This thesis approaches the topic of value of time calculation and route choice behavior with a new and innovative methodology using a survey dataset that was uniquely designed and implemented for this purpose. The survey is a 70 day, 218 participant GPS travel survey used to track individual location constantly at one minute intervals. Using a positive behavior theory framework, an in depth knowledge database for each user is created that iteratively updates the learned behavior and experienced travel conditions for each trip the user takes. A new approach for calculating value of time is presented; using the cost and trip duration of previous trips. The bounds (or caps and floors) are averaged to achieve the individual's value of time based upon their route (and therefore cost) decisions. Also using this updating knowledge base, route decision rules are derived using machine learning algorithms to tell why a user has decided to take the toll road option for certain days, and under what conditions the user will not take the toll road option. The final contribution is a model that fully takes advantage of longitudinal GPS data to create an adaptive system for value of time calculation and positive route decision making.