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- W2385206985 abstract "Concerning the data sparsity problem of moving object' s trajectory prediction, i. e., the available historical trajectories are far from enough to cover all possible query trajectories that can obtain predicted destinations, a Trajectory Prediction Algorithm suffer from Data Sparsity based on Iterate Grid Partition and Entropy Estimation(TPDS-IGPEE) was proposed. Firstly, the moving region of trajectories was iteratively divided into a two-dimensional plane grid graph, and then the original trajectories were mapped to the grid graph so that each trajectory could be represented as a grid sequence.Secondly, an L-Z entropy estimator was used to calculate the entropy value of trajectory sequence, and a new trajectory space was generated by doing trajectory synthesis based on trajectory entropy. At last combining with the Sub-Trajectory Synthesis(Sub Syn) algorithm, sparse trajectory prediction was implemented. The experimental results show when trajectory completed percentage increases towards 90%, the coverage of the Baseline algorithm decreases to almost 25%. TPDS-IGPEE algorithm successfully coped with it as expected with only an unnoticeable drop in coverage, and could constantly answer almost 100% of query trajectories. And TPDS-IGPEE algorithm' s prediction accuracy was generally 4% higher than Baseline algorithm. At the same time, the prediction time of Baseline algorithm to 100 ms was too long, while the prediction time of TPDS-IGPEE algorithm could be negligible(10 μs). TPDS-IGPEE algorithm can make an effective prediction for the sparse trajectory,providing much wider predicting range, faster predicting speed and better predicting accuracy." @default.
- W2385206985 created "2016-06-24" @default.
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- W2385206985 date "2015-01-01" @default.
- W2385206985 modified "2023-09-24" @default.
- W2385206985 title "Sparse trajectory prediction method based on iterative grid partition and entropy estimation" @default.
- W2385206985 hasPublicationYear "2015" @default.
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