Robust scheduling for large scale evacuation planning

Efficient evacuation plan with which a maximum evacuees can be sent as soon as possible from the disastrous place to the safe place is an important notion during the response phase of the disaster management. Such a plan in terms of optimization models has been extensively studied in a various scenarios, see [3]. The optimization models have been based on the flow conservation constraint which permits an evacuee to be taken out of the disastrous place only if it can be sent into the safe place. However, the evacuation plan model with no flow conservation can keep several evacuees in the relatively safe places besides the evacuees which could be sent into the safe place.
 In this paper, we describe an optimization model for the evacuation plan based on the non-conservation flow constraint with an efficient solution procedure which keeps a maximum evacuees on the prioritized intermediate places besides a maximum evacuees into the specified safe place.

Fast and efficient evacuation of pedestrians from an enclosed area is a difficult but crucial issue in modern society. In this paper, the optimization of evacuation from a building is studied. A graph is adopted to describe the building layout with nodes representing areas and edges representing connections. The dynamics of the evacuation process in the graph is formulated by a nonlinear discrete-time model at a macroscopic level. To find the optimal evacuation plan, a consecutive differential dynamic programming is developed. It inherits the differential dynamic programming property that solves the value and optimal policy locally. Additionally, it consecutively executes actions for multiple steps in the trajectory, which is beneficial to reduce computational burden and lower optimization difficulty. Simulations on a four-storey building layout demonstrates our method is efficient and suitable for on-site evacuation plan making.

Modelling building emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based simulation

The growing need for evacuation planning is addressed by using a computer-based model of traffic simulation. The VISSIM traffic simulation tool was used to evaluate a current plan and alternative plans for the deployment of transit during an emergency situation in a transit facility such as a bus depot. Different strategies were simulated to study the effect of evacuation on the surrounding traffic network as well as to help the local transit company (LYNX) evaluate its evacuation plan and consider different possibilities without the risk and cost of actual evacuation drills. Nine evacuation scenarios were simulated and analyzed to reach the best evacuation strategy for the LYNX company's main bus depot. Evacuation strategies evaluated include traffic diversion, bus signal optimization, access restriction, different destinations, and evacuation of pedestrians. Total network delay for each scenario was compared with the base case, and results indicate that pedestrian evacuation was better than using buses. Traffic rerouting also could potentially reduce delays and evacuation clearance time.

The isolated community evacuation problem with mixed integer programming

Network redesign for efficient crowd flow and evacuation

Primary health care (PHC) and emergency medical assistance (EMA) are discussed as two fundamentally different strategies of delivering health care. PHC is conceptualized as part of overall development, while EMA is delivered in disaster or emergency situations. The article contrasts the underlying paradigms, and the characteristics of care in PHC and EMA. It then analyzes the characteristics of PHC and EMA health services, their structure, management and support systems. In strategic aspects, it contrasts how managerial and financial sustainability are fundamentally different, and how the term accountability is used differently in development and disaster situations. However, while PHC and EMA, development and disaster, are clear opposite poles, many field situations in the developing world are today somewhere in-between. In such non-development, non-emergency situations, the objectives and approach will have to vary and an adapted strategy combining characteristics from PHC and EMA will have to be developed.

People's awareness of disaster prevention has risen, after the Great East Japan Earthquake. In local communities, the decision of the disaster prevention plan is pushed forward. In the first step, the evacuation plan is considered, however there are various limitations on-the-job training with a lot of residents' participation. Especially, it is impossible to take account of disaster conditions such as the collapses of the liquefaction, the collapses of the block wall and the collapses of the wooden house etc. in normal circumstances.In this study, an evacuation simulation system with tsunami and high tide disaster prediction is developed, and using this developed simulation system damage situations in a local community are estimated. The data base system based on the simulation results is developed in order to make effective use for evacuation for a variety of disaster situations. A costal area in Takamatsu City is selected, and some evacuation plans reflecting the disasters situations obtained by the simulation data base are made and examined.

A time-extended network model for staged evacuation planning

The Tinau flood, caused by Tinau River during monsoon, is one of the most devastating disasters in Butwal, densely populated business hub located in Butwal Sub-Metropolitan City, Lumbini Province, Nepal. The flood threatens lives and properties along the riverbank. An efficient evacuation plan for potential flood victims is crucial to save their lives. Mathematical optimization model, in particular, network flow model could be a tool for designing the plan. Literature flourishes mathematical models including network flow models for evacuation planning. In this paper, we study lexicographically maximum contraflow (Lex-Max Contraflow) problem and implement it to real data set for Tinau flood evacuation to propose optimum evacuation route plan along with optimum distribution of evacuees among shelters based on their suitable prioritization.

