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Evacuation shelter and route selection based on multi-objective optimization approach
Abstract
Evacuation shelter and route selection are indispensable parts of emergency planning. An efficient plan can effectively evacuate people from a dangerous place to a safer location. Meanwhile, it is crucial to improve risk management. This article presents a multi-objective optimization algorithm based on social media and GIS to optimize shelter usage and personnel distribution considering social relationship. The proposed algorithm is examined using a case study for the Zhongguancun district in Beijing. The district is modeled by the graph theory. Traffic route data and population distribution are synthesized to form a risk map. Furthermore, the Floyd-Warshall algorithm is adopted to find a shortest path route in order to relocate evacuees to a safer place effectively. Efficiency, fairness and social relation are taken into consideration in this context.
... Two main lines of research focus on: (1) traic monitoring at an aggregate level, e.g., to help city administration, and (2) services that road users are getting. Existing work towards traic monitoring include monitoring congestion [129], assessing the safety of roads and intersections [143], traic prediction [132], evacuation routing [265], optimizing the public transportation schedules [193]. Eforts on the services provided to road users include routing queries that balance the traic across roads [69], help ind drivers inding nearest facilities [121], personalized routing [130], eco-routing for minimizing greenhouse emissions [134], and enabling multi-modal trip planning [225]. ...
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art, and describe open challenges for the research community in the coming years.
... The algorithm was developed in Cþþ and compared with other Dijkstra-based algorithms, showing increased efficiency in dynamic or real-time applications. The study by Zhang et al. (2015) explored the complexities of evacuation shelter and route selection through a multi-objective optimization algorithm. The research integrated Geographic Information System (GIS) and social media data to optimize shelter usage and personnel distribution. ...
This study introduces a groundbreaking application of the Bellman-Ford algorithm for optimizing evacuation routes in multi-floor academic buildings, extending its traditional use in single-source shortest-path problems to address complex multiple- source multiple-exit (MSME) problems. A comprehensive computational model was developed, reflecting real-world evacuation scenarios and incorporating key constraints and assumptions. The model was rigorously benchmarked against a Dijkstra’s algorithm-based model, revealing a 3.5% improvement in the number of evacuees evacuated after the initial 9 seconds. Detailed simulation results and extended data analysis further substantiate these findings. While the current model assumes perfect evacuee compliance and overlooks human behavior, future research could address these limitations to enhance the model’s realism. This study significantly advances the field of emergency evacuation planning, offering valuable insights for emergency response practitioners, facility managers, and policymakers.
... Two main lines of research focus on: (1) traffic monitoring at an aggregate level, e.g., to help city administration, and (2) services that road users are getting. Existing work towards traffic monitoring include monitoring congestion [177], assessing the safety of roads and intersections [198], traffic prediction [166,181,232], evacuation routing [219,363], optimizing the public transportation schedules [256]. Efforts on the services provided to road users includes routing queries that balance the traffic across roads [71,92], help find drivers finding nearest facilities [163,275], personalized routing [178], eco-routing for minimizing greenhouse emissions [19,183], and enabling multi-modal trip planning [304]. ...
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.
... In order to reduce rescue decision time and improve rescue efficiency, it is necessary to study the optimal emergency resource scheduling method. Many scholars study emergency material scheduling problem [3,4,11,26,27]. In earlier studies, the problem was turned into solving the shortest path problem [12,17]. The scheduling problem considering potential accident resources received more attention after Sherali and Subramanian [19] Recently, multi-objective emergency resource scheduling model research has received more attention. ...
In recent years, frequent public emergencies have resulted in heavy casualties and economic losses. In the early stage of emergency rescue, the demand for emergency resources is strictly greater than the supply. As an important part of emergency management, emergency resource scheduling is an important manifestation of its rescue value. At present, Many studies take emergency resource scheduling as a multi-objective optimization problem. They focus on the total emergency cost and does not evaluate the emergency resource allocation according to the actual disaster severity of each emergency demand point. What's more, they do not consider road information. However, in actual, the condition of roads directly affects the emergency cost. To address this problem, we proposed an effective emergency logistics scheduling model based on multi-objective optimization algorithms. We get road information from spatial-temporal trajectory data. The research on emergency resource scheduling in this paper is helpful to achieve greater utility under the condition of limited emergency resources and reduce the loss of public emergencies.
