Demand responsive transportation is a variable route service of passengers or freight from specific origin(s) to destination(s) in response to the request of users. Operational planning of DRT system encompasses the methods to provide efficient service to the passengers and to the system operators. These methods cover the assignments of vehicles to transportation requests and vehicle routings under various constraints such as environmental conditions, traffic and service limitations. Advances in the information and communication technologies, such as the Internet, mobile communication devices, GIS, GPS, Intelligent Transportation Systems have led to a significantly complex and highly dynamical decision making environment. Recent approaches to DRT operational planning are based on "closed information loop" to achieve a higher level of automation, increased flexibility and efficiency. Intelligent and effective use of the available information in such a complex decision making environment requires the application of formal modeling and control approaches, which are robust, modular and computationally efficient. In this study, DRT systems are modeled as Discrete Event Systems using Finite Automata formalism and DRT real time control is addressed using Supervisory Control Theory. Two application scenarios are considered; the first is based on air-charter service and illustrates uncontrolled system model and operational specification synthesis. The automatic synthesis of centralized and modular supervisors is demonstrated. The second scenario is a mission critical application based on emergency evacuation problem. Decentralized supervisory control architecture suitable for accommodating the real-time contingencies is presented. Conditions for parallel computation of local supervisors are specified and the computational advantages of alternative supervisory control architectures are discussed. Discrete event system modeling and supervisory control theory are well established and powerful mathematical tools. In this dissertation, they are shown to be suitable for expressing the modeling and control requirements of complex and dynamic applications in DRT. The modeling and control approaches described herein, coupled with the mature body of research literature in Discrete Event Systems and Supervisory Control Theory, facilitate logical analysis of these complex systems and provide the necessary framework for development of intelligent decision making tools for real time operational planning and control in a broad range of DRT applications.