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Location prediction: a deep spatiotemporal learning from external sensors data

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Abstract

This paper proposes a deep multi-task learning framework to predict the next location from trajectories that are captured by external sensors (e.g., traffic surveillance cameras, or speed radars). The reported positions in such trajectories are sparse, due to the sparsity of the sensor distribution, and incomplete, because the sensors may fail to register the passage of objects. In this framework, we propose different preprocessing steps to align the trajectories representation and cope with a missing data problem. The multi-task learning approach is based on Recurrent Neural Networks. It utilizes both time and space information in the training phase to learn more meaningful representations, which boosts the learning performance of location prediction. The multi-task learning model, together with the preprocessing step, substantially improves the prediction performance. We conduct several experiments using a real dataset, and they demonstrate the validity of our multi-task learning model in terms of accuracy of 85.20%, which is more than 20% better than using a single-task learning model.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior —Brasil (CAPES) under Finance Code 001, Fundação de Apoio a Serviços Técnicos, Ensino e Fomento a Pesquisas (FASTEF) (Grant Number 31/2019) and in part by Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP No 8789771/2017).

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Authors and Affiliations

  1. Federal University of Ceará - Insight Data Science Lab, Campus do Pici, Bloco 952, Fortaleza, Ceará, Brazil

    Lívia Almada Cruz, Ticiana Linhares Coelho da Silva, José Antonio Fernandes de Macedo & José Soares da Silva

  2. University of Versailles, Versailles, France

    Karine Zeitouni

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  1. Lívia Almada Cruz
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  2. Karine Zeitouni
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  3. Ticiana Linhares Coelho da Silva
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  4. José Antonio Fernandes de Macedo
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  5. José Soares da Silva
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Correspondence to Lívia Almada Cruz.

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Cruz, L.A., Zeitouni, K., da Silva, T.L.C. et al. Location prediction: a deep spatiotemporal learning from external sensors data. Distrib Parallel Databases 39, 259–280 (2021). https://doi.org/10.1007/s10619-020-07303-0

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