计算机工程 ›› 2019, Vol. 45 ›› Issue (3): 188-196,201.doi: 10.19678/j.issn.1000-3428.0050177

• 人工智能及识别技术 • 上一篇    下一篇

基于RNN集成学习的个人轨迹恢复方法

鲁强,刘歆琦   

  1. 中国石油大学(北京) 石油数据挖掘北京市重点实验室,北京 102249
  • 收稿日期:2018-01-18 出版日期:2019-03-15 发布日期:2019-03-15
  • 作者简介:鲁强(1977—),男,副教授、博士,主研方向为空间数据挖掘、演化计算;刘歆琦,硕士研究生。
  • 基金项目:

    国家自然科学基金(61402532);中国石油大学(北京)青年基础科研基金(01JB0415)。

Personal Trajectory Recovery Method Based on RNN Ensemble Learning

LU Qiang,LIU Xinqi   

  1. Beijing Key Lab of Petroleum Data Mining,China University of Petroleum,Beijing 102249,China
  • Received:2018-01-18 Online:2019-03-15 Published:2019-03-15

摘要:

从多个轨迹数据库中连接并恢复出较为完整的个人轨迹对出行推荐和移动导航具有重要的意义。基于个人轨迹恢复,提出RNN集成学习方法。定义个人轨迹恢复的形式化模型,利用轨迹点数目采样模式将每个训练库划分为多个训练子库,并采用RNN网络模型描述个人轨迹的可拼接程度,使用集成学习方法构建多个RNN网络,以达到恢复个人轨迹的目的。实验结果表明,该方法可以较好地捕获轨迹时空连续性特征,实现个人轨迹恢复。

关键词: 轨迹恢复, 轨迹拼接, 集成学习, 神经网络, RNN网络

Abstract:

Connecting and restoring a relatively complete personal trajectory from multiple trajectory databases is important for travel recommendations and mobile navigation.Based on personal trajectory recovery,an integrated learning method for RNN is proposed.By defining a formal model of personal trajectory recovery,each training library is divided into multiple training sub-libraries by using the track point number sampling mode,and an RNN network model is used to describe the splicing degree of the personal trajectory.An integrated learning method is used to construct multiple RNN networks to achieve the goal of restoring personal trajectories.Experimental results show that this method can capture the temporal and spatial continuity of the trajectory and realize the personal trajectory recovery.

Key words: trajectory recovery, trajectory splicing, ensemble learning, neural network, RNN network

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