The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. Most travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Currently, systems that collect floating car data are designed to transmit the data in a limited form and relatively infrequently due to the cost of data transmission. That means the reported locations of vehicles are far apart in time and space. In order to extract traffic information from the data, it first needs to be matched to the underlying digital road network. Matching such sparse data to the network, especially in dense urban, area is challenging. This thesis introduces a map-matching and path inference algorithm for sparse GPS probes in urban networks. The method is utilized in a case study in Stockholm and showed robustness and high accuracy compared to a number of other methods in the literature. The method is used to process floating car data from 1500 taxis in Stockholm City. The taxi data had been ignored because of its low frequency and minimal information. The proposed method showed that the data can be processed and transformed into information that is suitable for traffic studies. The thesis implemented the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control. / <p>QC 20121107</p>