A nationally accepted and adopted methodology for state departments of transportation to assess the importance of roads, bridges, and other transportation assets doesn’t exist. Instead, transportation agencies must prioritize the projects to invest in, without having a complete picture of the makeup of their network and the risk posed from a loss or disruption of a component of the network. To make informed transportation investment decisions, transportation leaders need to have the tools necessary to determine the risk to the infrastructure based on the possible consequences if a segment of the transportation infrastructure network were disrupted.
This thesis provides a methodology for developing a network model of surface transportation infrastructure using Green et. al.’s Network Theory, and analyzing the risk of a disruption to that network based on the consequence of population and economic impacts. Population impacts are derived from the number of vehicles that will no longer be able to utilize the network, based on Annual Average Daily Traffic (AADT) numbers. Economic impacts are calculated from the tonnage of commodities that travel throughout the network, and calculating the dollar value per ton of those commodities.
The methodology is presented in two ways: first, by explaining the steps of the methodology using LeBlanc’s Sioux Falls North Dakota data set; and second, by applying those same steps to the real-world data available for Pierce County in Washington State. The outcome of the network-based risk assessment is then applied to Tundrea et. al.’s risk management theory, in order to demonstrate to transportation leaders what the data is telling them, and how to apply that information toward the reduction of risk to their respective transportation networks.
The methodology outlined in this thesis is simple in nature, can be applied to multiple levels of government, and fills a notable gap in the transportation community. By analyzing the surface transportation system as a network, and then analyzing the risk to that network based on the consequence of a disruption, transportation leaders can make better informed decisions about how to prioritize their investments based on mitigating the risk to the network.