Reliability based pre-positioning of recovery centers for resilient transportation infrastructure

Soltani-Sobh, Ali; Heaslip, Kevin; Scarlatos, Panagiotis D.; Kaisar, Evangelos I.

doi:10.1016/j.ijdrr.2016.09.004

Cited by 43 publications

(19 citation statements)

References 37 publications

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“…Farahani et al [26] defined the backup service as a situation where facilities other than the primary one can be use to serve a demand when needed. Soltani-Sobh et al [27] presented a facility location model to determine recovery centers by considering the failure possibilities of network links and facilities. e primary and backup recovery centers are determined based on the reliable path instead of the shortest path.…”

Section: Q-coverage Location Modelsmentioning

confidence: 99%

Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

Liu

2020

Journal of Advanced Transportation

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The reasonable location of emergency facilities plays an important role in both predisaster service and postdisaster relief. Moreover, damage to the transportation network often affects the accessibility of demand points, which can seriously hamper timely rescue operations. Reasonable location of emergency facilities and reinforcement of fragile roads are two important strategies to improve the reachability of demand points. In this paper, we proposed a biobjective optimization model to determine locations of emergency facilities and links to be reinforced given a limited budget. Each demand point is allocated a primary facility and a backup facility, the former can provides normal service, and the latter is prepared for postdisaster relief. One goal of the model is to minimize the operating cost of normal services, and another goal is to maximize the reachability guarantee of demand points. The novelty and contribution of this paper are that we defined the reachability by introducing damage tolerance instead of link failure probability. Based on this, we defined the reachability guarantee to deal with the worst scenario of disasters. By embedding the max-flow problem of the reachability guarantee into the emergency facility location problem, the locations of emergency facilities and links to be reinforced can be determined simultaneously. The methodology is applied to a simplified Sioux Falls transportation network. Results such as the trade-off curve of two goals, budget efficiency, and the effect of reinforcement demonstrated the effectiveness of the model.

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Section: Q-coverage Location Modelsmentioning

confidence: 99%

Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

Liu

2020

Journal of Advanced Transportation

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“…Network connections can be transformed to a mathematical graph model. System properties and interactions between individual components could be assessed by mathematical algorithms from graph theory [14][15][16]26]. When displaying the existing situation and analysing results, there are often used Geographic Information System (GIS) tools [7-10, 14, 15, 18, 28].…”

Section: Possibilities For Assessment Of Urban System Resilience To Nmentioning

confidence: 99%

“…Most current investigations are focused on the socio-economic vulnerability of the urban system [3,[5][6][7][8][9] or structural weakness of its physical components [10][11][12][13][14][15][16]. On the other hand, there are few studies that treat the urban system as a whole.…”

Section: Introductionmentioning

confidence: 99%

Proposal for Holistic Assessment of Urban System Resilience to Natural Disasters

Koren

Kilar

Rus

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Urban system is a complex mix of interdependent components and dynamic interactions between them that enable it to function effectively. Resilience of urban system indicates the ability of a system to resist, absorb, accommodate to and recover from the effects of a hazard in a timely and efficient manner. In the relevant literature, most studies consider individual components separately. On the other hand, the purpose of this paper is to assess the urban system as a whole, considering all relevant components and their interactions. The goal is a study of possibilities for holistic evaluation of urban system resilience to natural disasters. Findings from the preliminary study are presented: (i) the definition of urban system and categorization of its components, (ii) a set of attributes of individual components with impact on disaster resilience of the entire system and (iii) review of different methods and approaches for resilience assessment. Based on literature review and extensive preliminary studies a new conceptual framework for urban resilience assessment is proposed. In the presented paper, a conceptual model of urban system by abstraction of its components as nodes (buildings), patches -specific nodes with spatial properties (open space), links (infrastructures) and base layer (community) is created. In the suggested model, each component is defined by its own quantitative attributes, which have been identified to have an important impact on the urban system resilience to natural disasters. System is presented as a mathematical graph model. Natural disaster is considered an external factor that affects the existing system and leads to some system distortion. In further analyses, mathematical simulation of various natural disasters scenarios is going to be carried out, followed by comparison of the system functionality before and after the accident. Various properties of the system (accessibility, transition, complexity etc.) are going to be analysed with graph theory. The final result is going to be an identification of critical points and system bottlenecks as basis for further actions of risk mitigation.

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“…Generally, the travel time that related to disaster in past studies were generated by variety of methods, such as shortest path [29], statistical sampling [23], and four-step transportation model under travel demand sampling [30]. The statistical sampling methods were applied in case of uncertain data and not enough statistical data in the study area.…”

Section: Introductionmentioning

confidence: 99%

A Modeling Framework of Hierarchical Earthquake Relief Center Locations Under Demand Uncertainty

Pochan¹

2020

GEOMATE

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This paper presents a modeling framework for locating earthquake disaster relief centers, developed through multidimensional and integrated perspectives of transportation engineering and humanitarian logistics planning. The proposed framework consists of three sequential steps: 1) Seismic risk analysis, to evaluate the vulnerability of both road network and area-covering disruptions, using the spaghetti and meatballs method, which is a geographic information system (GIS) based analytical approach enriched with historical earthquake statistics and earthquake fault data; 2) Travel demand analysis, to forecast travel demand, travel behavior, travel pattern, and traffic volume, using the four-step transportation model developed based on field traffic survey data and seismic risk analysis data; and 3) Facility location problem analysis, to locate optimal earthquake disaster relief centers, using the hierarchical location problem model formulated based on a meta-heuristic genetic algorithm (GA) optimization technique. To evaluate model mechanism and performance, a preliminary model was then applied to the simulated geographical area with simulated road network of Chiang Rai Province, Thailand. As a result, two-level hierarchical disaster relief centers in response to earthquakes, taking into account accessibility and functional ability of transportation networks, risk covered/uncovered demand and supply distribution, can be viably determined. In the upperlevel, a central disaster relief center is functioned to collect rescue equipment and survival bags from both inside and outside the area and to distribute those to local disaster relief centers in the area. In the lowerlevel, three local disaster relief centers were optimally located, functioned to receive supplies from the central disaster relief center and then distribute to demand points affected by earthquakes. The resulting model can provide government and related agencies profound information in planning and developing either pre-disaster or post-disaster operations.

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Reliability based pre-positioning of recovery centers for resilient transportation infrastructure

Cited by 43 publications

References 37 publications

Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

Proposal for Holistic Assessment of Urban System Resilience to Natural Disasters

A Modeling Framework of Hierarchical Earthquake Relief Center Locations Under Demand Uncertainty

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