Moving Trajectory Prediction Algorithm Based on Position Similarity and Markov Model
Real time and accurate mobile trajectory prediction is an important support for the development of emerging mobile services.At present,Markov model is mostly used to predict the moving trajectory,but the Markov model does not make full use of the historical trajectory information.Therefore,this paper proposes a moving trajectory prediction algorithm based on position simi-larity and Markov model.Firstly,the original trajectory data is serialized by iterative meshing method.Then,the similar historical trajectories in the historical trajectory set are found according to the user's current trajectory position.Finally,the transition probabil-ity matrix is established from the similar historical trajectories to complete the prediction of the user's future region.Experimental re-sults on large-scale datasets show that the average prediction accuracy of this method is improved by 13.8%compared with the tradi-tional Markov model.
recommendation servicetrajectory predictionlocation similarityMarkov model
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