Optimal Evacuation Research Articles

Modified ACO Evacuation Model Based On Evacuation Entropy

Since the existing crowd evacuation model for path optimization problems seldom considers the degree of confusion in the crowd movement, a modified Ant Colony Optimization (ACO) evacuation model based on evacuation entropy is constructed in this paper. Evacuation entropy reflects the degree of crowd confusion. It is used to modify the heuristic function of traditional ACO and is taken as the metric of the local pheromone updating strategy. This study is modeled by grid method and the simulation results show that the introduction of evacuation entropy greatly improves the convergence rate. The simulation of the optimal path takes the degree of chaos and the length of the crowd movement into account. It is of great practical significance to study how to plan reasonable evacuation routes.

CLOTHO: A Large-Scale Internet of Things-Based Crowd Evacuation Planning System for Disaster Management

In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this paper, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile cloud computing platform in order to develop the crowd lives oriented track and help optimization system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the artificial potential field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. Based on APF, we propose an evacuation planning algorithm names as APF with relationship attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced.

Selection of optimal escape routes in a flood-prone area based on 2D hydrodynamic modelling

Abstract Optimizing escape routes during an extreme flood event is an effective way to mitigate casualties. In this study, a model for selecting optimal escape routes in a flood-prone area has been proposed, which includes a module for predicting the two-dimensional (2D) hydrodynamics and modules for assessing the hazard degree for evacuees, calculation of evacuation times and determination of different escape routes. In the module for determining escape routes, two evacuation schemes were used: scheme A to find optimal escape routes based on established road networks and scheme B to design a new optimal evacuation route. Extreme overbank floods occurred in the Lower Yellow River (LYR) in July 1958 (‘58.7’) and August 1982 (‘82.8’) and the proposed model was applied to select the optimal escape routes on a typical floodplain area of the LYR for these two floods. Model predictions indicated that: (i) the optimal escape routes for these two floods were the same for all three starting locations, and the optimized routes provided 3 h more time for evacuees to escape; and (ii) the time of evacuation would need to be earlier for the ‘58.7’ flood because of its larger amount of water volume and higher peak discharge.

Evacuation strategy of emergent event in metro station based on the ELECTRE method

Metro station is crowded during rush hours and the structure is usually complex. Hence, the determination of an optimal evacuation strategy is the key for improving the evacuation process efficiency when an emergency event happens. A group decision-making model based on triangular intuitionistic fuzzy numbers (TIFNs) theory is developed for selecting an evacuation strategy in metro station. In this model, a group of experts elicit their preferences about the different evacuation strategies using TIFNs for multiple criteria. The Elimination Et Choice Translation Reality (ELECTRE) method is used to sort the different emergency evacuation strategies proposed for the metro station, and it should be adapted to manage TIFNs above all. The TIFN-ELECTRE model is proposed and applied to select the best emergency evacuation strategy of metro station. Finally, a numerical example with respect to the emergency evacuation strategy of Wuhan Guanggu Square station is carried out.

Effect of exit placement on evacuation plans

Human behaviour while trying to escape a room via its main means of egress is an important issue in social science, complex systems research, and architectural planning. Disasters resulting from human crowding have increased in recent years. In such cases, it is important to consider several factors, including the smooth flow of pedestrians and the positions of obstacles and exits. This paper describes the effects of exit placement in environments congested with pedestrians. An evacuation system was designed and implemented with multiple exits in four different arrangements. The system utilised two artificial intelligence (AI) techniques—simulated annealing (SA) and depth-first search (DFS)—to examine the optimal balance between the placements of the various exits. Simulation and experimental results demonstrated that adjacently placed exits resulted in increased crowding at some exits over others when a nearest-exit path technique (DFS) was adopted as the evacuation strategy, resulting in longer evacuation times. Of the two examined evacuation techniques, SA proved superior, as it optimally balanced the pedestrian distribution over all available exits in all scenarios. In addition, the optimal-path technique (SA) did not suffer the ill-effects of adjacent exit placement. The simulation results confirm the importance of developing optimal evacuation plans, which could significantly outperform commonly employed nearest-exit evacuation strategies.

Network Design Model to Integrate Shelter Assignment with Contraflow Operations in Emergency Evacuation Planning

In traditional emergency evacuation planning, shelter assignment and contraflow operations are determined sequentially. In this paper, we show that these two types of network design should be considered simultaneously to achieve a better evacuation performance. A mixed integer linear program is proposed, in which an earliest arrival flow model is employed to describe the underlying system optimum evacuation traffic flow pattern. The embedded earliest arrival flow model, built upon a node-link network representation, helps to maintain a small problem size. To address the computational difficulty, an accelerated Benders decomposition algorithm is developed to leverage the separable structure of the proposed model. Numerical experiments are used to investigate the effectiveness of the proposed model and solution algorithm. The results illustrate that the integrated consideration of shelter assignment and contraflow operations in evacuation planning facilitates the effective usage of the evacuation network capacity to further reduce the total system travel time in emergency evacuation.

A GIS-based analysis of constraints on pedestrian tsunami evacuation routes: Cascais case study (Portugal)

Tsunami hazard in coastal areas susceptible to flooding, although reduced (in terms of probability of occurrence), may pose a high risk. Therefore, in these areas, a detailed evacuation planning of the affected population is required as a risk mitigation measure. The knowledge and enforcement of evacuation routes may reduce the population vulnerability, making it more resilient and reducing risk. This paper presents a GIS approach for modelling evacuation routes based on the optimal path search problem, of the graph theory, which is implemented on ArcCasper tool. The methodology proposed considers the elements involved in the evacuation process, the worst credible tsunami inundation scenario (hazard extent and travel time), the number of people that needs to be evacuated in different time scenarios, the safe areas or destination points of the evacuation routes, the roads network characteristics and finally the time available to evacuate. The knowledge of those elements allows predicting some possible outcomes of the evacuation, such as the arrival time of the evacuees to a shelter and the identification of congestion hot spots resulting from the application of a flocking model which simulates the path to be used by evacuees avoiding obstacles. The municipality of Cascais was used to test the methodology proposed in this study. Cascais is one of the largest urban centres located about 25 km west of Lisbon, Portugal, with a high density of infrastructure along the coastline whereby most of the population and economic activities are exposed to a tsunami. The results, presented in the form of maps, allow identifying the optimal evacuation routes as well as the unfeasible routes. This crucial information could be used to the evacuation optimization regarding the location of meeting points and vertical shelters as well as to improve the accessibility of the areas to be evacuated.

Fire modeling in a nonventilated corridor

The main objective of this study was to determine the effect of fire in a nonventilated corridor. A real-scale model of a corridor has been modeled in Fire Dynamics Simulator(F.D.S.) in order to determine the evolution of indoor temperatures, the visibility and the oxygen quantities during a fire. The start time of a sprinkler has also been determined. The use of sprinklers in buildings has become a necessity and a requirement imposed by technical norms. The provision of this type of installation has become a common feature in buildings with a high fire risk, with two main effects: fire extinction and protection of structural and partition elements from high temperatures[15]. The ultimate goal is to ensure optimal conditions for saving the building users, intervention teams and maintaining the stability of the building. Low temperatures and good visibility on the escape routes during a fire are the basic conditions to ensure the optimal evacuation of users.

Optimal number and location planning of evacuation leader in subway stations

Evacuation leaders are conducive to the improvement of crowd evacuation efficiency when the evacuees are not familiar with the evacuation routes. The assignment of the number and positions of leaders is particularly important for the efficiency of evacuation. This paper formulates the optimization of passenger evacuation with leaders in a subway station by taking the labor, time, and safety costs into account. A maximal covering model with modifications is proposed to optimize the number and initial locations of leaders. The modifications of social force model are used to represent the dynamics of the evacuees and the leaders in simulations. We use the microscopic simulations of the evacuation of a subway station in Beijing to evaluate the performance of the proposed optimal evacuation strategy. Compared to empirically placing the leaders in the subway station, the proposed strategy shows better performance in terms of labor cost, evacuation time and safety level.

