Optimal Evacuation Route Research Articles

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.

СТРУКТУРНАЯ СХЕМА СИСТЕМЫ ИНТЕЛЛЕКТУАЛЬНОГО УПРАВЛЕНИЯ ЭВАКУАЦИЕЙ

In an emergency, such as a fire or a terrorist attack, an important aspect is the safe evacuation of people. The unpredictability of the situation makes the task of emergency evacuation one of the main problems. The development of artificial intelligence technologies opens up possible promising ways of developing evacuation management systems. An intelligent evacuation management system can help track the movement and coordinates of people and related critical factors during the evacuation process (for example, slowing down traffic along the paths, blocking the paths, etc.). The study is devoted to the construction of a decision support system for the evacuation of people from buildings based on the results of modeling the development of a fire, intelligent forecasting of the evacuation time in the conditions of the current situation during a fire. The system allows dynamically forming optimal evacuation routes in a changing situation. The proposed results allow us to further move on to the development of an automated intelligent evacuation management system, which will ensure increased safety for evacuees, regardless of evacuation scenarios and the current situation in the building.

Проектирование интеллектуальной противопожарной системы

Fire protection systems utilize detectors that process signals from fire sensors using threshold-based methods and gen-erate a fire signal based on a logical function. Artificial neural networks can enhance these detectors by processing in-formation from a network of sensors after being trained. To train these neural networks, extensive data sets are neces-sary, which can be obtained through fire simulations on a supercomputer. Field tests are costly, subject to random fac-tors, limited to one or two rooms, and do not provide a comprehensive picture of fire development. Thus, designing intelligent fire systems falls under model-based design. Through modeling, large data sets were generated for training fire system algorithms, expanding the range of tasks they can address. A group of neural networks is proposed for op-timizing the placement of multi-parameter sensors, identifying the type of burning material, detecting fires at early stages, and localizing the fire zone to select appropriate extinguishing agents. Artificial neural networks enable the pre-diction of fire development, mapping hazardous factors' distribution to find optimal evacuation routes. An example of model-based design for a ship fire protection system is provided.

Evacuation information methodology that combined a flooded environment and pedestrian behavioral model

Disaster information influences decision-making. Wireless internet and location-based services, aided by recent technological advances, have laid the foundation for providing personalized disaster information. Despite the provision of simple disaster forecasts and warnings, provision of specific evacuation actions (“where” and “how,” i.e., location and reachability of safe areas) and location-based personalized information that consider real-time disaster situations are a pressing priority. Thus, this study proposed and developed a three-step methodology for providing disaster information during floods that combined a flooded environment and pedestrian behavioral model, namely, 1) prediction of the flooded environment, 2) analysis of evacuation routes based on the movement time of evacuees according to the flood depth, and 3) provision of evacuation information by selecting the optimal evacuation route. This model was demonstrated in the Gangnam Station area in Seoul, Korea. Flooded environment prediction modeling of the target area was performed by applying the 100-year rainfall design to the Storm Water Management Model. The optimal evacuation route, according to the flood depth, was selected by applying the behavioral model (movement time calculation formula) for each evacuee derived through an evacuation behavior experiment. The pre- and post-flooding age- and gender-wise total evacuation distance and time were compared and analyzed. Post-flooding, the evacuation distance and time increased by 168.3% (381.34 m) and 172.3% (3.16 min) on average, respectively, indicating that evacuees must travel farther and longer. The analysis of post-flooding evacuation route change types revealed changes in evacuation distance and time in cases where shelters and evacuation routes and time were significantly different depending on evacuee characteristics (age and gender). This indicated that specific and visual disaster information can be provided according to evacuees’ age and gender using the proposed methodology. This study can serve as the basis for designing an advanced and personalized disaster information system that can provide specific evacuation actions to aid fast evacuation decision-making during floods.