In recent years, online volunteers have played important roles in disaster response. After a major disaster, hundreds of volunteers are often remotely convened by humanitarian organizations to map the affected area based on remote sensing images. Typically, the affected area is divided using a grid-based tessellation, and each volunteer can select one grid cell to start mapping. While this approach coordinates the efforts of volunteers, it does not differentiate the priorities of different cells. As a result, volunteers may map grid cells in a random order. Due to the spatial heterogeneity, different cells may contain geographic information that is of different value to emergency responders. Ideally, cells that potentially contain more valuable information should be assigned higher priority for mapping. This article presents an analytical framework for prioritizing the mapping of cells based on the values of information contained in these cells. Our objective is to provide guidance for online volunteers so that potentially more important cells are mapped first. We present a method that is based on information value theory and focus on road networks. We apply this method to a number of simulated scenarios and to a real disaster mapping case from the 2015 Nepal earthquake.
This article presents a social network based methodology for improving urban traffic evacuation capability in emergency response by using smart phones. Considering destructive and unexpected characteristics of natural hazards or man-made disasters, the effected people cannot evacuate efficiently when they face the unknown impacts of these events. Therefore, a smart phone application, called EMAPP, is developed to tackle this difficulty. An overall description including design architecture and system modules is provided. Optimal shelter choice strategy is given by using the modified Dijkstra’s algorithm. The Nagel-Schreckenberg model loaded by vehicle arrival rate is implemented to produce the spatio-temporal diagrams of the vehicles. Both traffic congestion and cascading failure during real-time emergency evacuation are analyzed. The main issues are devoted to link the affected people, geographic information and traffic conditions through the smartphone application. Therefore, in emergency evacuation, the affected people can receive mutual help and urban resilience can be enhanced.
This paper explains a modeling approach that offers better understanding of the routing strategies taken by evacuees to reach a safe destination during hurricane evacuation. Route choice during evacuation is a complex process because evacuees may prefer to take the usual or familiar route on the way to the destination, or they might follow the routes recommended by the emergency officials. Depending on the condition of the traffic stream, sometimes they might switch to a different route to obtain better travel time from the one initially attempted, i.e.,the routing behavior is random. By using data from Hurricane Ivan, a mixed (random parameters) logit model is estimated which captures the decision making process on what type of route to select while accounting for the existence of unobserved heterogeneity across households. Estimation findings indicate that the choices of evacuation routing strategy involve a complex interaction of variables related to household location, evacuation characteristics, and socioeconomic characteristics. The findings of this study are useful to determine the manner in which different factions of people select a type of route for a given sociodemographic profile during an evacuation.
The Protective Action Decision Model (PADM) is a multistage model that is based on findings from research on people's responses to environmental hazards and disasters. The PADM integrates the processing of information derived from social and environmental cues with messages that social sources transmit through communication channels to those at risk. The PADM identifies three critical predecision processes (reception, attention, and comprehension of warnings or exposure, attention, and interpretation of environmental/social cues)— that precede all further processing. The revised model identifies three core perceptions— threat perceptions, protective action perceptions, and stakeholder perceptions—that form the basis for decisions about how to respond to an imminent or long-term threat. The outcome of the protective action decision-making process, together with situational facilitators and impediments, produces a behavioral response. In addition to describing the revised model and the research on which it is based, this article describes three applications (development of risk communication programs, evacuation modeling, and adoption of long-term hazard adjustments) and identifies some of the research needed to address unresolved issues.
The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management.l
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.
Recent natural or man-made disasters around the world have provided compelling evidence that transportation system plays a crucial role in the emergency evacuation and have stressed the need for effective evacuation traffic management to maximize the utilization of the transportation system and to minimize fatalities and losses. This paper presents a model reference adaptive control MRAC framework for real-time traffic management under emergency evacuation. Distinct from the well-studied evacuation planning, real-time traffic management for evacuation aims to dynamically guide control traffic flow under evacuation in such a way that certain system objective e.g., minimization of fatalities or property losses could be achieved. The proposed framework is based on both dynamic network modeling techniques and adaptive control theory, by considering the traffic network under evacuation as a dynamic system. First, a prescriptive dynamic traffic assignment model is applied to predict, in a short-term and rolling-horizon manner, the desired traffic states based on certain system optimal objectives. This model will serve as a reference point for the adaptive control. Then, the adaptive control system integrates these desired states and the current prevailing traffic conditions collected via the sensing system to produce real-time traffic control schemes. Finally, these traffic control schemes are implemented in the field to guide the real-world traffic flow to evolve towards the desired states. Simulation studies provided in this paper show that the proposed framework based on MRAC can significantly improve the performance of real-time evacuation traffic management.
