in danger evacuate as soon as possible. However, the current emergency plan based on the pre-established static exiting route is unable to considering the real-time fire scenario. In addition, the selection of evacuation routes significantly relies on the decision-maker's experiences. These issues seriously affect evacuation efficiency, decreasing the likelihood of survival. This paper developed an effective real-time evacuation guidance method that can automatically select the evacuation route in accordance with real-time fire scenarios. The model is established based on the on-policy learning algorithm SARSA (State–action–reward–state–action), an algorithm for learning a Markov decision process policy, which could mimic the decision-making of pedestrian behaviors in an emergency. In addition, two types of radar (exit radar and fire radar) are introduced into the SARSA algorithm to facilitate the wayfinding process, which formulated the so-called Radar-assisted SARSA (RSARSA). The results have shown that RSARSA can swiftly decide a safer evacuation route for pedestrians or crowd at arbitrary location. The convergence time of initial successful route planning is between 0.05 and 4.5 s under the tests in this paper. The evacuation route determined by this algorithm can well consider the fire, and timely avoid routes with potential dangerous. Moreover, RSARSA can flexibly respond to different fires under various heat release rates and development speeds. By applying this technology, fire evacuation can be guided by routes that are more attuned to the mindset of pedestrians. It can provide a good basis for route selection of crowd evacuation.

Real-time evacuation route optimization in the fire scenarios of cruise ships

The optimization of evacuation routes for evacuees in fire scenarios on cruise ships directly affects evacuation efficiency. A time-varying equivalent weight Dijkstra algorithm (TVEWDA) is proposed, combining a fire dynamics simulator (FDS) to find optimal evacuation routes in case of a sudden cruise ship fire. First, the FDS software is used to simulate a cruise ship's single and multi-source fire scenarios. Second, the Dijkstra algorithm using only distance between nodes is improved by considering the effects of CO concentration, visibility, and temperature. The evacuees’ speed is taken into consideration within the TVEWDA. The effects of congestion are considered in the evacuees’ moving speed. Finally, the TVEWDA and a cruise ship evacuation network model are integrated to develop a real-time evacuation route optimization model. Simulation results show that the evacuation time achieved by the TVEWDA is 22% shorter than that of the Dijkstra algorithm using only distance between nodes and 11% shorter than that of an improved social force model in the multi-source fire scenario. This approach is well-suited to guide the evacuation of cruise ship evacuees in the event of single-source or multi-source fires, effectively ensuring the safety of passengers and the preservation of their property.

Evacuation Management System for Major Disasters

Predicting and understanding mass evacuations are important factors in disaster management and response. Current modelling approaches are useful for planning but lack of real-time capabilities to help informed decisions as the disaster event evolves. To address this challenge, a real-time Evacuation Management System (EMS) is proposed here, following a stochastic approach and combining classical models of low complexity but high reliability. The EMS computes optimal assembly points and shelters and the related network of evacuation routes using GIS-based traffic, pedestrian and routing models including damaged assets or impassable areas. To test the proper operation performances of the EMS, we conducted a case study for the Gran Canaria wildfire (August 2019—Spain).

Extreme climate and tectonic controls on the generation of a large-scale, low-frequency debris flow

Low-frequency heavy rainfall events commonly trigger debris flows. However, it is not currently understood how high-frequency, light rainfall can trigger giant catastrophic debris flow events with return periods of greater than 100 years; for example, the 2014 event in the Erkazi gully (EKZG), Danba County, Sichuan Province, China. This study uses the EKZG debris flow as a case study to determine the main factors controlling the formation of low-frequency debris flows and their generation by high-frequency light rainfall through field investigations, indoor and outdoor experiments, simulated calculations, and statistical analysis. We revealed that the soil mass predominantly controlled the generation of the EKZG debris flow. Moreover, internal geological factors such as earthquakes and external geological factors such as dry-wet cycles and freeze-thaw erosion likely dominated the EKZG debris flow formation process. In contrast, the geomorphological characteristics, basaltic columnar jointing, and earthquakes jointly determined the amplification process of the debris flow. Furthermore, we discussed the critical role of community-based warning systems during debris flow disasters. For example, early identification of the blocking characteristics of the EKZG debris flow by observers enabled the successful real-time evacuation of 651 people. The following recommendations are proposed: (1) future research should focus on the dynamic replenishment process of the soil mass through dry-wet and freeze-thaw cycles, and (2) the effectiveness and applicability of community-based warning systems should be improved for the prevention and mitigation of geological hazards.

