标题:Time Mode Based Next Position Prediction System
作者:Yu, Chongsheng ;Li, Xin ;Ju, Lei ;Zhang, Yu ;Qin, Jian ;Dou, Lei ;Liu, Jie
通讯作者:Li, Xin
作者机构:[Yu, Chongsheng ;Li, Xin ;Ju, Lei ;Zhang, Yu ;Qin, Jian ;Liu, Jie ] School of Computer Science and Technology, Shandong University, Jinan, China;[Dou, 更多
会议名称:19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017
会议日期:18 December 2017 through 20 December 2017
来源:Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
出版年:2018
卷:2018-January
页码:586-593
DOI:10.1109/HPCC-SmartCity-DSS.2017.76
关键词:position prediction; time mode; trajectory pattern
摘要:Position prediction of moving object has become a reality utilizing the vast amount of location data acquired by positioning devices embedded in mobile phones and cars. In this paper, we proposed a position prediction system which focuses on the time regularity of object moving. Historical location data of the object is used to extract personal trajectory patterns to obtain candidate next positions. Each of the candidate positions is scored by the proposed Time Mode-based Prediction (TMP) algorithm according to the proximity between the time component of patterns and current time. The position with the highest score is regarded as predicted next position. Furthermore, a hybrid B/S and C/S architecture is employed to perform the real-time prediction and results display. An evaluation based on a public trajectory data set of 12 objects demonstrates that the proposed TMP algorithm can realize position prediction with high accuracy. Moreover, the average accuracy rate of our prediction algorithm is about 85.5%, which is 33.7% greater than the Markov-based algorithm with one known position. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047494351&doi=10.1109%2fHPCC-SmartCity-DSS.2017.76&partnerID=40&md5=6a020781acfbdd944a515129333e3271
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