Various types of spatial data can be used in different parts of crisis management. We concentrate on support for evacuations. Analyses of the current situation show, that cartographic visualization can be used in different phases of an evacuation. We test the potential of open data in this paper. They can be used without legislative restrictions and for free. We choose the region around nuclear power plant Dukovany (Czech Republic) for the pilot study. Handbook for public in the case of radiation accident already exists and describes the key phases like evacuation assembly areas, evacuation routes and safe area. But the potential of cartographic visualization is not used therein. We provided two examples of maps based on open data for the evacuation mechanism improvement. We created a map showing the actual number of inhabitants based on statistical data around the power plant as the first use case. This map can be used during evacuation planning. Second use case demonstrates possibility of evacuation route visualization. It can be used for navigation during evacuation. Proposed cartographic visualization and their possible extensions are discussed for both use cases. The advantages, limitations and future development are commented in the conclusion.

In the context of evacuation planning at a strategic level, an efficient model is proposed to augment network capacity under a budget constraint so as to enhance operational performance under a security threat. Contraflow strategies and lane additions are considered for capacity augmentation. The corresponding mathematical programming model is obtained through a case-specific graph theoretic transformation of Daganzo's cell transmission model, exploiting problem characteristics to enhance computational efficiency. This paper focuses on experiments and sensitivity analyses to provide insights: (1) on the computational efficiency of the proposed model; and (2) for strategies for evacuation planning and operations. Sensitivity analyses are performed for a test network in terms of the budget for contraflow operations, the evacuee population size, and the amount and spatial distribution of origin-destination (O-D) demand. The results suggest that there is a threshold budget beyond which benefits, in terms of the network clearance time, are negligible. They also indicate that the network clearance time varies linearly with uniformly distributed population size. Finally, greater spatial uniformity of O-D demand reduces clearance times, suggesting that specifying multiple destinations in the safety zone may lead to more efficient evacuation in many instances. From a response standpoint, the study suggests that operational effectiveness requires predetermined contraflow strategy plans with designated and well-trained personnel, rather than just reacting to a security event in real-time.

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.

Increasing disasters in recent years have necessitated the development of emergency logistics plans. Evacuation planning plays an important role in emergency logistics management, particularly when it comes to addressing transit-dependent populations that are often neglected in previous studies. This systematic literature review explores the current state of transit-based evacuation planning and examines the current gaps. We focused on transit-based evacuation planning problems that used optimisation and modelling approaches. This review conducts an extensive analysis of relevant studies to provide a comprehensive overview, identify research gaps, and outline future directions in the evacuation planning body of knowledge. Using an integrated systematic review methodology, a thorough search of the Scopus and Web of Science databases was conducted, resulting in a total of 538 articles. These articles were screened and evaluated based on predetermined inclusion and exclusion criteria, ultimately yielding 82 studies for final analysis. The findings highlight the growing importance of optimisation and modelling approaches within transit-based evacuation planning. Studies emphasize the integration of public transportation networks into evacuation strategies to enhance operational efficiency, optimize resource allocation, and ensure evacuee safety. Transit-based evacuation planning is vital for both those without personal vehicles, making evacuation more equitable, and vehicle owners, particularly in earthquakes where vehicles might be inaccessible or trapped, demonstrating its wide usefulness in all emergency scenarios. Various optimisation and modelling approaches have been employed in transit-based evacuation planning studies to simulate and analyse the flow of evacuees and vehicles during emergencies. Transit-based evacuation planning exhibits unique characteristics within disaster management, including the consideration of spatial and temporal dynamics of transit systems, integration of social and demographic factors, and involvement of multiple stakeholders. Spatial and temporal dynamics encompass transportation schedules, capacities, and routes, while social and demographic factors involve variables such as income, age, and mobility status. Stakeholder engagement facilitates collaborative decision-making and effective plan development. However, transit-based evacuation planning faces challenges that require further research and development. Data availability and accuracy, model validation, stakeholder coordination, and the integration of uncertainty and dynamic factors pose significant hurdles. Addressing these challenges necessitates advances in data collection, robust modelling frameworks, and improved communication and coordination mechanisms among stakeholders. Addressing these gaps requires interdisciplinary collaborations and advances in data analytics and modelling techniques.

Traffic evacuation is one of the most important tasks in emergency management, and it is challenging for governments to plan an efficient and safe evacuation before a huge disaster strikes. This paper presents a traffic evacuation simulation system that generates agent's driving behavior based on multi-level driving decision models. The agent's driving behavior combines multiple widely used behavior models from each decision level. The agent-based traffic evacuation system is proposed and a prototype system implements each agent's multi-level modular driving decision models. The simulation experiment studies show varied clearance time, evacuation rates per shelter, and the variety and number of traffic jams to support traffic evacuation planning decisions in a crowded city liked Beijing, China. The simulation studies compare the existing evacuation plan with other simulated plans and evaluate it for designing a better and more realistic traffic evacuation plan.