Largest scale successful real-time evacuation after the Wenchuan earthquake in China: lessons learned from the Zengda gully giant debris flow disaster

A catastrophic debris flow occurred in the Zengda gully, a branch of the Dajinchuan River in Zengda town, Jinchuan County, Sichuan Province, China. The successful implementation of a real-time evacuation avoided 820 casualties for people living in 200 settlements. This was the largest-scale successful real-time evacuation for a debris flow disaster after the Wenchuan earthquake in China. In order to better reveal the causes of the successful real-time evacuation process of the giant debris flow disaster, the characteristics, formation and movement process of the debris flow were studied using multi-temporal remote sensing images, field investigation, laboratory analysis, and empirical formula calculations. It was found that the successful real-time evacuation was possible because of a well-executed monitoring system, timely release of early warning information, a highly effective operation disaster prevention system, and decisive and advanced avoidance. It also transferred strategies through in-depth analysis of several important stages in the real-time evacuation process. Finally, an exemplary mode of community-based warning is proposed based on the Zengda gully giant debris flow disaster real-time evacuation. Specifically, the mode was led by government, implemented by local residents and proceeded with the guidance by experts in the field. The experience and effective risk avoidance mode presented in this paper can be shared and employed by other countries or regions at serious risk for debris flow disasters.

Development of a systematic approach and mathematical support for the evacuation process

In modern conditions, due to the vastness of the territory of Kazakhstan, with a certain probability, natural disasters such as earthquakes, floods, avalanches, as well as accidents, destruction of buildings, epidemics, release of chemical toxic substances at industrial enterprises, fires in educational and medical institutions are possible, which justifies the relevance of modern methods and technologies for solving the problem of evacuation. The peculiarity of this work lies in the formation of an integrated approach for organizing the evacuation process both in peacetime as training for the event of an emergency situation (emergency), and in the event of the emergency itself. A conceptual diagram of an evacuation system is proposed that uses heterogeneous sources for receiving and transmitting information about the onset of an emergency. The input and output sources for receiving and transmitting information about the number of people in the building are determined. The main purpose of the system is to form an operational real-time evacuation plan. This work is the result of a phased implementation of an integrated evacuation system, which consists in building a mathematical model and a method for solving the problem of maximum flow in the network. A mathematical model has been developed for the optimal flow distribution along the Grindshiels network with the analysis of the flow formation and the characteristics of people’s motion in enclosed spaces. A game-theoretic approach and mathematical methods of the theory of hydraulic networks for finding an equilibrium state in flow-distribution networks have been developed. An algorithm for solving the evacuation problem using the graph approach is proposed. The results of this paper make it possible to systematically organize training evacuations, prepare resources, train the personnel responsible for evacuation in order to quickly respond in an emergency and carry out the evacuation process in order to avoid major consequences.

Dynamic network flow optimization for real-time evacuation reroute planning under multiple road disruptions

During the course of an evacuation, evacuees often encounter unexpected incidents interrupting their plans for evacuation. Roads may not be accessible due to flooding, wild-fire propagation, accidents, the collapse of highway structures, and various other reasons. The evolving disturbances to the evacuation plan due to road disruptions may prolong the evacuation process and lead to chaos, injuries, and loss of life unless a quick, efficient recovery plan is implemented. In this work, we aim to provide a rerouting approach for an evacuation network that undergoes road disruptions. Unlike previous studies, it is assumed that incidents can occur on multiple roads and that the time of each occurrence can differ from the time of other occurrences. Flow optimization techniques are used to represent evacuation traffic flow on the transportation network. A dynamic traffic flow rate is considered in which the evacuation flow rate can change over time during the planning horizon. The variation in the flow rates enables a better projection of the traffic dynamics and consequences caused by disturbances. Furthermore, a path-based dynamic network flow optimization formulation is proposed to make the model scalable for large evacuation networks. Two preprocessing algorithms are introduced to calculate specific parameters associated with road disruptions and topology of the evacuation network. The use of these parameters enables us to transform the original optimization model into a linear model to reduce the computational burden. Numerical experiments are made to show the performance of the proposed model. Furthermore, the effects of specific features such as disruption time, disturbance location, and the plan updating time on the evacuation process are investigated. Results indicate that when more incidents occur later or when incident information is received earlier, the magnitude of the rerouting completion time is lessened.

A Study on Advancing Strategy of Seismic Tremors Solution For Busan using Open API

