Resiliency of Transportation Network of Santo Domingo, Dominican Republic

Every day 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 disasters and those caused by humans. To prepare for these events, decision makers need guidance to determine what investments are likely to improve the resiliency of their networks, which are often hampered by limited resources. To date, such guidance has been primarily qualitative. This paper presents a methodology to quantify resiliency, under preevent conditions, by use of a fuzzy inference approach. This methodology expands on previous work by the authors and others, by refining the definitions of key variables, adjusting model interactions, and increasing transparency between metrics. The paper includes a case study in which the methodology is applied to a disruptive event in Santo Domingo, Dominican Republic. The case study illustrates the methodology's ability to (a) evaluate the extent to which the Dominican Republic's transportation network is prepared for a disruptive event, (b) help select investments that have the potential to increase the resiliency of the network, and (c) provide outputs that will support a variety of current economic analysis strategies, allow comparison of different investment scenarios, and facilitate decision making. The paper concludes with a sensitivity analysis that shows the effects of alternative investments on the network.