计算机工程与应用2019，Vol.55Issue(15)：96-103,8.DOI:10.3778/j.issn.1002-8331.1901-0184
挖掘语义轨迹频繁模式及拼车应用研究
Mining Semantic Trajectory Frequent Pattern and Car Pooling Application Research
摘要
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 item-sets. 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.关键词
语义轨迹/频繁模式/数据挖掘/拼车Key words
semantic trajectory/ frequent pattern/ data mining/ car pooling分类
信息技术与安全科学引用本文复制引用
刘春,周燕,李鑫..挖掘语义轨迹频繁模式及拼车应用研究[J].计算机工程与应用,2019,55(15):96-103,8.基金项目
国家重点研发计划(No.2017YFC1405403). （No.2017YFC1405403）
