This thesis seeks to utilize network modeling and conditional probability concepts to develop methods that will give greater transparency to the reliability of various modes of passenger travel. There is a wide range of research and applications on deterministic shortest travel time itineraries. However, despite consistency and reliability in travel being important to travelers, there is little work that attempts to apply transportation data to give users reliability information for their itineraries. This work addresses these issues across various modes of transportation, including long-distance travel, such as flights, urban transit networks, like public transportation, and multi-modal networks, such as combining car and flight travel options. We evaluate the reliability of different itineraries and identify the itinerary with highest reliability given a start time and travel time budget. We use extensive historical datasets to create reliable itineraries and compare these with deterministic shortest travel time itineraries to build insights on the structure of those itineraries within the considered transportation network. This research presents several network models for evaluating reliability that vary depending on the mode of transportation being considered. For each of them, an efficient network search algorithm is implemented to identify the most reliable itinerary for a given traveler. The most reliable flight itinerary problem, presented in Chapter 2, is the first examination of how reliability can be measured and optimized given a large airline network. This model is expanded in Chapter 3 to include the initial and final driving legs to and from the airport, which creates additional complexity and new considerations to the model. Finally, the concept of reliability is translated into a public transit network in Chapter 4 with stops, delays and the additional consideration of backup itineraries after a missed transfer.