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多模式移动对象不确定性轨迹预测模型

乔少杰 韩楠 丁治明 金澈清 孙未未 舒红平

乔少杰, 韩楠, 丁治明, 金澈清, 孙未未, 舒红平. 多模式移动对象不确定性轨迹预测模型. 自动化学报, 2018, 44(4): 608-618. doi: 10.16383/j.aas.2017.c160575
引用本文: 乔少杰, 韩楠, 丁治明, 金澈清, 孙未未, 舒红平. 多模式移动对象不确定性轨迹预测模型. 自动化学报, 2018, 44(4): 608-618. doi: 10.16383/j.aas.2017.c160575
QIAO Shao-Jie, HAN Nan, DING Zhi-Ming, JIN Che-Qing, SUN Wei-Wei, SHU Hong-Ping. A Multiple-motion-pattern Trajectory Prediction Model for Uncertain Moving Objects. ACTA AUTOMATICA SINICA, 2018, 44(4): 608-618. doi: 10.16383/j.aas.2017.c160575
Citation: QIAO Shao-Jie, HAN Nan, DING Zhi-Ming, JIN Che-Qing, SUN Wei-Wei, SHU Hong-Ping. A Multiple-motion-pattern Trajectory Prediction Model for Uncertain Moving Objects. ACTA AUTOMATICA SINICA, 2018, 44(4): 608-618. doi: 10.16383/j.aas.2017.c160575

多模式移动对象不确定性轨迹预测模型

doi: 10.16383/j.aas.2017.c160575
基金项目: 

国家自然科学基金 61772138

国家自然科学基金 61501063

国家高技术研究发展计划(863计划) 2014BAI06B01

成都信息工程大学引进人才科研启动项目 KYTZ201750

成都信息工程大学中青年学术带头人科研基金 J201701

成都信息工程大学引进人才科研启动项目 KYTZ201715

国家自然科学基金 61772091

教育部人文社会科学研究青年基金 14YJCZH046

教育部人文社会科学研究规划基金 15YJAZH058

成都市软科学项目 2015-RK00-00059-ZF

四川高校科研创新团队建设计划资助 18TD0027

国家自然科学基金 91546111

四川省教育厅资助科研项目 14ZB0458

国家自然科学基金 61501064

国家自然科学基金 61100045

详细信息
    作者简介:

    乔少杰  成都信息工程大学网络空间安全学院教授.2009年获得四川大学计算机学院工学博士学位.主要研究方向为轨迹预测, 移动对象数据库, 大数据.E-mail:sjqiao@cuit.edu.cn

    丁治明  北京工业大学计算机学院教授.2002年获得中国科学院计算技术研究所工学博士学位.主要研究方向为轨迹大数据, 移动对象数据库.E-mail:zmding@bjut.edu.cn

    金澈清  华东师范大学数学科学与工程学院教授.2005年获得复旦大学计算机科学技术学院工学博士学位.主要研究方向为基于位置的服务, 不确定数据管理, 数据质量.E-mail:cqjin@sei.ecnu.edu.cn

    孙未未  复旦大学计算机科学技术学院教授.2002年获得复旦大学计算机科学技术学院工学博士学位.主要研究方向为空间数据处理, 基于位置的服务.E-mail:wwsun@fudan.edu.cn

    舒红平  成都信息工程大学软件工程学院教授.2010年获得四川大学计算机学院工学博士学位.主要研究方向为数据挖掘.E-mail:shp@cuit.edu.cn

    通讯作者:

    韩楠  成都信息工程大学管理学院讲师.2012年获得成都中医药大学博士学位.主要研究方向为数据挖掘.本文通信作者.E-mail:hannan@cuit.edu.cn

A Multiple-motion-pattern Trajectory Prediction Model for Uncertain Moving Objects

Funds: 

National Natural Science Foundation of China 61772138

National Natural Science Foundation of China 61501063

National High Technology Research and Development Program of China (863 Program) 2014BAI06B01

Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology KYTZ201750

Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology J201701

Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology KYTZ201715

National Natural Science Foundation of China 61772091

Youth Foundation for Humanities and Social Sciences of Ministry of Education 14YJCZH046

Planning Foundation for Humanities and Social Sciences of Ministry of Education of China 15YJAZH058

