Mining Frequent Route Patterns Based on Personal Trajectory Abstraction

Abstract

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.