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Solving the data sparsity problem in destination prediction

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Abstract

Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, almost all the existing techniques use various kinds of extra information such as road network, proprietary travel planner, statistics requested from government, and personal driving habits. Such extra information, in most circumstances, is unavailable or very costly to obtain. Thereby we approach the task of destination prediction by using only historical trajectory dataset. However, this approach encounters the “data sparsity problem”, i.e., the available historical trajectories are far from enough to cover all possible query trajectories, which considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) to address the data sparsity problem. SubSyn first decomposes historical trajectories into sub-trajectories comprising two adjacent locations, and then connects the sub-trajectories into “synthesised” trajectories. This process effectively expands the historical trajectory dataset to contain much more trajectories. Experiments based on real datasets show that SubSyn can predict destinations for up to ten times more query trajectories than a baseline prediction algorithm. Furthermore, the running time of the SubSyn-training algorithm is almost negligible for a large set of 1.9 million trajectories, and the SubSyn-prediction algorithm runs over two orders of magnitude faster than the baseline prediction algorithm constantly.

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Notes

  1. The demonstration system can be accessed following this link: http://spatialanalytics.cis.unimelb.edu.au/subsyndemo/.

  2. Note the difference between \(p_{i\rightarrow j}\) and \(p_{ij}\). The latter (without the arrow) is the transition probability defined in Markov model, and its definition was given in Eq. (4).

  3. Consecutive cells are two cells next to each other in a trajectory; adjacent cells are two cells next to each other in a grid.

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Acknowledgments

This work is supported by Australian Research Council (ARC) Discovery Project DP130104587 and Australian Research Council (ARC) Future Fellowships Project FT120100832.

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Authors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Andy Yuan Xue, Jianzhong Qi, Rui Zhang, Jin Huang & Yuan Li

  2. Microsoft Research Asia, Beijing, People’s Republic of China

    Xing Xie

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  1. Andy Yuan Xue
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Correspondence to Rui Zhang.

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Xue, A.Y., Qi, J., Xie, X. et al. Solving the data sparsity problem in destination prediction. The VLDB Journal 24, 219–243 (2015). https://doi.org/10.1007/s00778-014-0369-7

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