Mining frequent patterns from people’s trajectory has become a hot topic in big data research. Previously, these data mostly come from GPS. Compared with GPS data which is more densely sampled, base station data is extremely sparse in both time and space. Trajectory discovery from base station data becomes much more challenging. In this paper, we propose a new method to effectively solve this problem. In our method, we assume that the locations of objects are sampled over a long time period. First, sequential pattern mining algorithm is employed to find frequent passing areas of a person’s route every day. Second, frequent paths are pieced together by points of records which pass through frequent passing area. Finally, to ensure credibility and efficiency, we depend on the location information provided by scattered points which piece together frequent paths to mine frequent road paths.