Evacuation Route Planning Research Articles

복합건축의 보행량 분석을 통한 피난동선계획

복합건축의 보행량 분석을 통한 피난동선계획

Toward a Simulation-based Approach for Emergency Evacuation Route Planning in Metro Stations

This paper presents a systematic simulation-based approach with detailed step-by-step procedures for route planning in emergency evacuation in metro stations. In accordance with emergency evacuation mechanism analysis, the length of evacuation route (L), the time of evacuation (T) and the density of pedestrian flow (D) are identified as critical factors that affect the performance of emergency evacuation. With all the critical factors, such as L, T and D, taken into account, a comprehensive multi-attribute decision algorithm is developed in order to optimize the selection of evacuation route under emergency. Taking the Hongshan Station in Wuhan metro systems as an example, the Anylogic tool is used to simulate the scenario of evacuation route planning in case of a fire. The simulation results regarding the evacuation performance indicators are analyzed and compared between the traditional and proposed approaches. Results indicate that the proposed approach can reduce the evacuation time without increasing the route length, and improve greatly the crowded conditions of pedestrian flow. The developed approach can provide guidelines and support for the optimization of evacuation route planning under emergency conditions.

Evacuation Route Optimization based on Tabu Search Algorithm and Hill-climbing Algorithm

An optimization model for network evacuation route planning is constructed by taking into consideration the strategies of using reverse lane and eliminating intersection conflicts to minimize the total evacuation time in the dangerous areas. The optimization model is a bi-level model. The upper-level is to find optimized sub-network configuration of roadway section with lane reversal by the tabu search algorithm; the lower-level uses the hill-climbing algorithm to get the optimal routing plan with intersection crossing conflicts elimination. In order to prove the validity of the model and algorithm, this paper uses a simple example to illustrate its advantages.The calculation result shows that to solve the problem of evacuation route, the optimization algorithm designed in this paper not only can achieve an excellent computing result, but also has a better computational efficiency, a faster convergence rate and a relatively stable computing result.

Building-evacuation-route planning via time-expanded process-network synthesis

A method and software are proposed for optimal building-evacuation-route planning in terms of identifying evacuation routes and scheduling of evacuees on each route. First, the building-evacuation routes are represented by a P-graph, which gives rise to a time-expanded process-network synthesis (PNST) problem that can be algorithmically solved by the P-graph framework. In the proposed method, each location and passage in the building is given by a set of attributes to be taken into account in the evacuation-route planning. The evacuation time is calculated as a minimum cost of the corresponding PNST. In addition to the globally optimal solution, the P-graph framework provides the n-best sub-optimal solutions. The validity of the proposed method is illustrated by two examples.

Investigation on an Integrated Evacuation Route Planning Method Based on Real-Time Data Acquisition for High-Rise Building Fire

Evacuation of occupants in high-rise buildings is one of the most important tasks when the building is subjected to a significant level of fire threat. Since the mechanism of fire spread is too complicated for the trapped people to determine the evacuation route, a real-time evacuation route planning method is needed for rescue and safety management. In this paper, an integrated real-time evacuation route planning method for high-rise building fires is proposed. This method is composed of real-time data acquisition, risk distribution calculation, and evacuation route formulation. The real-time data acquisition process is achieved by using the sensor system and the wireless data transmission system, the calculation of risk distribution is based on the risk assessment of casualties, and the formulation of evacuation route is based on the fast marching level set method. To demonstrate the presented method, a sample building under an assumed fire is used for numerical investigation, and a real building model is used for experimental investigation. The results show that this method can evaluate the fire status and formulate evacuation strategies instantly in high-rise building fire. The risk distribution and the evacuation route of the building fire can be intuitively displayed. This method can be used in practical applications and will be an important technical basis for the program development of rescue and evacuation.

