Every day the dependence on transportation grows as local, regional, national, and international independence increases. Resilient transportation systems are needed to secure the highest possible level of service during disruptive events, including natural and man-made disasters. Because of limited resources, decision makers need guidance on how, when, and where to invest to improve resiliency of their networks. The research objective is to develop a method to assess and quantify resiliency, at pre-event conditions, using a fuzzy inference approach. This research expands previous work, refining key variable definitions, adjusting model interactions, and increasing transparency between metrics. This thesis presents the method and provides an illustrative example of the methodology using the Dominican Republic as a case study. The example explains how a transportation network responds to a disruptive event and how specific investments can increase resiliency of the network. The result of this research is a quantitative basis for decision makers to conduct cost-benefit analysis of resiliency increasing projects.