Comparative analysis of Bellman-Ford and Dijkstra’s algorithms for optimal evacuation route planning in multi-floor buildings

Comparative analysis of Bellman-Ford and Dijkstra’s algorithms for optimal evacuation route planning in multi-floor buildings

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

Safety in nuclear energy utilization is crucial. In the event of a radioactive release incident, coupled with meteorological uncertainties, a radioactive plume can impact personnel evacuation. This paper presents an integrated solution for radionuclide release accident assessment and emergency evacuation decision making. The solution consists of three processes: atmospheric dispersion calculation, dose calculation, and path planning. The individual processes are connected through data exchange, thus allowing users to choose specific models based on experience. The proposed scheme combination is the Gaussian plume model, the dose conversion factor method, and an improved Dijkstra’s path planning algorithm. This algorithm, combined with dispersion and dose results, weighs nodes using the moving expected dose, facilitating the path with minimum dose risk. A program for Atmospheric Diffusion and Dose Calculation (ADDC) is developed based on the recommended scheme. Advantages include ease of use, minimal data requirements, data accessibility, and efficient evacuation. Dose estimates and optimal evacuation routes can be obtained quickly and at very low cost in response to rapidly changing environmental conditions. In a case study at a Chinese planned nuclear plant, we consider a spent fuel pool water loss scenario, assessing dose risks from 2020 to 2022 meteorological statistics. In dose calculation, results reveal that during an SFP drying accident, the radiation dose in the core area (100 m away) can reach 30–150 mSv within 2 h, and at 500 m away, it can reach 5–15 mSv. The dose in all downwind directions can drop below 250 mSv within 60 m. In path planning, results reveal the program is capable of accurately and efficiently calculating the minimum dose evacuation route. The program’s path reduces the effective dose by up to 67.3% compared to the shortest route, enhancino safety, and guiding post-accident decision making and planning.

Network analysis for determining the fastest evacuation routes in flood-prone areas of the Tuntang Watershed, Indonesia

Flooding in the Tuntang Watershed, Central Java Province, is a hydrological disaster that occurs regularly. Several factors, such as high rainfall, limited water catchment areas, and narrowing riverbanks, cause flooding. The amount of material and non-material losses can be minimized through an effective disaster mitigation process. One way is to determine and establish flood evacuation routes to shelters. This research aims to analyze the fastest and most effective evacuation route for residents using Geographic Information Systems. This research process includes analyzing flood-prone areas, ranging from highly vulnerable areas to areas that are not vulnerable. Furthermore, it analyzes population density, distribution of residential areas, natural physical conditions, and road conditions and identifies the location of the best evacuation center for residents affected by flooding. The method used is Sample Additive Weighting (SAW) to determine the best evacuation point and Network Analysis Fastest Route to determine the optimal evacuation route to the designated evacuation center. The results of this study include modeling a flood evacuation route with an estimated effective evacuation time of 30-45 minutes, consisting of 50 fastest alternative routes to appropriate shelters.

Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

Background: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that the topic has been addressed in the literature, it is necessary to have quick response methods that can be used by decision-makers to adapt and utilize existing spaces as temporary shelters, in addition to knowing how to evacuate people. Methods: Addressing this void, a methodology to minimize evacuation and aid distribution costs is introduced. Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. Results: The research identifies optimal evacuation routes from 13 affected areas to 34 shelters and analyzes aid distribution costs under various demand scenarios: original, increased, and decreased by 10%, based on the number of transport units allocated and Google Maps distances. It also evaluates the costs associated with humanitarian aid distribution under varying collection strategies, involving state and municipal governments. Conclusion: This approach provides a decision-making foundation and can be adapted for similar analyses in other communities during extreme events.

GIS‐Based Identification and Analysis of Optimal Evacuation Areas and Routes in Flood‐Prone Zones of Swabi District, Pakistan

GIS‐Based Identification and Analysis of Optimal Evacuation Areas and Routes in Flood‐Prone Zones of Swabi District, Pakistan

A building information modeling-based fire emergency evacuation simulation system for large infrastructures

During fire emergencies, people in large infrastructures are difficult to comprehend real-time fire dynamics and identify available evacuation routes, leading to lengthy evacuation time and unnecessary loss of life and property. This study aims to develop a fire emergency evacuation simulation system for large infrastructures. This system integrates the data from building information modeling platform, fire dynamics simulator, and evacuation simulation platform through customized developments. Real-time fire situation is transmitted to the evacuation simulation platform to assess the impact of dynamic fire spread on the evacuation of people. The behavioral and psychological characteristics of evacuees of different genders and age groups are considered in simulated evacuations. An algorithm for optimizing evacuation route planning is designed to improve the utilization of each evacuation exit and provide a visualization of evacuation routes. This proposed system was validated at a busy subway station in China. The evacuation efficiency increased by 12.87%. It is concluded that this system can provide real-time dynamic optimal evacuation route planning for enhanced emergency management.

