Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120136
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Type: Conference paper
Title: On prediction of user destination by sub-trajectory understanding: a deep learning based approach
Author: Zhao, J.
Xu, J.
Zhou, R.
Zhao, P.
Liu, C.
Zhu, F.
Citation: CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018 / Cuzzocrea, A., Allan, J., Paton, N., Srivastava, D., Agrawal, R., Broder, A., Zaki, M., Candan, S., Labrinidis, A., Schuster, A., Wang, H. (ed./s), pp.1413-1422
Publisher: Association for Computing Machinery (ACM)
Publisher Place: New York
Issue Date: 2018
ISBN: 9781450360142
Conference Name: ACM International Conference on Information and Knowledge Management (CIKM) (22 Oct 2018 - 26 Oct 2018 : Torino, Italy)
Statement of
Responsibility: 
Jing Zhao, Jiajie Xu, S, Rui Zhou, Pengpeng Zhao, Chengfei Liu, Feng Zhu
Abstract: Destination prediction is known as an important problem for many location based services (LBSs). Existing solutions generally apply probabilistic models to predict destinations over a sub-trajectory, but their accuracies in fine-granularity prediction are always not satisfactory due to the data sparsity problem. This paper presents a carefully designed deep learning model called TALL model for destination prediction. It not only takes advantage of the bidirectional Long Short-Term Memory (LSTM) network for sequence modeling, but also gives more attention to meaningful locations that have strong correlations w.r.t. destination by adopting attention mechanism. Furthermore, a hierarchical model that explores the fusion of multi-granularity learning capability is further proposed to improve the accuracy of prediction. Extensive experiments on Beijing and Chengdu real datasets finally demonstrate that our proposed models outperform existing methods without considering external features.
Keywords: Trajectory prediction; trajectory embedding; deep learning
Rights: © 2018 Association for Computing Machinery
RMID: 0030109992
DOI: 10.1145/3269206.3271708
Grant ID: http://purl.org/au-research/grants/arc/DP160102412
http://purl.org/au-research/grants/arc/DP170104747
http://purl.org/au-research/grants/arc/DP180100212
Appears in Collections:Computer Science publications

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