Background/Objectives: Among the earthquake damage analysis systems developed so far, no system that could even respond from the perspective of managers in the event of a disaster.
 Methods/Statistical analysis: It was intended to ensure the convenience of managers by adding additional functions to the type of seismic damage analysis system. The existing system predicted and calculated the damage caused by the earthquake. In this paper, we present a way to help respond quickly after an earthquake by using Open API, etc. It includes functions that are already being implemented, as well as those that are being researched and developed.
 Findings: The existing seismic damage analysis system mostly calculates building damage caused by earthquakes, evaluates the amount of damage based on those figures, and produces figures related to refugees. But when an actual earthquake occurs, there is not much that managers can do right away with the system. This paper presents a strategic improvement plan by adding four additional functions to overcome the shortcomings of the existing system. This improvement plan is based on 3D Geographic Information System (GIS) and was intended to add Earthquake Excellence History, Vulnerable Groups Concentrated Area Information, Standard Operating Procedures, and Real-Time Evaluation Route. Earthquake Accuracy History function allows an intuitive decision on past similar records to be made. Vulnerable Groups Concentrated Area Information function helps you make quick judgments about the situation in the region where you need help first and foremost. Standard Operating Procedures are built into the system so that non-skilled people can easily perform it. Real-Time Evacuation Route feature allows administrators to have a quick insight into the path of refugees.
 Improvements/Applications: The improvement plan with additional functions in the system for calculating the output related to damage can be further improved through further implementation.

Ultrasound real-time evacuation of ICHs: an italian two centers consecutive experience

Ultrasound real-time evacuation of ICHs: an italian two centers consecutive experience

Time-Aware and Temperature-Aware Fire Evacuation Path Algorithm in IoT-Enabled Multi-Story Multi-Exit Buildings.

Temperature sensors with a communication capability can help monitor and report temperature values to a control station, which enables dynamic and real-time evacuation paths in fire emergencies. As compared to traditional approaches that identify a one-shot fire evacuation path, in this paper, we develop an intelligent algorithm that can identify time-aware and temperature-aware fire evacuation paths by considering temperature changes at different time slots in multi-story and multi-exit buildings. We first propose a method that can map three-dimensional multi-story multi-exit buildings into a two-dimensional graph. Then, a mathematical optimization model is proposed to capture this time-aware and temperature-aware evacuation path problem in multi-story multi-exit buildings. Six fire evacuation algorithms (BFS, SP, DBFS, TABFS, TASP and TADBFS) are proposed to identify the efficient evacuation path. The first three algorithms that do not address human temperature limit constraints can be used by rescue robots or firemen with fire-proof suits. The last three algorithms that address human temperature limit constraints can be used by evacuees in terms of total time slots and total temperature on the evacuation path. In the computational experiments, the open space building and the Taipei 101 Shopping Mall are all tested to verify the solution quality of these six algorithms. From the computational results, TABFS, TASP and TADBF identify almost the same evacuation path in open space building and the Taipei 101 Shopping Mall. BFS, SP DBFS can locate marginally better results in terms of evacuation time and total temperature on the evacuation path. When considering evacuating a group of evacuees, the computational time of the evacuation algorithm is very important in a time-limited evacuation process. Considering the extreme case of seven fires in eight emergency exits in the Taipei 101 Shopping Mall, the golden window for evacuation is 15 time slots. Only TABFS and TADBFS are applicable to evacuate 1200 people in the Taipei 101 Shopping Mall when one time slot is setting as one minute. The computational results show that the capacity limit for the Taipei 101 Shopping Mall is 800 people in the extreme case of seven fires. In this case, when the number of people in the building is less than 700, TADBFS should be adopted. When the number of people in the building is greater than 700, TABFS can evacuate more people than TADBFS. Besides identifying an efficient evacuation path, another significant contribution of this paper is to identify the best sensor density deployment at large buildings like the Taipei 101 Shopping Mall in considering the fire evacuation.

Real-Time Evacuation Strategy Based on Cell-Inspired Simulation Model.

During the evacuation process in an emergency, the route conditions usually change instantaneously, which makes routing selection a challenging work. To describe dynamic changes in the evacuation environment, this work proposes a real-time evacuation strategy based on a comprehensive route constraint in the framework of a cell-inspired simulation model, intelligence decision P system (IDPS). In our model, the comprehensive route constraint is formed to describe more complex road condition information including the total distance, congestion state, and unreliability of the route. Meanwhile, the IDPS model has been further improved in both the knowledge base description and the decision-making ability. Experiments are conducted to simulate five different scenarios in a fire evacuation. The results show that the evacuation strategy with a comprehensive route constraint has a significant improvement in the evacuation efficiency and has higher robustness. Which kind of route condition should be emphasized more in different evacuation scenarios is also analyzed.

EvacuSafe: A real-time model for building evacuation based on Dijkstra's algorithm

Building evacuation strategies directly affect the success of an emergency response plan in reaction to a disastrous event in a building. Evacuation alternatives result from applying time delays or altering path planning in the evacuation strategy. The best strategy for evacuation depends on the characteristics of the building, circumstances of the particular emergency, and reactions of the evacuees during the emergency. A real-time evacuation management model that identifies the fire hazard, contemplates possibilities, foresees consequences, and eventually proposes the best strategy of evacuation during the emergency can reduce the risk of a failed evacuation. In this paper, a real-time model is proposed, whose main goal is to discover the safest strategy of path-planning at every instant of time during an evacuation. The model focuses on fire emergencies, as they are the dominant cause of fatalities in buildings compared to other types of natural and manmade disasters. The proposed model first defines a risk factor for each compartment based on the location of the fire and any potential blockages caused by structural collapse or door malfunction. The lowest risk path is then calculated based on a modified Dijkstra's algorithm. The model is developed and implemented such that it conforms to the Active Dynamic Signage System (ADSS). Herein, a model is designed to monitor the building in real-time and in case of any unexpected event, changes to the evacuation strategy are communicated to occupants using ADSS. The case study shows that the proposed model for real-time evacuation management can significantly enhance the safety level of evacuation compared to the conventional shortest path evacuation plan.