Perencanaan Peta Jalur Evakuasi Mitigasi Banjir

Flooding is a serious threat that often hits Libungo Village, Bone Bolango Regency. In order to improve flood risk preparedness and mitigation in this village, the purpose of this service is to plan a clear evacuation route map so as to provide the necessary guidance for villagers to be able to identify safe evacuation routes and carry out efficient evacuations when floods occur. The process of making evacuation route maps will involve collecting topographic, weather, and historical flood data. The data will be processed and analyzed and then enter the stage of the map-making process, namely field surveys to identify optimal evacuation routes. After making the map is complete, counseling is carried out to the community to provide an understanding of the use of evacuation route maps and actions to be taken when floods occur. The method used in this service is the PAR (Participatory Action Research) method, the result of this service is the level of public awareness of anticipating more severe floods. With conclusions Through mapping evacuation zones and planning clear evacuation route maps, people can better understand the steps that need to be taken in emergency situations.

Dynamic planning method of evacuation route in dam-break flood scenario based on the ACO-GA hybrid algorithm

As the major water storage infrastructure, the failure of a reservoir dam will cause significant losses to people's lives and property. A scientific and reasonable evacuation route is one of the essential measures to reduce casualties. Thus, it is of great significance to realize the dynamic planning of evacuation routes in dam-break flood scenarios to reduce the risk of dam failure. This study employed a physically based mathematical model (Breach model) and a two-dimensional hydrodynamic model to simulate dam-break flood routing. Further, a road network construction method was proposed based on the graph theory and flood routing information. The ant colony optimization algorithm (ACO) was improved by the backtracking, the improved heuristic function, and the elite ants, and the genetic algorithm (GA) was optimized according to the characteristics of flood avoidance route planning. Accordingly, an improved ant colony-genetic optimization hybrid algorithm (ACO-GA) was proposed. Compared with the basic ACO algorithm, the correct rate of simulation results of the ACO-GA hybrid algorithm was improved by 34 %, the average number of iterations was reduced by 18.3 times, and the optimization ability was markedly enhanced. Based on the constructed dam-break flood scenario and the ACO-GA hybrid algorithm, the novel dynamic planning method for evacuation routes in the dam-break flood scenario was proposed. Moreover, this method was applied to a typical water conservancy engineering project, and the planned optimal evacuation routes accurately avoided the flood influence, and the calculation results were reasonable and accurate. The real-time dynamic planning of flood avoidance routes based on flood routing information was realized. This study provides effective guarantee for the emergency transfer of downstream people, which has valuable implications for the theoretical research and engineering practice of reservoir dam safety and management.

Integration of Level 3 probabilistic risk assessment for nuclear power plants with transportation simulation considering earthquake hazards

This research improves the realism of Level 3 probabilistic risk assessment (PRA) for nuclear power plants (NPPs) to avoid subjective expert judgement by modeling the evacuation behavior of the residents and making two advancements. First, evacuation speed taken from a transportation simulation code was used as an input to the Level 3 PRA code, i.e., the MELCOR Accident Consequence Code System (MACCS). The transportation simulation code uses the Multiagent Transport Simulation (MATSim) evacuation extension software. MATSim is interfaced with MACCS, by generating input-output communication through evacuation routes and speed distributions. Integrating the agent-based transportation simulation with the Level 3 PRA code enables the explicit incorporation of spatiotemporal evacuation processes into NPP offsite risk assessment. Second, this research is the first to explicitly incorporate the probability of seismic damage to roadway bridges into Level 3 PRA of NPPs. The likelihood of seismic bridge damage, given an earthquake, is estimated using the HAZUS program developed by the Federal Emergency Management Agency (FEMA). The road closure due to seismic damage, estimated by HAZUS, is used as input to the agent-based transportation simulation, where evacuation routes and speed distributions are calculated considering the impact of seismic damage. These advancements can improve the realism of Level 3 PRA for NPPs by enhancing the evacuation analysis resolution and reducing the reliance on subjective judgment to characterize evacuation routes, speed, and seismic impact on Level 3 PRA. Integrating the transportation simulation and the natural hazard risk assessment with the Level 3 PRA code can also provide more in-depth causal insights for offsite risk management, such as recommending optimal evacuation routes in an emergency response plan. These contributions are demonstrated using the Sequoyah NPP case study adopted from the state-of-the-art reactor consequence analyses (SOARCA) conducted by the U.S. Nuclear Regulatory Commission (NRC).

