Shared autonomous vehicles present a significant opportunity to change the way that urban mobility is viewed by society. By providing a shared mobility platform at a cost lower than has previously been obtainable there are significant possibilites to enable a new era of mobility for consumers. This opportunity, however, comes with significant risks in the form of emissions and increased road usage. Understanding how the risks and benefits of shared autonomous vehicles can be balanced is crucial to be able to adequately prepare for their introduction. One of the primary ways to understand the interplay between the risks and benefits of autonomous vehicles is through the use of computer simulations. However, typically simulations must be defined for a specific area and provide results that are not applicable to a wide range of areas and situations. This work presents the development of a framework that can be used to simulate SAV behaviour at any given region of interest. This framework automates the process of generating a directed non-planar graph using data gathered from the OpenStreetMap project. It further provides tools to generate activity based trips that are statistically similar in time and density to provided data that reflects the trips in the simulation area. In the absence of this data, this work has identified the 2009 National Household Travel Survey as an acceptable surrogate for data specific to a region. The framework then provides methods by which the trip origins and destinations are mapped into the directed non-planar graph representation of the area of interest. This mapping is performed using real-world data including business locations and census data. Finally the framework is capable of simulating the activity of SAV in response to the defined trips given a variety of starting conditions and relocation strategies. In addition to the simulation framework this work presents a novel relocation strategy for unoccupied SAV based on the potential field methods that have been used in robotic navigation. This method provides a continously differentiable function that describes the unmet demand in the service area for a network of shared autonomous vehicles. The tunable parameters of the method are explored by using a design of experiments, and optimal values reflecting different scenarios are identified.The method is also evaluated in the context of both and over- and under-supply of vehicles for the given demand. As a result this method has been shown to provide substantial reductions in the wait time for a vehicle to service a trip with a minimal increase in the total distance that is traveled by all vehicles in the network.