Abstract
Research on human mobile behavior is becoming more available and important. One of the key challenges is how to divide long and continuous trajectory sequences into meaningful segments which builds a foundation for user similarity measure, trajectory data management and routine mining. While in traditional research trajectory sequence is segmented on basis of fixed time window or spatiotemporal criteria. In this paper, we propose a probabilistic topic model considering the spatial property and temporal Markov property of human mobility to address the problem of topic segmentation in human mobile behavior: automatically segmenting trajectory sequence into meaningful segments. The trajectory segments reflect high-level semantics for understanding human mobile behavior and can be used for higher-level applications. We consider one synthetic dataset and one real-life human dataset collected by mobile phones to evaluate our model. Results show that our model has good results in segmentation and outperforms traditional methods for practical purposes especially in learning long duration routines.
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Xing, X., Li, M., Hu, W., Huang, W., Song, G., Xie, K. (2014). A Spatial-temporal Topic Segmentation Model for Human Mobile Behavior. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_28
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DOI: https://doi.org/10.1007/978-3-319-08010-9_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
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