Transportation networks are vital elements in modern economic and social systems. These networks are vulnerable to damage from the impact of extreme events. Such damage adversely affects network connectivity, as well as delaying relief and restoration operations. To better plan how to restore these infrastructure elements, this study develops network-analysis and graph theory based tools using real-world data for network restoration planning. Models are developed that identify the influential nodes to map the interdependencies between different modes of transportation and determine which network components contribute most to its connectivity. An efficient node ranking method is also proposed to aid in the restoration of the critical infrastructure network in the aftermath of a disaster. Weighting factors are used to rank and map influential nodes for prioritizing respective network regions by their actual use. This approach is applied to publicly available real-world data for St. Louis, Missouri.