Abstract
Human mobility prediction in ubiquitous computing is the ability of a system to forecast the anticipated movement of an individual or a group of persons. This interdisciplinary problem has gained traction in fields of academic and industrial research mainly because it is fundamental to achieving system efficiency and marketing efficacy in many applications. This study seeks to develop a novel heuristic technique that predicts the actual geo-spatial locations associated with the most probable semantic tags of locations (e.g. restaurant) that individuals are likely to visit. The intuition of this work lies in the fact that, for any given probable future semantic tag there exists multiple geo-spatial locations associated with it, hence the need to disambiguate the actual destination location. We develop an algorithm \( STS \_ predict \), that exploits the spatio-temporal relationships between the current location of a target individual and candidate geo-spatial locations associated with future semantic tags to predict the actual destination location. We evaluate our approach on a real world GPS trajectory dataset.
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Abdel-Fatao, H., Li, J., Liu, J., Ashfaqur, R. (2016). An Effective Spatio-Temporal Approach for Predicting Future Semantic Locations. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_22
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DOI: https://doi.org/10.1007/978-3-319-46922-5_22
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