Urbanization in developing countries has resulted in increased demand for public transportation in the face of limited resources. This requires smart transportation management that allows urban planners to evaluate the impact of their policies and design targeted interventions. This paper proposes a three-layer management system to support smart urban mobility with an emphasis on bus transportation. In Layer-1, we apply novel Big Data techniques to compute bus travel time and passenger demands in an efficient and economical way. Layer-2 contains two analytic components: network analysis of passenger transit patterns and causal relationship analysis for bus delays. The third layer provides decision support in an interactive visualization environment. The proposed system is developed and validated in cooperation with the city of Fortaleza in Brazil. The use of generally available urban transportation data makes our methodology adaptable and customizable for other cities.