A computerized simulation model for capturing human behavior during flood emergency evacuation is developed using a system dynamics approach. It simulates the acceptance of evacuation orders by the residents of the area under threat; number of families in the process of evacuation; and time required for all evacuees to reach safety. The model is conceptualized around the flooding conditions (physical and management) and the main set of social and mental factors that determine human behavior before and during the flood evacuation. The number of families under the flood threat, population in the process of evacuation, inundation of refuge routes, flood conditions (precipitation, river elevation, etc.), and different flood warnings and evacuation orders related variables are among the large set of variables included in the model. They are linked to the concern that leads to the danger recognition, which triggers evacuation decisions that determine the number of people being evacuated. The main purpose of the model is to assess the effectiveness of different flood emergency management procedures. Each procedure consists of the choice of flood warning method, warning consistency, timing of evacuation order, coherence of the community, upstream flooding conditions, and set of weights assigned to different warning distribution methods. Model use and effectiveness are tested through the evaluation of the effectiveness of different flood evacuation emergency options in the Red River Basin, Canada.
Although there is a substantial amount of research on households’ hurricane evacuation decision making, there is much less
research on the logistical issues involved in implementing those evacuations. The limited research on household evacuation
logistics has consistently shown that most evacuees stay in the homes of friends and relatives or in commercial facilities
rather than in public shelters. However, evacuation logistics—which can be defined as the activities and associated resources
needed to reach a safe location and remain there until it is safe to return—encompasses a much broader range of behaviors
than this. The present study extends previous research by reporting data on other aspects of evacuation logistics such as
departure timing, vehicle use, evacuation routes, travel distance, shelter type, evacuation duration, and evacuation cost.
Hurricane Lili evacuation data at the county level are generally consistent with the data from previous hurricanes, but there
is notable variation across counties studied here. There were only modest correlations of demographic and geographic variables
with the evacuation logistics variables, a result that indicates further research is needed to better understand what happens
between the time an evacuation decision is made and the time re-entry is begun. Moreover, research is needed to understand
the logistics of evacuation by special populations such as transients and households with disabled members.
KeywordsHurricanes–Evacuation–Traffic management
The focus of this paper is on the development of a methodology to identify network and demographic characteristics on real transportation networks which may lead to significant problems in evacuation during some extreme event, like a wildfire or hazardous material spill. We present an optimization model, called the critical cluster model, that can be used to identify small areas or neighborhoods which have high ratios of population to exit capacity. Although this model in its simplest form is a nonlinear, constrained optimization problem, a special integer-linear programming equivalent can be formulated. Special contiguity constraints are needed to keep identified clusters spatially connected. We present details on how this model can be solved optimally as well as discuss computational experience for several example transportation networks. We describe how this model can be integrated within a GIS system to produce maps of evacuation risk or vulnerability. This model is now being utilized in several research projects, in Europe and the US.
Most traffic delays in regional evacuations occur at intersections. Lane-based routing is one strategy for reducing these delays. This paper presents a network flow model for identifying optimal lane-based evacuation routing plans in a complex road network. The model is an integer extension of the minimum-cost flow problem. It can be used to generate routing plans that trade total vehicle travel-distance against merging, while preventing traffic crossing-conflicts at intersections. A mixed-integer programming solver is used to derive optimal routing plans for a sample network. Manual capacity analysis and microscopic traffic simulation are used to compare the relative efficiency of the plans. An application is presented for Salt Lake City, Utah.
