Transportation networks are critical to the social and economic function of nations. Given the continuing increase in the populations of cities throughout the world, the criticality
of transportation infrastructure is expected to increase. Thus, it is ever more important to mitigate congestion as well as to assess the impact disruptions would have on individuals
who depend on transportation for their work and livelihood.
Moreover, several government organizations are responsible for ensuring transportation networks are available despite the constant
threat of natural disasters and terrorist activities. Most of the previous transportation network vulnerability research has been performed in the context of static traffic models, many of which are formulated as traditional optimization problems.
However, transportation networks are dynamic because their usage varies over time. Thus, more appropriate methods to characterize the vulnerability of transportation networks should
consider their dynamic properties. This paper presents a quantitative approach to assess the vulnerability of a transportation network to disruptions with methods from traffic simulation.
Our approach can prioritize the critical links over time and is generalizable to the case where both link and node disruptions
are of concern. We illustrate the approach through a series of examples. Our results demonstrate that the approach provides
quantitative insight into the time varying criticality of links.
Such an approach could be used as the objective function of less traditional optimization methods that use simulation and
other techniques to evaluate the relative utility of a particular network defense to reduce vulnerability and increase resilience.
Revised: June 4, 2018 |
Published: June 8, 2017
Citation
Shekar V., L. Fiondella, S. Chatterjee, and M. Halappanavar. 2017.Quantitative Assessment of Transportation Network Vulnerability with Dynamic Traffic Simulation Methods. In IEEE International Symposium onTechnologies for Homeland Security (HST 2017), April 25-26, 2017, Waltham, MA, 1-7. Piscataway, New Jersey:IEEE.PNNL-SA-124025.doi:10.1109/THS.2017.7943454