A decision-support framework for emergency evacuation planning during extreme storm events

Developing an effective real-time evacuation strategy during extreme storm events such as hurricanes has been a topic of critical significance to the emergency planning and response community. The spatial and temporal variabilities of inland flooding during hurricanes present significant challenges for robust evacuation planning. In this study, a framework for real-time evacuation planning was developed that combines the results obtained from hydrodynamic modeling and traffic microsimulation. First, a fine-scale hydrodynamic model was developed based on depth-averaged 2D shallow-water equations (SWE) to obtain information pertaining to flood depth and velocity for planning evacuation routes during a storm event. Next, a traffic microsimulation was conducted using time-dependent information from the hydrodynamic model regarding the traffic velocities along evacuation routes during an event. An optimization technique was also implemented to reduce the overall travel time by about 6% from that of the base model. The last component of the framework involves combining the results from both models to generate a time-lapse animation of emergency evacuation based on a geographic information system (GIS). The results obtained using this framework could be easily accessed by the general public and decision-makers to enable efficient evacuation planning during extreme storm events.

Real-time evacuation and failure mechanism of a giant soil landslide on 19 July 2018 in Yanyuan County, Sichuan Province, China

A giant soil landslide named Boli landslide occurred on the right bank of Taozi Gully, a branch of the Jiami River in Taozi town, Sichuan Province, China, at approximately 5:40 am local time on 19 July 2018. The landslide destroyed 186 houses and nearly 300 acres of agricultural land in Boli village. Real-time evacuation was successfully implemented to avoid casualties of 281 people among 65 settlements due to the effective warning. This study revealed the typical characteristics, the cause, and the failure mechanism of Boli landslide on the basis of multi-temporal remote sensing images, field investigation, and analysis of the landslide soils. A simple, effective, and scalable community-based early warning system was also proposed in the study on the basis of real-time evacuation in response to landslide hazards in mountain in near future. Moreover, the experience and the effective risk avoidance mode of this study can be used by the concern parties to improve the disaster prevention and countermeasures of poor mountainous areas.

Collaborative Virtual Reality Environment for a Real-time Emergency Evacuation of a Nightclub Disaster

Collaborative Virtual Reality Environment for a Real-time Emergency Evacuation of a Nightclub Disaster

Traffic Modeling for Wildland–Urban Interface Fire Evacuation

Several traffic modeling tools are currently available for evacuation planning and real-time decision support during emergencies. This paper reviews potential traffic-modeling approaches in the context of wildland-urban interface (WUI) fire-evacuation applications. Existing modeling approaches and features are evaluated pertaining to fire-related, spatial, and demographic factors; intended application (planning or decision support); and temporal issues. This systematic review shows the importance of the following modeling approaches: dynamic modeling structures, considering behavioral variability and route choice; activity-based models for short-notice evacuation planning; and macroscopic traffic simulation for real-time evacuation management. Subsequently, the modeling features of 22 traffic models and applications currently available in practice and the literature are reviewed and matched with the benchmark features identified for WUI fire applications. Based on this review analysis, recommendations are made for developing traffic models specifically applicable to WUI fire evacuation, including possible integrations with wildfire and pedestrian models.

Optimization of Evacuation Route considering Uncertainty of Undetected Fires in Real-Time Evacuation Guidance under Post-Earthquake Fires

Optimization of Evacuation Route considering Uncertainty of Undetected Fires in Real-Time Evacuation Guidance under Post-Earthquake Fires

Performance modeling of an intelligent emergency evacuation system in buildings on accidental fire occurrence

Construction of multi-floor buildings have increased multifold in the urban areas due to scarcity of space for the growing population. There is a need for safe evacuation of people in such multi floor buildings during emergency situations such as fire occurrence. In this paper, we present an analytical model of the evacuation scenario on accidental fire using stochastic petri nets. The proposed evacuation model takes into account human behavior parameters such as panic among occupants, and realistic situations namely stampede in the stairwell, and distance of each occupant to the floor exits. The performance of the system is evaluated in terms of evacuation time, pre evacuation delay and average system throughput and compared with those of the evacuation model without human behavior and realistic constraints. The analytical results are validated using the real time evacuation drill measurements in a 10 storey building.