Soft Science Foundation of Chengdu 2015-RK00-00059-ZF

Innovative Research Team Construction Plan in Universities of Sichuan Province 18TD0027

National Natural Science Foundation of China 91546111

Foundation of Educational Commission of Sichuan Province 14ZB0458

National Natural Science Foundation of China 61501064

National Natural Science Foundation of China 61100045

More Information
    Author Bio:

     Professor at the School of Cybersecurity, Chengdu University of Information Technology. He received his Ph. D. degree from the College of Computer Science, Sichuan University in 2009. His research interest covers trajectory prediction, moving objects databases, and big data

     Professor at the College of Computer Science, Beijing University of Technology. He received his Ph. D. degree from the Institute of Computing Technology, Chinese Academy of Sciences in 2002. His research interest covers trajectory big data and moving objects databases

     Professor at the School of Data Science and Engineering, East China Normal University. He received his Ph. D. degree from the School of Computer Science, Fudan University in 2005. His research interest covers location-based services, uncertain data management, and data quality

     Professor at the School of Computer Science, Fudan University. He received his Ph. D. degree from the School of Computer Science, Fudan University in 2002. His research interest covers spatial data processing and location-based services

     Professor at the School of Software Engineering, Chengdu University of Information Technology. He received his Ph. D. degree from the College of Computer Science, Sichuan University in 2010. His main research interest is data mining

    Corresponding author: HAN Nan  Lecturer at the School of Management, Chengdu University of Information Technology. She received her Ph. D. degree from Chengdu University of Traditional Chinese Medicine University in 2012. Her main research interest is data mining. Corresponding author of this paper
  • 摘要: 以移动设备、车辆、飞机、飓风等移动对象不确定性轨迹预测问题为背景,将大规模移动对象数据作为研究对象,以频繁轨迹模式挖掘、高斯混合回归技术为主要研究手段,提出多模式移动对象轨迹预测模型,关键技术包括:1)针对单一运动模式,提出一种基于频繁轨迹模式树FTP-tree的轨迹预测方法,利用基于密度的热点区域挖掘算法将轨迹点划分成不同的聚簇,构建轨迹频繁模式树,挖掘频繁轨迹模式预测移动对象连续运动位置.不同数据集上实验结果表明基于FTP-tree的轨迹预测算法在保证时间效率的前提下预测准确性明显优于已有预测算法.2)针对复杂多模式运动行为,利用高斯混合回归方法建模,计算不同运动模式的概率分布,将轨迹数据划分为不同分量,利用高斯过程回归预测移动对象最可能运动轨迹.实验证明,相比于基于隐马尔科夫模型和卡尔曼滤波的预测方法,所提方法具有较高的预测准确性和较低的时间代价.
    1)  本文责任编委 黎铭
  • 图  1  Chengdu数据集下不同算法预测准确率比较

    Fig.  1  Prediction accuracy comparison of different algorithms under Chengdu datasets

    图  2  GeoLife数据集下不同算法预测准确率比较

    Fig.  2  Prediction accuracy comparison of different algorithms under GeoLife datasets

    图  3  PathPrediction算法在不同数据集下预测时间比较

    Fig.  3  Prediction tiof PathPrediction algorithm under different datasets

    图  4  Chengdu数据集下不同算法预测时间比较

    Fig.  4  Prediction time comparison of different algorithms under Chengdu datasets

    图  5  GeoLife数据集下不同算法预测时间比较

    Fig.  5  Prediction time comparison of different algorithms under GeoLife datasets

    图  6  不同高斯过程分量下轨迹预测误差比较

    Fig.  6  Prediction error comparison with distinct Gaussian regression components

    图  7  不同算法预测误差比较

    Fig.  7  Prediction bias comparison of different algorithms

    图  8  不同算法预测准确率比较

    Fig.  8  Prediction accuracy comparison of different algorithms

    图  9  不同算法预测时间性能比较

    Fig.  9  Prediction time comparison among different algorithms

    图  10  单一运动模式轨迹预测准确率比较

    Fig.  10  Prediction accuracy comparison beyond single-motion-pattern trajectories

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出版历程
  • 收稿日期:  2016-08-04
  • 录用日期:  2016-12-10
  • 刊出日期:  2018-04-20

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