Sparse Trajectory Prediction Method Based on Entropy Estimation

Abstract

Most of the existing algorithms cannot effectively solve the data sparse problem of trajectory prediction. This paper proposes a novel sparse trajectory prediction method based on L-Z entropy estimation. Firstly, the moving region of trajectories is divided into a two-dimensional plane grid graph, and then the original trajectories are mapped to the grid graph so that each trajectory can be represented as a grid sequence. Secondly, an L-Z entropy estimator is used to calculate the entropy value of each grid sequence, and then the trajectory which has a comparatively low entropy value is segmented into several sub-trajectories. The new trajectory space is synthesised by these sub-trajectories based on trajectory entropy. The trajectory synthesis can not only resolve the sparse problem of trajectory data, but also make the new trajectory space more credible. In addition, the trajectory scale is limited in a certain range. Finally, under the new trajectory space, Markov model and Bayesian Inference is applied to trajectory prediction with data sparsity. The experiments based on the taxi trajectory dataset of Microsoft Research Asia show the proposed method can make an effective prediction for the sparse trajectory. Compared with the existing methods, our method needs a smaller trajectory space and provides much wider predicting range, faster predicting speed and better predicting accuracy.