Fast Time-Aware Sparse Trajectories Prediction with Tensor Factorization

Lei ZHANG  Qingfu FAN  Guoxing ZHANG  Zhizheng LIANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.7   pp.1959-1962
Publication Date: 2018/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8017
Type of Manuscript: LETTER
Category: Data Engineering, Web Information Systems
Keyword: 
trajectory prediction,  data sparsity,  tensor factorization,  

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Summary: 
Existing trajectory prediction methods suffer from the “data sparsity” and neglect “time awareness”, which leads to low accuracy. Aiming to the problem, we propose a fast time-aware sparse trajectories prediction with tensor factorization method (TSTP-TF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the original trajectory space. It resolves the sparse problem of trajectory data and makes the new trajectory space more reliable. Then, we introduce multidimensional tensor modeling into Markov model to add the time dimension. Tensor factorization is adopted to infer the missing regions transition probabilities to further solve the problem of data sparsity. Due to the scale of the tensor, we design a divide and conquer tensor factorization model to reduce memory consumption and speed up decomposition. Experiments with real dataset show that TSTP-TF improves prediction accuracy generally by as much as 9% and 2% compared to the Baseline algorithm and ESTP-MF algorithm, respectively.