Discovering Companion Vehicles from Live Streaming Traffic Data
Companions of moving objects are object groups that move together in a period of time.To quickly identify companion vehicles from a special kind of streaming traffic data,called Automatic Number Plate Recognition (ANPR) data,this paper proposes an approach to discover companion vehicles.Compared to related approaches,we transform the companion discovery into a frequent sequence-mining problem.We make several improvements on top of a recent frequent sequence-mining algorithm,called SeqStream,to handle customized time constraints among sequence elements when discovering traveling companions.We also use pseudo projection technique to improve the performance of our algorithm.Finally,extensive experiments are done using a real dataset to show efficiency and effectiveness of our approach.
Companion vehicles ANPR data Moment companion Traveling companions Frequent sequence-mining
Chen Liu Xiongbin Wang Meiling Zhu Yanbo Han
Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data,North China University Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data,North China University
国际会议
International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
苏州
英文
116-128
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）