Hurricane Evacuation Modeling Using Behavior Models and Scenario-Driven Agent-based Simulations

Transportation modeling and simulation play an important role in the planning and management of emergency evacuation. It is often indispensable for the preparedness and timely response to extreme events occurring in highly populated areas. Reliable and robust agent-based evacuation models are of great importance to support evacuation decision making. Nevertheless, these models rely on numerous hypothetical causal relationships between the evacuation behavior and a variety of factors including socio-economic characteristics and storm intensity. Understanding the impacts of these factors on evacuation behaviors (e.g., destination and route choices) is crucial in preparing optimal evacuation plans. This paper aims to contribute to the literature by integrating well-calibrated behavior models with an agent-based evacuation simulation model in the context of hurricane evacuation. Specifically, discrete choice models were developed to estimate the evacuation behaviors based on large-scale survey data in Northern New Jersey. Monte-Carlo Markov Chain (MCMC) sampling method was used to estimate evacuation propensity and destination choices for the whole population. Finally, evacuation of over a million residents in the study area was simulated using agent-based simulation built in MATSim. The agent-based modeling framework proposed in this paper provides an integrated methodology for evacuation simulation with specific consideration of agents’ behaviors. The simulation results need to be further validated and verified using real-world evacuation data.

Analysis of Optimal Evacuation Route for Flood Disaster

Analysis of Optimal Evacuation Route for Flood Disaster

Modeling and simulation of group formation and crowd dynamics with smoke effect

In order to depict crowd dynamics in the case of smoke effect, a novel evacuation model is presented based on the extended cellular automaton. In the model, the moving probability of the target position in the next time step is given by combining distance gain and smoke gain at first. Through considering individual kinship and group effect, group disbanding and regrouping rules are defined, and the optimal evacuation route planning is determined. At last, the simulation was conducted to study the key factors influencing evacuation efficiency. The results showed that when the group size was small and the smoke spreading velocity was slow, priority selection of the shortest route strategy was good for reducing evacuation time, and large-scale group should be avoided as much as possible in evacuation process.

An evacuation model based on co-evolutionary multi-particle swarms optimization for pedestrian–vehicle mixed traffic flow

In a large common place, a huge number of pedestrians may flood into the surrounding region and mix with the vehicles which originally existed on the roads when emergent events occur. The mutual restriction between pedestrians and vehicles as well as the mutual effect between evacuation individuals and the environment which evacuees are situated in, will have an important impact on evacuation effects. This paper presents a pedestrian–vehicle mixed evacuation model to produce optimal evacuation plans considering both evacuation time and density degree. A co-evolutionary multi-particle swarms optimization approach is proposed to simulate the evacuation process of pedestrians and vehicles separately and the interaction between these two kinds of traffic modes. The proposed model and algorithm are effective for mixed evacuation problems. An illustrating example of a study region around a large stadium has been presented. The experimental results indicate the effective performances for evacuation problems which involve complex environments and various types of traffic modes.

Incorporating Intelligence into Exit Choice Model for Typical Evacuation

Integrating an exit choice model into a microscopic crowd dynamics model is an essential approach for obtaining more efficient evacuation model. We describe various aspects of decision-making capability of an existing rule-based exit choice model for evacuation processes. In simulations, however, the simulated evacuees clogging at exits have behaved non-intelligently, namely they do not give up their exits for better ones for safer egress. We refine the model to endow the individuals with the ability to leave their exits due to dynamic changes by modifying the model of their excitement resulted from the source of panic. This facilitates the approximately equal crowd size at exits for being until the end of the evacuation process, and thereby the model accomplishes more optimal evacuation. For further intelligence, we introduce the prediction factor that enables higher probability of equally distributing evacuees at exits. A simulation to validate the contribution is performed, and the results are analyzed and compared with the original model.

Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study

This study proposes a stochastic modeling approach as an evacuation decision support system to determine the required vehicles, scheduling and routes under uncertainties in evacuee population, time windows and bushfire propagation. The proposed model also considers road availability and disruptions. A greedy solution method is developed to cope with the complex nature of vehicle routing problem. Furthermore, the effectiveness of the proposed solution is evaluated by comparison with a designed genetic algorithm on sets of various numerical examples. The model is then applied on the real case study of the 2009 Black Saturday bushfires in Victoria, Australia. Several plausible evacuation scenarios are generated, utilizing the historical data of Black Saturday. The results are analyzed using the frequency approach to determine the optimal evacuation plan. The results show that it would have been possible to evacuate the late evacuees on Black Saturday, even within hard time windows and a maximum population.

Multisource multisink optimal evacuation routing with dynamic network changes: A geometric algebra approach

In this paper, we introduced geometric algebra to develop a new multisource multisink optimal evacuation route planning method. A dynamically updatable data structure and a matrix‐based greedy searching algorithm were developed to support the dynamic evacuation route searching for multiple evacuees. Unlike most existing methods, which iteratively search the optimal path for each evacuee, our method can search all the possible evacuation routes synchronously. The dynamic updating of the network topography, weights, and constraints during the route searching is direct and flexible, thus can support the evacuation in dynamic cases. The method is demonstrated and tested by an evacuation case in the city of Changzhou, China. The simulation experiments suggest that the method can well support the dynamic route searching in large scales with dynamic weight changes.

Integer Linear Program Approach for Evacuation of Disaster Victims with Different Urgency Levels

In the aftermath of natural disasters, optimal evacuation planning plays a crucial role in saving people’s lives. In this paper, we propose a mathematical model for optimal planning for helicopters with different capabilities to save victims with different urgency levels. The model is formulated as an optimization problem whose objective is maximizing the number of evacuees under operational constraints such as helicopter’s capacity and endurance and different urgency levels of victims. The proposed model is validated with an illustrative numerical example based on real-world data.

Real-time evacuation route selection methodology for complex buildings

This paper proposes a novel real-time decision methodology for the selection of optimal evacuation routes for buildings. A summary of the mathematical formulation, the solution algorithm and the computer model are presented. The optimization algorithm is based on the stochastic evacuation model predictions by considering emergency data such as the location of the hazard. The method is applied to an industrial building. The stochastic evacuation model is compared with the commercial evacuation model STEPS. In addition, the computational model is applied to several emergency scenarios to evaluate its validity.

Modeling to Optimize Hospital Evacuation Planning in EMS Systems

Objective: To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful in evacuation planning. Methods: An optimization model was used to produce detailed evacuation plans given the number and type of patients in the evacuating hospital, resource levels (teams to move patients, vehicles, and beds at other hospitals), and evacuation rules. Results: Optimal evacuation plans under various resource levels and rules were developed and high-level metrics were calculated, including evacuation duration and the utilization of resources. Using this model we were able to determine the limiting resources and demonstrate how strategically augmenting the resource levels can improve the performance of the evacuation plan. The model allowed the planner to test various evacuation conditions and resource levels to demonstrate the effect on performance of the evacuation plan. Conclusion: We present a hospital evacuation planning analysis for a hospital in a large urban EMS system using an optimization model. This model can be used by EMS administrators and medical directors to guide planning decisions and provide a better understanding of various resource allocation decisions and rules that govern a hospital evacuation.

Evaluating and Optimizing Evacuation Plans for Crowd Egress.

Evacuation planning is an important and difficult task in building design. The proposed framework can identify optimal evacuation plans using decision points, which control the ratio of agents that select a particular route at a specific spatial location. The authors optimize these ratios to achieve the best evacuation based on a quantitatively validated metric for evacuation performance. This metric captures many of the important aspects of an evacuation: total evacuation time, average evacuation time, agent speed, and local agent density. The proposed approach was validated using a night club model that incorporates real data from an actual evacuation.