Human-Computer Interaction and Pervasive Computing |
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Moving trajectory prediction model based on double layer multi-granularity knowledge discovery |
WANG Liang, YU Zhi-wen, GUO Bin |
1. School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China;
2. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China |
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Abstract
An online trajectory prediction model was proposed based on multi-granularity knowledge discovery from double layers in view of the fact that the sparsity and heterogeneity in spatial distribution of reallife moving trajectory pose a challenge to model and predict moving trajectory. The operation of multi-granularity modeling and pattern mining were conducted for existing moving trajectory data on coarse/fine grained semantic layers, respectively. A hybrid prediction of online query moving trajectory can be achieved by leveraging the manipulation of matching and output complementary of online moving trajectory based on bi-layer semantic patterns. The experimental results on real data sets show that the proposed method can effectively improve the prediction accuracy and extend the range of predictable trajectory for sparse data.
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Published: 25 April 2017
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Cite this article:
WANG Liang, YU Zhi-wen, GUO Bin. Moving trajectory prediction model based on double layer multi-granularity knowledge discovery. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 669-674.
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基于双层多粒度知识发现的移动轨迹预测模型
针对现实移动轨迹空间分布上的稀疏性、不均匀性对移动轨迹建模与预测方面带来的挑战性问题,提出基于双层多粒度移动轨迹知识发现的在线轨迹预测模型.分别在粗/细粒度语义层对移动轨迹数据进行多粒度建模与模式挖掘,基于粗/细粒度语义表达之间的关联包含关系建立双层移动模式映射索引结构.通过在双层语义模式上对在线移动轨迹进行匹配与输出合并互补操作,实现对在线查询移动轨迹的混合预测.在真实数据集上的实验结果表明,采用提出的方法能够有效地提升预测精度,可以扩展稀疏数据下的可预测轨迹范围.
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