Abstract
Prediction of future locations from traces of human mobility has significant implications for location-based services. Most existing research in this area focuses on predicting the next location or the destination rather than the entire route. This paper presents a temporal frequent-pattern tree (TFT) method for predicting future locations and routes. We evaluate the method using a real-world dataset containing location data from 50 users in a city. Our results show that for more than 91% of the users, the accumulated average distance between the actual and predicted locations is less than 1000 m (\(46\,\mathrm{m}<\mathrm{range}<1325\,\mathrm{m}\)). The results also show that the model benefits from similarities between users’ movement patterns.
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Cai, M., Yan, R., Doryab, A. (2022). Daily Trajectory Prediction Using Temporal Frequent Pattern Tree. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_29
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DOI: https://doi.org/10.1007/978-981-16-2380-6_29
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