Impact analysis of extreme events on flows in spatial networks

Kermanshah, Amirhassan; Karduni, Alireza; Peiravian, Farideddin; Derrible, Sybil

doi:10.1109/bigdata.2014.7004428

Cited by 19 publications

(18 citation statements)

References 20 publications

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“…Most works that have sought to establish a direct relationship between loss of level of service and hazard intensity measures have limited this relationship to be represented by binary or step functions (e.g. Kermanshah, Karduni [19]). Note that some differences may be observed as a result of the copy-editing and typesetting process.…”

Section: Figure 4 Illustrative Fragility Functionsmentioning

confidence: 99%

Stress tests for a road network using fragility functions and functional capacity loss functions

Lam

Adey

Heitzler

et al. 2018

Reliability Engineering & System Safety

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A quantitative approach to conduct a specific type of stress test on road networks is presented in this article. The objective is to help network managers determine whether their networks would perform adequately during and after the occurrence of hazard events. Conducting a stress test requires (i) modifying an existing risk model (i.e., a model to estimate the probable consequences of hazard events) by representing at least one uncertainty in the model with values that are considerably worse than median or mean values, and (ii) developing criteria to conclude if the network has an adequate post-hazard performance. Specifically, the stress test conducted in this work is focused on the uncertain behavior of individual objects that are part of a network when these are subjected to hazard loads. Here, the relationships between object behavior and hazard load are modeled using fragility functions and functional capacity loss functions. To illustrate the quantitative approach, a stress test is conducted for an example road network in Switzerland, which is affected by floods and rainfall-triggered mudflows. Beyond the focus of the stress test, this work highlights the importance of using a probabilistic approach when conducting stress tests for temporal and spatially distributed networks.

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Section: Figure 4 Illustrative Fragility Functionsmentioning

confidence: 99%

Stress tests for a road network using fragility functions and functional capacity loss functions

Lam

Adey

Heitzler

et al. 2018

Reliability Engineering & System Safety

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show abstract

“…The presence of traffic sensors enables the automatic counting of vehicles, at least for a portion of the road network. Incorporation of this data and other emerging data sets from driver monitoring systems offers the potential for a far richer understanding of vehicle response during disruptive events and should greatly improve the validation and calibration of this type of model (Jenelius and Mattsson 2012;Batty 2013;Kermanshah et al 2014;Osei-Asamoah and Lownes 2014;Kermanshah and Derrible 2016).…”

Section: Implications Of Uncertainties and Future Developmentmentioning

confidence: 99%

Impact of Climate Change on Disruption to Urban Transport Networks from Pluvial Flooding

Pregnolato

Ford

Glenis

et al. 2017

J. Infrastruct. Syst.

103

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Short-duration, high-intensity rainfall causes significant disruption to transport operations, and climate change is projected to increase the frequency and intensity of these events. Disruption costs of flooding are currently calculated using crude approaches. To support improved business cases for adapting urban infrastructure to climate change, this paper presents an integrated framework that couples simulations of flooding and transport to calculate the impacts of disruption. A function, constructed from a range of observational and experimental data sources, is used to relate flood depth to vehicle speed, which is more realistic than the typical approach of categorizing a road as either blocked or free flowing. The framework is demonstrated on Newcastle upon Tyne in the United Kingdom and shows that by the 2080s disruption across the city from a 1-in-50-year event could increase by 66%. A criticality index is developed and is shown to provide an effective metric to prioritize intervention options in the road network. In this case, just two adaptation interventions can reduce travel delays across the city by 32%.

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“…From rivers, roads and pipelines to electrical and telecommunication lines, many geographical systems are composed of collections of elements that are connected in space. Therefore, from studying and understanding human activities 1 to studying the relationship between different urban areas 2 , conducting these studies through the lens of graphs and networks can shed light on many complex characteristics of the elements that are built upon them 3 , 4 . This approach is rooted in the origins of the field of Graph Theory developed in the 18th century by Euler and his Seven Bridges of Königsberg 5 , and it has been applied widely ever since 6–13 .…”

Section: Background and Summarymentioning

confidence: 99%

A protocol to convert spatial polyline data to network formats and applications to world urban road networks

2016

Self Cite

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The study of geographical systems as graphs, and networks has gained significant momentum in the academic literature as these systems possess measurable and relevant network properties. Crowd-based sources of data such as OpenStreetMaps (OSM) have created a wealth of worldwide geographic information including on transportation systems (e.g., road networks). In this work, we offer a Geographic Information Systems (GIS) protocol to transfer polyline data into a workable network format in the form of; a node layer, an edge layer, and a list of nodes/edges with relevant geographic information (e.g., length). Moreover, we have developed an ArcGIS tool to perform this protocol on OSM data, which we have applied to 80 urban areas in the world and made the results freely available. The tool accounts for crossover roads such as ramps and bridges. A separate tool is also made available for planar data and can be applied to any line features in ArcGIS.

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Impact analysis of extreme events on flows in spatial networks

Cited by 19 publications

References 20 publications

Stress tests for a road network using fragility functions and functional capacity loss functions

Stress tests for a road network using fragility functions and functional capacity loss functions

Impact of Climate Change on Disruption to Urban Transport Networks from Pluvial Flooding

A protocol to convert spatial polyline data to network formats and applications to world urban road networks

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