Due to the increasing popularity of smartphones and in-dash navigation equipment, traffic data gathered by mobile devices has grown in popularity as a basis for predicting traffic conditions. Mobile devices submit location information to a central server, where future traffic conditions are computed. Because this type of system would be expensive to test with real drivers, algorithms for road traffic prediction are often evaluated according to the outcomes of simulations. These simulations frequently use the same types of vehicle data to model the flow of traffic. Based on the observation that these algorithm simulators share common functionality, we have developed a framework to facilitate the rapid development of simulators for traffic prediction. Using our framework, developers can quickly implement traffic prediction simulators with included data processing and visualization features. To date, we have utilized the framework in creating two separate simulators for influence-aware predictive density queries and naive density queries, respectively.