Evacuation Route Planning Research Articles

Model of Tsunami Mitigation and Evacuation Routes in Pancer Banyuwangi

Banyuwangi has the largest tsunami disaster risk in East Java, about 45,305 affected people, Rp. 160,886,000,- for the risk of physical losses, Rp. 106,414,000,- for the risk of economic losses and 84 ha of environmental losses (BNPB, 2023). In addition, according to historical data, Banyuwangi was hit by tsunami on June 9, 1994 with magnitude of 7.8 which caused a maximum run up of 14.0 m and killed 238 lives (BNPB, 2023). Also based on Undang- undang No. 24 Tahun 2007 concerning Disaster Management, Indonesia should have preventive and mitigation measures for tsunami disasters both before and after as an effort to reduce the disaster risk (Putra, 2009) . Tsunami modeling in southern Java using COMCOT v1.7 as tsunami worst case scenario in Banyuwangi is the first step for mitigation. The initial results of COMCOT were then used for preparing tsunami disaster evacuation route planning based on the Tsunami Natural Disaster Evacuation Route Planning Guidelines, Dirjen Bina Marga, 2023. The final step is validation/routing survey of the 8 (eight) routes that have been determined. 2 (two) of the 8 (eight) routes fulfill the guidelines for planning tsunami mitigation routes. However, all routes can be considered due to the distribution of settlements, the number of affected residents and the number of routes needed

Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires

When a fire occurs underground, pre-existing emergency escape routes may become ineffective. Such scenarios necessitate the real-time planning of escape routes that consider evolving conditions and prioritize safety. This paper proposes a multi-objective real-time search method for emergency escape routes using dynamic programming to address these challenges. Firstly, this paper discusses how the evacuation of an underground mine fire caused by external factors depends on the fire monitoring system, personnel positioning system, and emergency evacuation system, as well as the modeling and solution methods of evacuation route planning. Then, based on fire smoke, CO, and temperature sensor data from the mine ventilation network structure and monitoring system, the possible smoke spread in the event of an underground fire was calculated. The objectives are to optimize both the shortest equivalent length of the path and the shortest time to walk through the smoke flow while ensuring that temporary escape time does not exceed the rated protection time of the self-rescuer. A mathematical model for emergency escape route planning is established under these conditions. A labeling algorithm based on dynamic programming is employed to find the Pareto optimal solution set of emergency evacuation routes that meet emergency requirements. Finally, two path evaluation indicators, namely “escape target priority” and “personnel temporary escape time”, are introduced to re-rank the solutions in the Pareto optimal set, thereby obtaining disaster evacuation routes with different priorities. Example verification shows that the algorithm can quickly solve the disaster evacuation routes that meet the actual disaster evacuation needs in complex networks. Example verification shows that the algorithm can quickly solve the emergency escape routes in complex networks that meet actual emergency escape needs.

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

Research on the Intelligent Planning of Mine Fire Evacuation Routes Based on a Multifactor Coupling Analysis

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.

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.

A GIS-Based Evacuation Route Planning in Flood-Susceptible Area of Siraha Municipality, Nepal

Flood is one of the most frequently occurring and devastating disasters in Nepal. Several locations in Nepal are at high risk of flood, which requires proper guidance on early warning and safe evacuation of people to emergency locations through optimal routes to minimize fatalities. However, the information is limited to flood hazard mapping only. This study provides a comprehensive flood susceptibility and evacuation route mapping in the Siraha Municipality of Nepal where a lot of flood events have occurred in the past and are liable to happen in the future. The flood susceptibility map was created using a Geographic Information System (GIS)-based Analytical Hierarchy Process (AHP) over nine flood conditioning factors. It showed that 47% of the total area was highly susceptible to flood, and the remaining was in the safe zone. The assembly points where people would gather for evacuation were selected within the susceptible zone through manual digitization while the emergency shelters were selected within a safe zone such that they can host the maximum number of people. The network analysis approach is used for evacuation route mapping in which the closest facility analysis proposed the optimum evacuation route based on the walking speed of evacuees to reach the emergency shelter place considering the effect of slope and flood on the speed of the pedestrian. A total of 12 out of 22 suggested emergency shelters were within 30 min, 7 within 60 min, and 2 within 100 min walk from the assembly point. Moreover, this study suggests the possible areas for further shelter place allocations based on service area analysis. This study can support the authorities’ decision-making for the flood risk assessment and early warning system planning, and helps in providing an efficient evacuation plan for risk mitigation.

