With the rapid growth in modern cities and congestion on major freeways and local streets, public transit services have become more and more important for urban transportation. As an important component of Intelligent Transportation Systems (ITS), Transit Signal Priority (TSP) systems have been extensively studied and widely implemented to improve the quality of transit service by reducing transit delay. The focus of this research is on the development of a platform with the physical queue representation that can be employed to evaluate and/or improve TSP strategies with the consideration of the interaction between transit vehicles and queues at the intersection.This dissertation starts with deterministic analyses of TSP systems based on a physical queue model. A request oriented TSP decision process is then developed which incorporates a set of TSP decision regions defined on a time-space diagram with the physical queue representation. These regions help identify the optimal detector location, select the appropriate priority control strategy, and handle the situations with multiple priority requests. In order to handle uncertainties in TSP systems arising in bus travel time and dwell time estimation, a type-2 fuzzy logic forecasting system is presented and tested with field data. Type-2 fuzzy logic is very powerful in dealing with uncertainty. The use of Type-2 fuzzy logic helps improve the performance of TSP systems. The last component of the dissertation is the development of a Colored Petri Net (CPN) model for TSP systems. With CPN tools, computer simulation can be performed to evaluate various TSP control strategies and the decision process. Examples for demonstrating the process of implementing the green extension strategy and the proposed TSP decision process are presented in the dissertation. The CPN model can also serve as an interface between the platform developed in this dissertation and the implementation of the control strategies at the controller level.