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Co-attention trajectory prediction by mining heterogeneous interactive relationships

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Abstract

In the multi-modal spatio-temporal semantic trajectory prediction, if we can make full use of its multi-modal characteristics and heterogeneous interaction, the prediction accuracy can be significantly improved. However, the existing methods have some thorny problems. Firstly, the process of constructing an effective and semantically rich heterogeneous information interaction scene is very complex. Secondly, it is difficult to obtain interaction path instances with high quality, high relevance and high reliability. Finally, how to introduce path instances into trajectory prediction is also a difficulty. This paper proposes a common attention prediction method based on heterogeneous information network (HBCAPM). Firstly, the heterogeneous information network is constructed to make effective use of the multi-modal features in the trajectory and the heterogeneous interaction between features. Secondly, HBCAPM mines multi-source heterogeneous nodes and interaction patterns in heterogeneous information networks. Then, a path generation algorithm based on matrix decomposition and rankwalk is designed to obtain high-quality path instances. Finally, a collaborative semantic enhancement mechanism based on attention mechanism is designed to obtain the collaborative semantics of users, destinations and meta-paths. In addition, a large number of experiments on two real data sets show that HBCAPM significantly improves the effectiveness of various evaluation criteria. Compared with the latest method we discussed, the prediction accuracy is improved by 1.28% and the average distance error is reduced by 65.5 m.

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Code Availability

The source code can be downloaded from http://github.com/liu-jie-cumt/HBCAPM.

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Acknowledgements

We thank anonymous reviewers for valuable suggestions.

Funding

This work was supported in part by “the Double-First-Rate Special Fund for Construction of China University of Mining and Technology, No. 2018ZZCX14” and “the Fundamental Research Funds for the Central Universities, No. 2019XKQYMS88”. The funder had no role in study design, data collection and preparation of the manuscript.

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Authors and Affiliations

  1. School of Computer Science, China University of Mining and Technology, Jinshan, Xuzhou, 221116, Jiangsu, China

    Lei Zhang, Jie Liu, Bailong Liu, Shaojie Zhu & Jiyong An

  2. Engineering Research Center of Mine Digitalization (Ministry of Education), China University of Mining and Technology, Jinshan, Xuzhou, 221116, Jiangsu, China

    Lei Zhang, Jie Liu, Bailong Liu, Shaojie Zhu & Jiyong An

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  1. Lei Zhang
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Contributions

LZ and JL conceived the prediction method, implemented the experiments, conducted the experimental result analysis, and wrote the paper; BL and SZ gathered data and performed experiments. BL and JA revised the paper. All authors have read and approved the final paper.

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Correspondence to Bailong Liu.

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The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article.

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The data sets can be downloaded from http://github.com/liu-jie-cumt/HBCAPM

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Zhang, L., Liu, J., Liu, B. et al. Co-attention trajectory prediction by mining heterogeneous interactive relationships. Multimed Tools Appl 82, 15345–15370 (2023). https://doi.org/10.1007/s11042-022-13942-5

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