Trends and gaps in the literature of road network repair and restoration in the context of disaster response operations

Almeida, Luana Souza; Goerlandt, Floris; Pelot, Ronald

doi:10.1016/j.seps.2022.101398

Cited by 10 publications

(10 citation statements)

References 63 publications

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“…Thus, the intensity of the edge is assumed to be the probability calculated in its midpoint. For example, if the intensity of the midpoint of edge [1 , 2] is 0.06, then edge (1,2) has intensity of 0.06. The relevant parameters the mechanism to determine what roads in the network are blocked, are specified in Table 3 .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The instances can be used by researchers in the field of Operations Research and Computer Science working with road network repair and restoration problems. As discussed in a recent review [2] , researchers in the field have been developing and testing models on different instances. Therefore, it is not possible to compare the performance (i.e.…”

Section: Value Of the Datamentioning

confidence: 99%

“…As discussed in a recent review [2] , researchers in the field have been developing and testing models on different instances. Therefore, it is not possible to compare the performance (i.e.…”

Section: Value Of the Datamentioning

confidence: 99%

See 2 more Smart Citations

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Almeida

Kodali²,

Goerlandt³

2022

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Value Of the Datamentioning

confidence: 99%

See 1 more Smart Citation

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Almeida

Kodali²,

Goerlandt³

2022

Data in Brief

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“…As stated in Souza Almeida et al. [2] , there are no datasets that enable such applications in real-world contexts. The proposed database is not recommended to be used for prediction purposes.…”

Section: Value Of the Datamentioning

confidence: 99%

“…As stated in Souza Almeida et al. [2] , there are no datasets that enable such applications in real-world contexts.…”

Section: Value Of the Datamentioning

confidence: 99%

Datasets of disrupted transportation networks on Canada's West Coast in a plausible M9.0 Cascadia Subduction Zone earthquake scenario

et al. 2023

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Flood risk assessment in Kogi State Nigeria through the integration of hazard and vulnerability factors

Aladejana,

Ebijuoworih

2024

Discov Geosci

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Annually, Kogi State in Nigeria experiences significant flooding events, leading to serious fatalities, the destruction of livelihoods, and damage to vital infrastructure. This study presents a multi-faceted approach and methodology to generate a state-wide flood risk map by analyzing both flood vulnerability and flood hazard factors. Seven flood hazard factors (drainage length, distance to river, elevation, slope, rainfall, distance from confluence/dam area, and geomorphology) and vulnerability factors (population density, female population, land cover, road length, distance from hospitals, literacy rate, and employment rate) were ranked and weighted based on their contributions to flooding within the state using the Fuzzy Analytical Hierarchy Process (FAHP). From these, the Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were derived. Results showed that Kabba, Idah, Olamabor, Kotonkar, and the southern part of Ajaokuta LGAs exhibit high flood vulnerability due to dense populations, remoteness from roads and critical infrastructure, and considerable distances from healthcare facilities. Likewise, LGAs exhibiting a very high FHI occur along the geographic zones bounded by the confluence of the Niger and Benue rivers, specifically along the Lokoja area, and the Kotonkar, Kogi, Bassa, and Ibaji LGAs. Five flood risk classes—very low, low, moderate, high, and very high FRI classes—occupy 26.82, 31.12, 22.07, 15.26, and 4.71% of the area, respectively. Out of 295 villages, 65 villages are spread across the high flood risk zone. The safest LGAs include Ankpa, Omala, Dekina, Ijumu, Kabba, and Mopa-Muro LGAs.

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Trends and gaps in the literature of road network repair and restoration in the context of disaster response operations

Cited by 10 publications

References 63 publications

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Datasets of disrupted transportation networks on Canada's West Coast in a plausible M9.0 Cascadia Subduction Zone earthquake scenario

Flood risk assessment in Kogi State Nigeria through the integration of hazard and vulnerability factors

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