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.

Dynamic Evacuation Routes Navigation for Passenger Ship Fire Based on Intelligence Algorithms

AbstractTo explore fire hazards and provide real‐time path navigation services for passenger ships, this study proposes an improved artificial fish swarm algorithm (IAFSA) to give the optimal evacuation route for evacuees when a passenger ship is on fire. First, the PyroSim software is used to simulate the fire scene on the sixth deck of the passenger ship. Through which the real‐time temperature, carbon monoxide (CO) concentration, and visibility of each exit are obtained, and the spread speed of the fire hazard area is determined. Then, the artificial fish swarm algorithm is improved by using the dynamic Visual and step, and the optimal path planning for the evacuation of passenger ships is achieved. The Visualized Evacuation Guidance System (VEGS) is developed to provide the optimal route for evacuees on the passenger ship. Finally, VEGS is applied to the fire evacuation of passenger ships. The results show that VEGS can provide the optimal evacuation path for evacuees and avoid detours in fire hazard areas compared with other algorithms. Seventy‐five percent of the evacuees who used VEGS in the event of a passenger ship can escape in a time shortened by up to 40.8%.

GIS-based identification and analysis of suitable evacuation areas and routes in flood-prone zones of Nakhon Si Thammarat municipality

Floods are a significant issue across Southeast Asia, with Thailand, particularly the Nakhon Si Thammarat Municipality, being heavily affected due to its unique conditions such as heavy rainfall, rapid urbanization, and low-lying coastal position. This study utilizes Geographic Information Systems (GIS) to analyze the flood-prone areas of Nakhon Si Thammarat Municipality, assess its road networks, and identify optimal evacuation locations and routes. Various data layers such as slope angle, elevation, distance from roads, rainfall, TWI, NDVI, land use, soil texture, distance from rivers, stream density, and road density are integrated using the frequency ratio method. The findings reveal areas with very high flood susceptibility, which span 198.78 sq. km (56.74%) at the high frequency level, 299.43 sq. km (37.63%) at the low frequency level, and 284.62 sq. km (53.88%) at the moderate frequency level. During flood scenarios, travel times saw an average increase to 21.4 min, which is a fourfold surge compared to regular conditions, highlighting the impact of floods on evacuation strategies. Upon evaluating the road network under flood conditions and applying network analysis techniques, efficient and safe evacuation routes were determined. The results, which underscore a fourfold increase in travel time during flood scenarios, present valuable insights for emergency management authorities across Thailand and Southeast Asia to devise comprehensive evacuation plans, thereby enhancing regional resilience against future flood events. Furthermore, the methodologies and findings are adaptable and can be applied to other flood-prone regions within Southeast Asia, contributing to improved disaster preparedness and response.

Dynamic Dose-Based Emergency Evacuation Model for Enhancing Nuclear Power Plant Emergency Response Strategies

The safe evacuation of residents near a nuclear power plant during a nuclear accident is vital for emergency response planning. To tackle this challenge, considering the dynamic dispersion of radioactive materials in the atmosphere and its impact on evacuation routes under different meteorological conditions is crucial. This paper develops a dynamic dose-based emergency evacuation model (DDEEM), which is an efficient and optimized nuclear accident evacuation model based on dynamic radiological dose calculation, utilizing an improved A* algorithm to determine optimal evacuation routes. The DDEEM takes into account the influence of radiological plume dispersion and path selection on evacuation effectiveness. This study employs the DDEEM to assess radiological consequences and evacuation strategies for students residing 5 km from a Chinese nuclear power plant. Under various meteorological conditions, including the three typical meteorological conditions, random ordered and random unordered meteorological sequences, optimal routes obtained through the DDEEM effectively reduce radiological dose exposure and mitigate radiation hazards. The results indicate that all evacuation paths generated by the DDEEM have a maximum dose of less than 1 mSv. Through simulations, the model’s effectiveness and reliability in dynamic radiological environments in terms of radiological consequences and evacuation analysis is verified. The research provides valuable insights and a practical tool for nuclear power plant emergency decision-making, enhancing emergency management capabilities during nuclear accidents. The DDEEM offers crucial technical support and a solid foundation for developing effective emergency response strategies.

