High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals’ locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy.

Frequent route pattern mining from personal trajectory data is the basis of location awareness and location services. However, because personal trajectory data is highly uncertain, most existing approaches are only capable of finding short and incomplete route patterns. In this paper, a novel approach is proposed for the discovery of frequent route patterns based on trajectory abstraction. First, trajectory partition, location extraction, data simplification, and common segment discovery are used to abstract trajectory data, convert these trajectories into common segment temporal sequences (STS) and generate 1-frequent itemsets. Then, a pattern mining algorithm is proposed based on the spatial-temporal adjacency relationship. This algorithm uses the constraint mechanism and bidirectional projected database to mine frequent route patterns from STS. Based on the real GeoLife trajectory data, the experimental results indicate that the proposed method has better performance and can find longer route patterns than other currently available methods.

Identifying periodic patterns in individuals’ trajectories is the basis of location awareness and personalized location services. It can help us understand personal behaviors. However, fuzziness and uncertainty of trajectory data, as well as noise and period distortion make it difficult to recognize periodic patterns. Additionally, the period lengths are usually unknown, and patterns have multiple granularities. Most of the existing mining algorithms focus on the discovery of patterns with different period lengths at a specific spatial scale, and few algorithms identify periodic patterns from the perspective of spatiotemporal multi-granularity. Based on the existing studies, we propose a framework for identifying periodic patterns with different spatiotemporal granularities from personal trajectory data. First, a sequence of trajectory points is transformed into a time series of locations by using trajectory abstraction. Then, a multi-granularity behavior model is defined from spatial and temporal information. Finally, the single behavior periodic patterns can be discovered without knowing the period length by using a novel algorithm. And based on the association rules between locations, we can determine the periodic patterns of multiple behaviors from single behavior patterns. To evaluate the accuracy and efficiency of the algorithms, an artificial trajectory sequence and a real-world trajectory dataset called GeoLife are used in comparative experiments. The experimental results show that the proposed algorithm has higher accuracy on the promise of efficiency.