›› 2019, Vol. 58 ›› Issue (5): 17-25.doi: 10.13471/j.cnki.acta.snus.2019.05.003

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Trajectory accompanying patterns mining method based on spatialtime segmentation and word vector similarity

LI Xin   

  1. Collaborative Innovation Center of Three-aspect Coordination of Central Plain Economic Region∥College of Resource and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
  • Received:2018-10-22 Online:2019-09-25 Published:2019-09-25

Abstract:

A trajectory big data mining method based on spatial-time Hausdorff distance segmentation and word vector similarity is designed in this paper. It can analyze the accompanying rules accurately and efficiently, and truly reflect the flow behavior of people and vehicles. The one-to-three Hausdorff distance algorithm based on time series characteristics can exclude the reverse trajectory and mine the accompanying relations. The set of trajectory segments separated by the time sliding window can establish the basis for the similarity measurement. The method of trajectory similarity measurement based on word vector establishes the analogical relationship between trajectory and sentences, reflects the spatial, temporal and directional heterogeneity of the trajectory, and accurately measures the structural similarity of the accompanying trajectories. It provides a reference for exploring similar objectives, detecting frequent paths as well as other related applications.

Key words: trajectory data, accompanying pattern, Hausdorff distance, word vector, trajectory similarity

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