In the past few years a growing number of cities have started monitoring the position of public transportation vehicles using GPS devices. In this paper, we focus on a particularly important urban dataset: GPS bus data. Buses are valuable sensors and information associated with buses can provide unprecedented insight into many different aspects of city's life, from human behavior to mobility patterns. But analyzing these large urban datasets presents many challenges. Urban datasets are complex, containing location and temporal components in addition that they are commonly released in their raw format. Furthermore, urban datasets may have noisy and missing data, locations gathered in a low sampling rate and not mapped to the underlying road network, among other issues which makes it difficult for citizens, administrators and developers to get insights. In this paper, we present a system, called USapiens, for analyzing large urban trajectory data. We first describe the architecture of the proposed system for pre-processing and analyzing urban trajectory data. We then detail five use cases we build using very large GPS dataset obtained from buses operating in the city of Rio de Janeiro to get insights into various aspects of public transportation in the city.