The Protective Action Decision Model (PADM) is a multistage model that is based on findings from research on people's responses to environmental hazards and disasters. The PADM integrates the processing of information derived from social and environmental cues with messages that social sources transmit through communication channels to those at risk. The PADM identifies three critical predecision processes (reception, attention, and comprehension of warnings or exposure, attention, and interpretation of environmental/social cues)--that precede all further processing. The revised model identifies three core perceptions--threat perceptions, protective action perceptions, and stakeholder perceptions--that form the basis for decisions about how to respond to an imminent or long-term threat. The outcome of the protective action decision-making process, together with situational facilitators and impediments, produces a behavioral response. In addition to describing the revised model and the research on which it is based, this article describes three applications (development of risk communication programs, evacuation modeling, and adoption of long-term hazard adjustments) and identifies some of the research needed to address unresolved issues.
Purpose : This paper aims to develop and verify a framework of logistics aspects for flood emergency planning. Design/methodology/approach : Provides a topical review of academic literature, illustrative case study and structured interviews in practice. Findings : Creates and details a framework for logistics flood emergency planning on four aspects: demand management, supply management, inventory management and resource management. Research limitations/implications : The paper is empirical in nature and based on an investigation within one country (the Netherlands) that is considered an expert in this area; the framework requires international testing. Practical implications : The framework presented provides practitioners with a tool for logistics flood emergency planning and highlights essential elements of these plans. Originality/value :The field of humanitarian logistics so far has received limited attention by logistics academics and there is a strong need for empirical research in this area. This paper provides the results of an empirical investigation into a highly
For those workers in GIS with limited operational research experience, but now considering optimum use of network algorithms, the paper discusses two such algorithms. The performance of the "out-of-killer' algorithm in an emergency planning context is examined, with particular reference to population evacuation. -Authors
Responding to hurricanes is an exceedingly complex task, the effectiveness of which can significantly influence the final effects of a hurricane. Despite a lot of progress, recent events and unchecked population growth in hurricane-prone regions make it clear that many challenges remain. Hurricane Katrina has shown that having appropriate shelter options and an appropriate shelter evacuation plan are very important for hurricane evacuations. This paper proposes a scenario-based shelter location model for optimizing a set of shelter locations among potential alternatives that are robust across a range of hurricane events. This model considers the influence of changing the selection of shelter locations on driver route-choice behavior and the resulting traffic congestion. The state of North Carolina is used as a case study to show the applicability of the model. DOI: 10.1061/(ASCE)IS.1943-555X.0000067. (C) 2011 American Society of Civil Engineers.
In this research, the factors affecting route choice in hurricane evacuation are investigated. Two hypotheses are tested. First, it is hypothesized that contrary to common practice where travel time is considered the sole or main determinant of route choice, other variables such as familiarity with the route, availability of fuel and shelter, road type, and accessibility of the route have an effect on an evacuees' route choice as well. The second hypothesis is that as time passes and storm conditions change, the impact each variable has on route choice changes. That is, it is hypothesized that the importance evacuees assign to the factors determining route choice is not static but varies with time. The logit structure was used for modeling the choice process and stated choice data previously collected from the New Orleans area on hypothetical storms was used to calibrate the model. The study found that accessibility to a route, familiarity with the route, road type, length of a route, and availability of services (gas stations and hotels) had an effect on evacuation route choice. The magnitude of the coefficients of perceived service, accessibility, and distance differed among those evacuating in the first half of the evacuation period versus those that evacuated in the second half, but coefficients of road type were not significantly different between the two time intervals. Observed traffic count data from hurricane Katrina evacuation were used to validate the model. Comparison of traffic volumes predicted by the model with actual traffic volumes from hurricane Katrina shows error percentages of 17.5, 0.01, and 28% of error for volumes on I-10, I-55, and US-61 respectively.