Safety map: Disaster management road network for urban resilience

This study aims to select disaster resistance roads in urban areas which require management through their topological relationship and by the consideration of the roads’ durable and resilient attributes against disaster. Disaster resistance road network means a network of safe roads that are used dynamically according to various disaster scenarios. The network requires high accessibility and disaster resistance. In some cases, roads are used as an evacuation route to secure the safety of the citizens, to preserve human resources, and to transport emergency supplies to disaster areas. Upon the occurrence of a disaster, using the road infrastructure resistant to various types of disaster can help maintain the functions of cities and can contribute to the increase of urban resilience. In this paper, the process of reconstructing road network entails the extraction of road layers through ERAM (Eigenvector Ratio of Adjacency Matrix) model, both the road layers with high disaster resilience from the public road data, and the road layers with high connectivity in the topological relationship between roads. Subsequently, the network combines the layers based on high disaster resistance and the layers based on topological relationship, and leads to a disaster resistance road network. Verification will be performed through a case study comparing the extracted disaster resistance road network and the traffic counting points that signify the traffic characteristics. The proposed research results will be used as geographical information referred to for evacuation route planning and emergency transport route planning in disaster situations and will serve as basic study material for disaster plan.

Disaster evacuation route planning strategy based on traffic prediction

Disaster evacuation route planning strategy based on traffic prediction

Disaster Evacuation Route Planning Strategy Based on Traffic Prediction

Disaster Evacuation Route Planning Strategy Based on Traffic Prediction

Preparedness of Tsunami Disaster in Pandeglang Region Due to The Activity of Mount Krakatau

Pandeglang Regency is one of the areas with the most building damage in The 2018 Anak Krakatau Tsunami. The tsunami in 2018 was caused by the activity of Anak Krakatau Volcano in the form of an avalanche of material on the volcano's cliffs. The subdistricts in Pandeglang Regency affected by the tsunami were Carita Subdistrict, Labuan Subdistrict, and Panimbang Subdistrict. This research evaluates potential damage to buildings to determine the condition of the existing land, determines an evacuation route to a temporary evacuation site (TES), and simulates a tsunami evacuation using this evacuation route. Parameters of run-up height and building type are used as parameters for assessing building damage. The determination of TES is influenced by run-up height, elevation, and distance from the shoreline. Evacuation route planning and evacuation simulation are based on the assumed number of evacuees and the scenario of a tsunami evacuation. The results showed moderate damage to buildings in Carita Subdistrict, Labuan Subdistrict, and Panimbang Subdistrict. The examination of existing land as TES, namely Carita Vacant Land, Carita 1 Middle School, LDII Labuan Mosque, Labuan Shelter Building, Panimbang Vacant Land. According to the tsunami evacuation scenario during the day, evacuation time results for 25-30 minutes with an average speed of 1 m/s. The tsunami evacuation time at night is free of obstacles or with obstacles for 50-85 minutes with an average speed of 1 m/s. The tsunami evacuation time at night is full of free and obstacle-free tours for 60-100 minutes with an average speed of 0.5 m/s. Evacuation time based on simulation results is compared with evacuation time calculated by ETA and other studies as data validation to determine the probability of community preparedness in the Pandeglang Regency. The preparedness community in Pandeglang Regency is in the ready category by 25% in the Carita subdistrict, Labuan subdistrict, and Panimbang Subdistrict.

Practical initialization of the Nelder–Mead method for computationally expensive optimization problems

Black-box optimization (BBO) algorithms are widely employed by practitioners to address computationally expensive real-world problems such as automatic tuning of machine learning models and evacuation route planning. The Nelder–Mead (NM) method is a well-known local search heuristic for BBO that has been applied to solve many real-world problems from way back because of its promising performance. However, this method has a strong dependence on initialization due to its local search tendency. Nevertheless, a discussion on the proper initialization of the NM method is limited to the recent study by Wessing (Optim Lett 13(4):847–856, 2019), which is solely based on an analysis using the simple sphere function. In this study, we take a further step to improve Wessing’s result by massively investigating how the initialization affects the search performance in views of the initial simplex size and shape and a constraint handling method that is employed on 24 BBO benchmarking problems. Based on the numerical results, we present the empirical best practice for the initialization of the NM method for cases involving a limited evaluation budget.

