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With the popularity of mobile devices and the maturity of location technologies,a variety of location-based ap— plications emerge.In order to make such applications provide users with accurate location-based services,timely,accu— rately,reliably forecast uncertainty track of moving objects is particularly important.Currently,most traditional methods predict the destination of a given trajectory by calculating the similarity between tWO trajectories,and this algorithm does not fully consider the drawbacks of backward and forward linkages between trajectory time series,resulting in a larger prediction erro~Theory demonstrates that the Markov model to deal with time series data have very good effect. Therefore,we proposed a sparse trajectory destination prediction algorithm based on Markov mode1.Meanwhile we in— vestigated a new partitioning strategies for the sample motion space——一grid partitioning based on K—d tree.The experi— mental results show that compared with traditional methods,using Markov model to predict the destination of the trajectory will significantly improve the accuracy of the algorithm,and the predicted time will be shortened.

一种基于马尔可夫模型的稀疏轨迹终点预测算法 (Sparse Trajectory Destination Prediction Algorithm Based on Markov Model).