Study on optimization of shelter locations and evacuation routes of gas leakage accidents in chemical industrial park

Safety hazards cannot be ignored during the promotion of local economic development in chemical parks. In a crowded chemical industrial park, heavy casualties and serious economic losses may be incurred in a toxic gas leak accident. Accordingly, the location of emergency shelters and occupant escape routes in chemical parks during gas leakage accidents were examined in this study. Additionally, the risk of leakage in chemical parks throughout the year was assessed by selecting the average values of wind direction, wind speed and temperature across four seasons, and criteria for selecting emergency shelters were set according to the minimum risk of toxic gas leakage throughout the year. Moreover, a 30,000 t/a carbon-five separation construction project was adopted as a case study, where instantaneous leakage of an auxiliary raw material liquid ammonia storage tank occurred. The Floyd algorithm was then used to calculate the evacuation paths for people at multiple sources and multiple endpoints. The influence of the dynamic diffusion of toxic gas around the leakage source point on evacuation route safety was thoroughly analyzed, and occupant evacuation plans were employed in the chemical park based on the shortest evacuation path with the least impact from toxic gases. The findings produced by this study may provide reliable technical support guidance for safety evacuation in regard to toxic gas leakage accidents for personnel in chemical industry parks.

Evaluation of pollutant exposure using virtual walkers and large-eddy simulation: Application to an idealised urban neighbourhood

Urban air quality studies have primarily focused on pollutant dispersion; however, spatial or temporal concentrations collected at discretely distributed grid points (or fixed receptors) do not reflect the actual pollutant exposure of pedestrians. Using large-eddy simulation (LES) with virtual walkers implemented, this study investigates pollutant exposure of walking agents (or moving receptors) in an urban turbulent boundary-layer flow developed over an aligned building array under the influence of different wind directions. The spatial variability of the exposure risks are found to be better captured by the moving receptors than the fixed receptors along the same agent walking tracks. We demonstrate that the actual exposure can differ significantly from results interpreted from data recorded by the fixed receptors (corresponding to Eulerian estimates) and show that large discrepancies occur in avenues near the source, wherein dispersion of the point release has not occurred on larger spatiotemporal scales. In most scenarios, optimal evacuation routes are shown to be ones that deviate as much as possible from the dominant wind direction; however, one needs to decide the priority of moving to further avenues first or immediately adjusting the walking direction. The results should serve as a useful baseline reference for environmental health impact assessment and evacuation route planning against hazardous releases of air pollutants in more complex urban environments.

EVACUATION ROUTE PLANNING MODEL BASED ON THE FUZZY ANT COLONY OPTIMIZATION ALGORITHM

The construction of evacuation routes from urban areas has become an urgent task in the modern world, as the number of emergencies is constantly increasing. Standard route planning algorithms do not meet the requirements of fast and efficient evacuation because they do not fully consider the environmental parameters and have a high computational complexity, and erroneous results can have critical consequences, including loss of life. The paper proposes a model of finding an optimal evacuation route in emergencies in urban areas based on a modified ant colony optimization algorithm: an ant (a person or a vehicle) is allowed to start moving from several possible vertices of the graph, as well as to end the route at several available vertices. It is connected with the fact that there are usually multiple evacuation start points and destinations. The transitions matrix is built using additional parameters, the uncertainty of which is taken into account using the methods of computing with words and the theory of type-2 fuzzy sets and systems. For modeling, in the paper such additional parameters as the quality of the road surface, the number of road lanes, the level of traffic jams, and the distance to the epicenter of the emergency were used. The proposed model was implemented and applied in one of the quarter of Kyiv.