Two-phase evacuation route planning approach using combined path networks for buildings and roads

This paper addresses the problem of modeling evacuation routes from a building and out of an affected area. The evacuation route involves pathways such as corridors, and stairs in buildings and road networks and sidewalks outside the building. To illustrate such an approach, we consider the problem of finding evacuation paths from an urban building and out of a predetermined neighborhood of the building on foot. A case study for a college campus building and small set of road around it is provided. There are a pre-defined set of exit points out of the target building and out of the road network serving the building. A two-step approach with an uncapacitated network model for route finding and a capacitated scheduling algorithm for evacuation time computation is proposed. A recent efficient heuristic algorithm is selected as a reference for comparative analysis. The process of creating a combined building and road path network data is discussed. The key results are the competitive evacuation time provided by the proposed uncapacitated route planning model, simple pedestrian flow capacity formulas for corridors and roads from readily available geometric data, and the illustration of the creation and use of combined building and road path network.

Evacuation Route Planning in Mount Gamalama, Ternate Island-Indonesia

Abstract Mount Gamalama (hereafter Mt. Gamalama) is similar to conical strato volcano type that comprises entire Ternate Island, which is one of the Indonesia's most actiive volcanoes. Hence, evacuation route planning is needed to reduce the number of victims, unfortunately, the local government does not have it yet until today. This paper aims to analyze the evacuation route planning in Ternate Island in terms of Mt.Gamalama eruption. The result of this study discovered two types evacuation routes which are overland evacuation route is analyzed using network analysis method and marine evacuation route which is analyzed using trip frequency method to another islands surrounding Ternate Island by ferry.

Experiences with evacuation route planning algorithms

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in the event of natural disasters. Hurricane Rita and the recent tsunami revealed limitations of traditional approaches to provide emergency preparedness for evacuees and to predict the effects of evacuation route planning (ERP). Challenges arise during evacuations due to the spread of people over space and time and the multiple paths that can be taken to reach them; key assumptions such as stationary ranking of alternative routes and optimal substructure are violated in such situations. Algorithms for ERP were first developed by researchers in operations research and transportation science. However, these proved to have high computational complexity and did not scale well to large problems. Over the last decade, we developed a different approach, namely the Capacity Constrained Route Planner (CCRP), which generalizes shortest path algorithms by honoring capacity constraints and the spread of people over space and time. The CCRP uses time-aggregated graphs to reduce storage overhead and increase computational efficiency. Experimental evaluation and field use in Twin Cities Homeland Security scenarios demonstrated that CCRP is faster, more scalable, and easier to use than previous techniques. We also propose a novel scalable algorithm that exploits the spatial structure of transportation networks to accelerate routing algorithms for large network datasets. We evaluated our new approach for large-scale networks around downtown Minneapolis and riverside areas. This article summarizes experiences and lessons learned during the last decade in ERP and relates these to Professor Goodchild's contributions.

A Method for Evacuation Route Planning in Disaster Situations

In this paper, the design and analysis of evacuation routes in transportation networks in cases of natural disasters is examined. A method for evacuation route planning in disaster situations is suggested. This method applies iteratively a heuristic algorithm to define two independent paths from the disaster area to each shelter for vehicle flow allocation in evacuation planning, considering both travelling time and capacity of the transportation network as parameter for analysis. Routes to different shelters cannot present intersection points either in order to allow continuous traffic flow and reduce potential accidents. An evacuation planning exercise is also presented.

Framework for Earthquake Evacuation Planning: Case Study for Montreal, Canada

AbstractDisaster management planning in Canada is government led, and the responsibilities are divided among federal, provincial, and local levels of government. The Civil Emergency Plan of Greater Montreal (Plan de securite civile de l’agglomeration de Montreal) describes operations and responsibilities during emergencies and provides a framework for the strategic management cycle, which includes action, coordination, and communication between various decision centers. However, evacuation planning processes have not been described for any of these levels of government. Recognizing the necessity for evacuation planning, this paper proposes a three-step framework for earthquake evacuation planning for Montreal: (1) vulnerability assessment of the built environment and community demographics, (2) shelter and evacuation route planning, and (3) preparation of community evacuation maps. This paper also acknowledges the need for participatory and advocacy planning during the preparation of community evacuation ...

