Multi-granularity periodic activity discovery for moving objects

With the development of location-based services, more moving objects can be traced and a great deal of trajectory data can be collected. Periodicity is very commonly used to analyse the habits of moving objects, so finding objects' periodic patterns can aid in understanding their behaviour. However, objects' periodic patterns are always unknown previously, and describing their periods with different granularities will create some surprised findings. This article proposes a multi-granularity periodic activity discovery (MPAD) approach for moving objects. First, a multi-granularity model is introduced to describe the spatial and temporal information of an object's activities. Then, two algorithms, namely, spatial first and temporal first multi-granularity activity discovery algorithms, are provided to transfer objects' activities into different granularities. Finally, a novel periodic discovery algorithm is described to find the periodicities of objects' activities. Experiments on both synthetic and real datasets demonstrate both the efficiency and effectiveness of the proposed work and its notably improved running performance compared to the same algorithms. Additionally, the discovered periodic patterns are more practically significant.