关键词: 语义轨迹, 频繁模式, 数据挖掘, 拼车

Abstract: Current trajectory analysis mainly uses clustering method to mine common residence points from multi-user trajectories, calculate user similarity to find hot spots and extract public attributes of approximate people. It has no commercial value to calculate similarity for the same user, so it is seldom studied on single-user trajectory analysis. This paper presents a method of mining frequent patterns of individual user trajectories based on location semantics. The semantic trajectory is retrieved by inverse geocoding and preprocessed to obtain Top-[k] candidate frequent location itemsets. The frequent iteration calculation of long itemsets is transformed into regular operation of hierarchical sets by using intersection and merging methods of spatiotemporal sequences, and the frequent sequence supersets and subsets are obtained. This frequent pattern mining of semantic trajectories can actively identify and discover potential car pooling needs, and provide higher accuracy for location-based intelligent recommendation such as shared car pooling and HOV Lane travel. The results of simulation car pooling experiment prove the applicability and efficiency of this method.

Key words: semantic trajectory, frequent pattern, data mining, car pooling