A fast heuristic approach for large‐scale cell‐transmission‐based evacuation route planning

AbstractThe cell‐transmission‐model by Daganzo (1994) was introduced as a traffic simulation model. Its basic ideas were picked up recently to formulate optimization models for evacuation planning. These optimization models were capable of generating high quality evacuation plans, but computational effort and computational requirements, especially in dimensions of real‐world applications, were very high. To extend adaptability to much larger evacuation scenarios, a fast heuristic procedure will be presented. In a computational study, we will demonstrate the effectiveness of our approach in several real‐world scenarios of different road network size, population distribution, and risk distribution, respectively. © 2012 Wiley Periodicals, Inc. NETWORKS, 2012

Urgent Computing of Storm Surge for North Carolina's Coast

Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. This latter element is that which induces the need for urgency in this area. In this paper, we outline the North Carolina Forecasting System (NCFS) for storm surge and waves for coastal North Carolina, which is threatened by tropical cyclones about once every three years. We initially used advanced cyberinfrastructure techniques (e.g., opportunistic grid computing) in an effort to provide timely guidance for storm surge and wave impacts. However, our experience has been that a distributed computing approach is not robust enough to consistently produce the real-time results that end users expect. As a result, our technical approach has shifted so that the reliable and timely delivery of forecast products has been guaranteed by provisioning dedicated computational resources as opposed to relying on opportunistic availability of external resources. Our experiences with this forecasting effort is discussed in this paper, with a focus on Hurricane Irene (2011) that impacted a substantial portion of the US east coast from North Carolina, up along the eastern seaboard, and into New England.

An inexact optimization model for evacuation planning

Purpose – The purpose of this paper is to develop an interval method for vehicle allocation and route planning in case of an evacuation.Design/methodology/approach – First, the evacuation route planning system is described and the notations are defined. An inexact programming model is proposed. The goal of the model is to achieve optimal planning of vehicles allocation with a minimized system time under the condition of inexact information. The constraints of the model include four types: number of vehicles constraint, passengers balance constraints, maximum capacity of links constraints and no negative constraints. The model is solved through the decomposition of the inexact model. A hypothetical case is developed to illustrate the proposed model.Findings – The paper finds that the interval solutions are feasible and stable for evacuation model in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving evacuation managers' estimates under different conditions....

Contraflow Transportation Network Reconfiguration for Evacuation Route Planning

Given a transportation network having source nodes with evacuees and destination nodes, we want to find a contraflow network configuration, i.e., ideal direction for each edge, to minimize evacuation time. Contraflow is considered a potential remedy to reduce congestion during evacuations in the context of homeland security and natural disasters (e.g., hurricanes). This problem is computationally challenging because of the very large search space and the expensive calculation of evacuation time on a given network. To our knowledge, this paper presents the first macroscopic approaches for the solution of contraflow network reconfiguration incorporating road capacity constraints, multiple sources, congestion factor, and scalability. We formally define the contraflow problem based on graph theory and provide a framework of computational workload to classify our approaches. A greedy heuristic is designed to produce high quality solutions with significant performance. A bottleneck relief heuristic is developed to deal with large numbers of evacuees. We evaluate the proposed approaches both analytically and experimentally using real world datasets. Experimental results show that our contraflow approaches can reduce evacuation time by 40% or more.

EVACUATION ROUTE PLANNING: A CASE STUDY IN SEMANTIC COMPUTING

Semantic computing addresses the transformation of data, both structured and unstructured into information that is useful in application domains. One domain where semantic computing would be extremely effective is evacuation route planning, an area of critical importance in disaster emergency management and homeland defense preparation. Evacuation route planning, which identifies paths in a given transportation network to minimize the time needed to move vulnerable populations to safe destinations, is computationally challenging because the number of evacuees often far exceeds the capacity, i.e. the number of people that can move along the road segments in a unit time. A semantic computing framework would help further the design and development of effective tools in this domain, by providing a better understanding of the underlying data and its interactions with various design techniques. Traditional Linear Programming(LP) based methods using time expanded networks can take hours to days of computation for metropolitan sized problems. In this paper, we propose a new approach, namely a capacity constrained routing planner for evacuation route planning which models capacity as a time series and generalizes shortest path algorithms to incorporate capacity constraints. We describe the building blocks and discuss the implementation of the system. Analytical and experimental evaluations that compare the performance of the proposed system with existing route planners show that the capacity constrained route planner produces solutions that are comparable to those produced by LP based algorithms while significantly reducing the computational cost.