Risk-based vulnerability assessment for transportation infrastructure performance

Shital A. Thekdi and Nilesh N. Joshi

International Journal of Critical Infrastructures, 2016, vol. 12, issue 3, 229-247

Abstract: In recent years, several high profile weather events, earthquakes, man-made incidents, and other disruptive events have caused significant disruption to the flow of goods and services across large-scale transportation networks. Resulting economic and safety repercussions of these events are associated with reduced efficiency of movement, cost-overruns, and supply disruptions for freight movement. As transportation infrastructure is vulnerable to a variety of uncertain future conditions, transportation management in coordination with industry must recognise uncertainties in future events during stages of distribution planning. This paper describes a scenario-based Bayesian approach to evaluate evidence from big-data resources, such as geographic landscape and demographic data, to identify vulnerable sections of the transportation network. This method contributes to organisational priority setting by considering the influence of a variety of scenarios on priorities, thereby identifying robust risk-informed investments for the protection of geographically diverse large-scale infrastructure systems. The approach will allow decision-makers to utilise a data-driven graphical model for network operations, with updated beliefs as new evidence emerges. The methods are demonstrated on a critical transportation network in the Commonwealth of Virginia. The results are useful to stakeholders responsible for promoting efficiency across transportation networks, such as infrastructure managers, supply chain managers, disaster relief organisations.

Keywords: performance management; risk management; vulnerability assessment; transport infrastructure; supply chain management; SCM; scenario analysis; multicriteria analysis; disruptive events; freight movement; uncertainty; distribution planning; big data; transportation networks; modelling; disaster relief; emergency management; emergency planning; USA; United States. (search for similar items in EconPapers)
Date: 2016
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