吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1764-1770.doi: 10.13229/j.cnki.jdxbgxb201406035

• 论文 • 上一篇    下一篇

基于GPS轨迹的规律路径挖掘算法

何雯1, 2, 李德毅1, 2, 安利峰1, 张天雷1, 郭沐1, 陈桂生2   

  1. 1.清华大学 计算机科学与技术系,北京100084;
    2.中国电子系统工程研究所,北京 100840
  • 收稿日期:2013-04-27 出版日期:2014-11-01 发布日期:2014-11-01
  • 通讯作者: 李德毅(1944-),男,研究员.研究方向:不确定人工智能,智能交通,网络数据挖掘,云计算.E-mail:lidy@cae.cn
  • 作者简介:何雯(1982-),女,博士研究生.研究方向:智能交通,空时数据挖掘.E-mail:
  • 基金资助:

    国家自然科学基金重点项目(91120306,90920305)

Regular route mining algorithm based on GPS trajectories

HE Wen1, 2, LI De-yi1, 2, AN Li-feng1, ZHANG Tian-lei1, GUO Mu1, CHEN Gui-sheng2   

  1. 1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2.Institute of Electronic System Engineering of China, Beijing 100840, China
  • Received:2013-04-27 Online:2014-11-01 Published:2014-11-01

RICH HTML

0   

PDF (PC)

1306

摘要：

基于用户的历史轨迹数据,对用户的规律路径进行挖掘和提取。在轨迹预处理和聚类的基础上,定义了支撑路径的概念,提出了一种基于支撑得分的规律轨迹挖掘算法。并通过规律停止率特征,提高了对轨迹交通模式识别的准确率。基于178名用户4年的GPS轨迹记录,以及37名用户的实际轨迹数据,开展了用户试验。结果表明,本文算法能够有效地提取用户的规律路径,并对路径中的干扰具有一定的鲁棒性。

关键词: 人工智能, 轨迹挖掘, 规律路径, 交通模式, 位置服务

Abstract:

Based on users' historical trajectory data, users' regular routes were mined and extracted. A concept of support route was defined after route pre-processing and grouping. A regular route mining algorithm was proposed based on the support score. A feature of Regular Stop Rate (RSR) was used to improve the accuracy of the transportation mode recognition. The effectiveness of the approach was validated based on the GPS data of 178 users over four years. A real user study was also performed among 37 users. The experiment results demonstrate that the algorithm can effectively extract the regular routes and is robust to slight disturbance in trajectory data.

Key words: artificial intelligent, trajectory mining, regular routes, transportation mode, location based service

中图分类号: 

  • TP18
[1] 袁冠,夏士雄,张磊,等. 基于结构相似度的轨迹聚类算法[J].通信学报,2011, 32(9): 103-110. Yuan Guan, Xia Shi-xiong, Zhang Lei, et al. Trajectory clustering algorithm based on structural similarity[J]. Journal on Communications, 2011, 32(9): 103-110.
[2] Lee J G, Han J, Li X, et al. Mining discriminative patterns for classifying trajectories on road networks[J]. IEEE Trans Knowledge and Data Eng, 2011, 23(5): 713-726.
[3] 赵越,刘衍珩,余雪岗,等,基于模式挖掘与匹配的移动轨迹预测方法[J]. 吉林大学学报:工学版,2008, 38(5):1125-1130. Zhao Yue, Liu Yan-heng, Yu Xue-gang, et al. Method for mobile path prediction based on pattern mining and matching[J]. Journal of Jilin University (Engineering and Technology Edition),2008,38(5):1125-1130.
[4] Chang K, Wei L, Yeh M Y, et al. Discovering personalized routes from trajectories[C]∥Pro 3rd ACM Sigspatial International Workshop on Location-Based Social Networks,New York, 2011:33-40.
[5] Chen L, Lü M, Ye Q, et al. A personal route prediction system based on trajectory data mining[J]. Information Sciences, 2011,181(7): 1264-1284.
[6] Zheng J, Ni L. An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data[C]∥Pro 2012 ACM Conf on Ubiquitous Computing, New York, 2012:153-162.
[7] Farrahi K, Daniel G. What did you do today? Discovering daily routines from large-scale mobile data[C]∥Pro of the 16th ACM Conference on Multimedia, New York, 2008:849-852.
[8] Gidofalvi G, Pedersen T B. Mining long, sharable patterns in trajectories of moving objects[J]. GeoInformatica,2009,13(1):27-55.
[9] 郭黎敏,丁治明,胡泽林,等. 基于路网的不确定性轨迹预测[J]. 计算机研究与发展,2010, 47(1):104-112. Guo Li-min, Ding Zhi-ming, Hu Ze-lin, et al. Uncertain path prediction of moving objects on road networks[J]. Journal of Computer Research and Development,2010,47(1):104-112.
[10] Lee J G, Han J, Whang K Y. Trajectory clustering: a partition-and-group framework[C]∥Pro of the 2007 ACM SIGMOD International Conference on Management of Data, New York, 2007:593-604.
[11] Zheng Y, Li Q, Chen Y, et al. Understanding mobility based on GPS data[C]∥Pro of ACM Conference on Ubiquitous Computing, New York, 2008: 312-321.
[12] Zheng Y, Zhang L, Xie X, et al. Mining interesting locations and travel sequences from GPS trajectories[C]∥Pro of International Conference on World Wild Web, New York, 2009:791-800.
[13] Yuan J, Zheng Y, Xie X, et al. T-drive: enhancing driving directions with taxi drivers' intelligence[J]. IEEE Transactions on Knowledge and Data Engineering,2013,25(1):220-232.
[14] Zheng Y, Xie X, Ma W. GeoLife: a collaborative social networking service among user, location and trajectory[J]. IEEE Data Engineering Bulletin,2010,33(2):32-40.
[1] 董飒, 刘大有, 欧阳若川, 朱允刚, 李丽娜. 引入二阶马尔可夫假设的逻辑回归异质性网络分类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1571-1577.
[2] 顾海军, 田雅倩, 崔莹. 基于行为语言的智能交互代理[J]. 吉林大学学报(工学版), 2018, 48(5): 1578-1585.
[3] 王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] 张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] 黄岚, 纪林影, 姚刚, 翟睿峰, 白天. 面向误诊提示的疾病-症状语义网构建[J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] 李雄飞, 冯婷婷, 骆实, 张小利. 基于递归神经网络的自动作曲算法[J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] 刘杰, 张平, 高万夫. 基于条件相关的特征选择方法[J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] 王旭, 欧阳继红, 陈桂芬. 基于多重序列所有公共子序列的启发式算法度量多图的相似度[J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[9] 杨欣, 夏斯军, 刘冬雪, 费树岷, 胡银记. 跟踪-学习-检测框架下改进加速梯度的目标跟踪[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[10] 刘雪娟, 袁家斌, 许娟, 段博佳. 量子k-means算法[J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[11] 曲慧雁, 赵伟, 秦爱红. 基于优化算子的快速碰撞检测算法[J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[12] 李嘉菲, 孙小玉. 基于谱分解的不确定数据聚类方法[J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[13] 邵克勇, 陈丰, 王婷婷, 王季驰, 周立朋. 无平衡点分数阶混沌系统全状态自适应控制[J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
[14] 王生生, 王创峰, 谷方明. OPRA方向关系网络的时空推理[J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243.
[15] 马淼, 李贻斌. 基于多级图像序列和卷积神经网络的人体行为识别[J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发