Wildfire Risk in the Complex Terrain of the Santa Barbara Wildland–Urban Interface during Extreme Winds

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas.

Optimal Emergency Evacuation Route Planning Model Based on Fire Prediction Data

For the emergency evacuation of cruise ships in case of sudden fire, this research proposes a dynamic route optimization method based on the improved A∗ algorithm for real-time information, in order to obtain the real-time optimal evacuation route. Initially, a basic network topology diagram is established according to the internal structure of the cruise ship. Before the occurrence of the accident, the A∗ algorithm can be applied to obtain an a priori evacuation network consisting of all the optimal routes from each node to the exit. At the time of the accident, the dynamic diffusion of fire can be simulated using Fire Dynamics Simulator (FDS) based on the preliminary information of the fire, so as to estimate the impact of the fire domain on each node of the network. Then, according to the fire dynamic diffusion data, the evacuation route planning is carried out by the improved A∗ algorithm applying the breadth-first search strategy, so as to determine the optimal route from the current node to the safety exit and to reduce the possibility of casualties due to the uncertainty of the fire during the evacuation. This model allows for both people’s safety and evacuation time to dynamically avoid fire-affected nodes and helps people to reach the safe area as soon as possible. Finally, the evacuation model is established according to the open-source cruise ship structure, and the evacuation process of people under the dynamic spread of cruise ship fire is simulated. The results show that the route planning method proposed in this research works out well in evacuating mass people, which can effectively reduce the evacuation time and improve the safety of the evacuation process.

Evacuation route planning for alternative fuel vehicles

As the number of adopted alternative fuel vehicles increases, it is crucial for communities (especially those that are susceptible to hazards) to make evacuation plans that account for such vehicles refueling needs. During emergencies that require preemptive evacuation planning, travelers using alternative fuel vehicles are vulnerable when evacuation routes do not provide access to refueling stations on their way to shelters. In this paper, we formulate and solve a novel seamless evacuation route plan problem, by designing |K| spanning trees with side constraints that capture the refueling needs of each k∈K vehicle fuel type on their way to reach a shelter. We develop a branch-and-price matheuristic algorithm based on column generation to solve the evacuation route planning problem. We apply the proposed framework to the Sioux Falls transportation network with existing infrastructure deployment and present numerical experiments. Specifically, we discuss the optimal system evacuation travel and refueling times under scenarios of various alternative fuel vehicle driving ranges. Our findings show that the characteristics of each vehicle fuel type, like driving range and the refueling infrastructure topology, play a pivotal role in determining evacuation route plans. This means that an evacuation route could prove unique to a single vehicle fuel type, while being infeasible to others. Finally, we observe that the driving range constraints could force evacuee vehicles to detour to meet their refueling needs before reaching safety and increase the total evacuation time by 7.32 % in one of evaluated scenarios.

Optimal Evacuation Route Planning of Urban Personnel at Different Risk Levels of Flood Disasters Based on the Improved 3D Dijkstra’s Algorithm

In the event of a flood, the choice of evacuation routes is vital for personnel security. This is particularly true when road factors play an important role in evacuation time. In this study, the traditional Dijkstra algorithm for route planning is improved, and the evacuation model is improved from 2D to 3D. At the same time, the Lasso regression method is adopted to take the road factors into account in the pedestrian speed, and the location of shelter is selected and optimized through the calculation results, and then based on the improved 3D Dijkstra’s algorithm, an optimal evacuation route method in different flood disasters risk levels is proposed, which can make pedestrians reach the shelters within the shortest time. After taking into account road factors (road width, slope, non-motorized lane width, and pedestrian density), through the calculation of the pedestrian speed formula, the estimated evacuation time of pedestrians is obtained. By combining available shelters with evacuation routes, the optimized algorithm improves the evacuation efficiency facing different risk levels of flood disasters. The results show that when residents are confronted with flood disasters of once-in-20-year, once-in-50-year, and once-in-100-year, the proposed optimization algorithm can save 7.59%, 11.78%, and 17.78% of the evacuation time. Finally, according to the verification of the actual effect in Meishan Town, the proposed method of optimal evacuation route planning can effectively reduce the evacuation time of pedestrians, evaluate, and optimize the location of existing shelter, and provide suggestions for urban road reconstruction.

Intelligent Evacuation Route Planning Algorithm Based on Maximum Flow.

