Traditional transportation systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and traffic demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding traffic demand and system status can be collected in real-time. This information provides opportunities to perform various types of control and coordination for large scale intelligent transportation systems. In this paper, we present a novel receding horizon control (RHC) framework to dispatch taxis, which combines highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include reducing taxi idle driving distance and matching spatiotemporal ratio between demand and supply for service quality. Moreover, our RHC framework is compatible with different predictive models and optimization problem formulations. This compatibility property allows us to model disruptive passenger demands and traffic conditions into a robust optimization problem. Extensive trace driven analysis with a real taxi data set from San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the total supply demand ratio error across the city by up to 45%.