Abstract Vehicle detectors (VDs) are usually distributed in a road network to detect macroscopic traffic situations. These detectors provide global information such as vehicle flows, average speed, and road occupancy. Given that the collected statistic data are difficult for citizens to interpret, we visualize the data by providing users with realistic traffic videos. To achieve this aim, our system collects the surveillance videos and VD data that represent the traffic situation of a position. It then builds the connection between these two types of data. Considering the distribution of VDs is much denser than that of surveillance cameras, for those road segments with a VD but without a surveillance camera, one can utilize our system to synthesize videos for visually depicting the traffic situations over there. That is, we estimate vehicle flows from a video and apply the regression model to build the mapping between the flows and VD data. After that, given by a VD dataset, our system retrieves videos that match the VD data and seamlessly composes them to synthesize a traffic video. The evaluations and the experimental results demonstrate the feasibility of our system.