This paper focuses on the problem of intelligent evacuation route planning for emergencies, including natural and human resource disasters and epidemic disasters, such as the COVID-19 pandemic. The goal of this study was to quickly generate an evacuation route for a community for victims to be evacuated to safe areas as soon as possible. The evacuation route planning problem needs to determine appropriate routes and allocate a specific number of victims to each route. This paper formulates the problem as a maximum flow problem and proposes a binary search algorithm based on a maximum flow algorithm, which is an intelligent optimization evacuation route planning algorithm for the community. Furthermore, the formulation is a nonlinear optimization problem because each route’s suggested evacuation time is a convex nonlinear function of the number of victims assigned to that route. Finally, numerical examples and Matlab simulations demonstrate not only the algorithm’s effectiveness, but also that the algorithm has low complexity and high precision. The study’s findings offer a practical solution for nonlinear models of evacuation route planning, which will be widely used in human society and robot path planning schemes.

Fire Scenario Zone Construction and Personnel Evacuation Planning Based on a Building Information Model and Geographical Information System

The spatial–temporal simulation of fire disasters and evacuation route planning are important research fields for urban emergency responses and are primary tasks that answer complex questions after fires break out. The increasing demand for refined building information models will sharply increase the calculated and analyzed quantity. This demand presents a challenge for fire emergency responses based on massive building information. In this paper, the principle of the realistic worst case (RWC) is introduced into fire simulation and evacuation route planning. Taking the library of the Nanjing Forestry University as the study object, the spatial–temporal characteristics of the influential environmental factors of the fire are simulated, such as the meteorological elements, building structure, and building skin. The scenario zones that are relatively prone to fire are selected using an overlay analysis across the four seasons. Then, the risk threshold for evacuating personnel is analyzed in the fire zone according to international standards and firefighting criteria. Specific parameters are determined based on the analysis of the above. The growing trends for fires across the four seasons are simulated with scenario zones as the starting positions and incorporate factors such as temperature, carbon monoxide, and smoke. Lastly, a life safety assurance path (LSAP) for personnel evacuation is designed based on an indoor road network and path search algorithm. The evacuation planning result is compared with the traditional shortest-time path and shortest-distance path. Based on the study results, fire scenario zones can improve the speed and operating efficiency of spatial–temporal simulation models of fire and can also support path planning and design for emergency responses.

Development of Evacuation Route Planning Algorithm for Responding to Urban Forest Fires

The Gangwon region (Korea) is severely affected by forest fires, where approximately sixty-six wildfires have occurred over the last three years, which in turn have damaged 1,299 ha of this region. Hence, it is necessary to develop schemes for reducing the damage caused by forest fires in Gangwon. In this study, we developed an algorithm for planning evacuation routes. The developed algorithm was applied to a virtual scenario for determining evacuation start points within the spread range of wildfires, fifteen evacuation routes were then determined for each start point, and the associated distance information was displayed. Furthermore, by employing the Naver Maps software, the obtained evacuation routes was compared and analyzed with respect to the route distance. We believe that the results obtained from this study can be used as basic data for making decisions to identify various evacuation routes.

Network Modeling of Hurricane Evacuation Using Data-Driven Demand and Incident-Induced Capacity Loss Models

The development of a hurricane evacuation simulation model is a crucial task in emergency management and planning. Two major issues affect the reliability of an evacuation model: one is estimations of evacuation traffic based on socioeconomic characteristics, and the other is capacity change and its influence on evacuation outcome due to traffic incidents in the context of hurricanes. Both issues can impact the effectiveness of emergency planning in terms of evacuation order issuance, and evacuation route planning. The proposed research aims to investigate the demand and supply modeling in the context of hurricane evacuations. This methodology created three scenarios for the New York City (NYC) metropolitan area, including one base and two evacuation scenarios with different levels of traffic demand and capacity uncertainty. Observed volume data prior to Hurricane Sandy is collected to model the response curve of the model, and the empirical incident data under actual evacuation conditions are analyzed and modeled. Then, the modeled incidents are incorporated into the planning model modified for evacuation. Simulation results are sampled and compared with observed sensor-based travel times as well as O-D-based trip times of NYC taxi data. The results show that the introduction of incident frequency and duration models can significantly improve the performance of the evacuation model. The results of this approach imply the importance of traffic incident consideration for hurricane evacuation simulation.

Conflict-Free Evacuation Route Planning

Conflict-Free Evacuation Route Planning