ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (7): 1357-1369.doi: 10.7544/issn1000-1239.2019.20170662

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Location Prediction Model Based on Transportation Mode and Semantic Trajectory

Zhang Jinglei1,2, Shi Hailong1, Cui Li1   

  1. 1(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190);2(University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2019-07-01

Abstract: The research of existing location prediction technologies focuses on the mining and analysis of trajectory data, but there still exists space for research that how to improve the location prediction result with mining the information contained in trajectory data and exogenous data. In this paper, we propose a new location prediction model of mining the semantic trajectory and the transportation mode. On one hand, this model firstly mines the similar users according to the semantic trajectory, then establishes the frequent pattern set combined with the individual semantic trajectory and location trajectory of similar users, and finally obtains the candidate future location prediction set; On the other hand, it recognizes the future transportation mode, then combines the history transportation mode and historical location trajectory to predict the future location set with building Markov model. Finally the prediction result will be obtained with these two candidate sets. This method not only uses the semantic trajectory to mine the behavior of similar users, but also combines the transportation mode to overcome the limitation of location trajectory. The experimental result shows that the accuracy of this model can reach 86%, and 5% higher than that of the unmatched travel model under different frequent pattern support with the daily trajectory data. Therefore, it is effective to improve the location prediction result with this model.

Key words: transportation mode recognition, frequent pattern mining algorithm, semantic trajectory, location trajectory, location prediction

CLC Number: