Human-Computer Interaction and Pervasive Computing
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
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．
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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