Very high traffic volumes may lead to extensive congestion during hurricane evacuations. Evacuation planners reduce this congestion by careful planning for multiple hurricane scenarios and assignment of evacuation routes and timing. This planning may be for naught if obstructions block key roadways. An advanced traveler information system (ATIS) may be used to guide evacuees to alternate routes, but how effective will that guidance be? Should the use of alternate routes be encouraged? How are drivers likely to respond to delays and information? Will information shorten or improve the reliability of travel times in emergency conditions? Integration of a dynamic evacuation simulation and a decision-making model (representative of the decisions made by potential hurricane evacuees when provided with information on downstream traffic congestion and alternate routes) can help emergency planners prepare for the unexpected. Advance modeling of likely accident locations and the severity can forecast the effects of alternate route use, help determine the best locations and timing of alternate route information, and support decision making. This study integrated an evacuee route choice decision model and a mesoscopic evacuation transportation simulation for southeastern Virginia. Study results show how the effects of ATIS can be tested in advance, thus allowing more comprehensive planning by emergency management and transportation professionals. Simulations of ATIS' effectiveness in evacuation scenarios have been largely unexplored. Methods presented can be applied in a variety of evacuation scenarios and may be of particular value to emergency planners.
Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority’s point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the flow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the importance of the number and locations of shelter sites and the trade-off between efficiency and fairness.
Two network flow methods are presented in this paper to optimize a city emergency evacuation plan. The problem is to assign each resident of the city to one of the places of refuge (PR) in preparation for major disasters. We model the city as an undirected graph, and by solving a shortest path problem on this graph, we obtain the shortest evacuation plan. The second model takes the capacity limit on each PR explicitly into account. The problem can then be transformed into a minimal cost flow problem on a slightly modified graph. We can evaluate the efficiency of the current city evacuation plan by comparing this against the optimal solutions of the above stated problems. Also, various pieces of information obtainable from these solutions can be utilized in evaluating the current evacuation policy. In addition, sensitivity analysis can be performed to answer various what-if questions
A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.
This paper deals with route choice models capturing travelers’ strategic behavior when adapting to revealed traffic conditions en route in a stochastic network. The strategic adaptive behavior is conceptualized as a routing policy, defined as a decision rule that maps from all possible revealed traffic conditions to the choices of next link out of decision nodes, given information access assumptions. In this paper, we use a specialized example where a variable message sign provides information about congestion status on outgoing links. We view the problem as choice under risk and present a routing policy choice model based on the cumulative prospect theory (CPT), where utility functions are nonlinear in probabilities and thus flexible attitudes toward risk can be captured.In order to illustrate the differences between routing policy and non-adaptive path choice models as well as differences between models based on expected utility (EU) theory and CPT, we estimate models based on synthetic data and compare them in terms of prediction results. There are large differences in path share predictions and the results demonstrate the flexibility of the CPT model to represent varying degrees of risk aversion and risk seeking depending on the outcome probabilities.
In this paper, the optimal design and analysis of evacuation routes in transportation networks is examined. An methodology for optimal egress route assignment is suggested. An integer programming (IP) formulation for optimal route assignment is presented, which utilizes M/G/c/c state dependent queueing models to cope with congestion and time delays on road links. M/G/c/c simulation software is used to evaluate performance measures of the evacuation plan: clearance time, total travelled distance and blocking probabilities. Extensive experimental results are included.
In an emergency situation, evacuation is conducted in order to displace people from a dangerous place to a safer place, and it usually needs to be done in a hurry. It is necessary to prepare evacuation plans in order to have a good response in an emergency situation. A central challenge in developing an evacuation plan is in determining the distribution of evacuees into the safe areas, that is, deciding where and from which road each evacuee should go. To achieve this aim, several objective functions should be brought into consideration and need to be satisfied simultaneously, though these objective functions may often conflict with each other.This paper aims to address the use of multiobjective evolutionary algorithms (MOEA) and the geographical information system (GIS) for evacuation planning. The paper proposes a three-step approach for evacuation planning. It explains that the last step, which corresponds to distribution of evacuees into the safe areas, is a spatial multiobjective optimization problem (MOP), because the objective functions and data required for solving the problem has a spatial component. To solve the MOP, two objective functions are defined, different algorithms for solving the problem are investigated, and the proper algorithm is selected. Finally, in the context of a case study project and based on the proposed approach and algorithm, evacuation planning is conducted in a GIS environment, and the results are tested. This paper is based on an ongoing research project in Iran.
Model Reference Adaptive Control Framework for Real-Time Traffic Management under Emergency Evacuation
- H X Liu
- Liu H. X.
Bilevel Optimization for Integrated Shelter Location Analysis and Transportation Planning for Hurricane Events
- A C Y Li
- Li A. C. Y.