Contextual Conditional Models for Smartphone-based Human Mobility Prediction
| Type of publication: | Conference paper |
| Citation: | Do_UBICOMP_2012 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of the 14th ACM International Conference on Ubiquitous Computing |
| Year: | 2012 |
| Month: | September |
| Abstract: | Human behavior is often complex and context-dependent. This paper presents a general technique to exploit this ``multidimensional'' contextual variable for human mobility prediction. We use an ensemble method, in which we extract different mobility patterns with multiple models and then combine these models under a probabilistic framework. The key idea lies in the assumption that human mobility can be explained by several mobility patterns that depend on a subset of the contextual variables and these can be learned by a simple model. We showed how this idea can be applied to two specific online prediction tasks: \textit{what is the next place a user will visit?} and \textit{how long will he stay in the current place?}. Using smartphone data collected from 153 users during 17 months, we show the potential of our method in predicting human mobility in real life. |
| Keywords: | |
| Projects: |
Idiap NOKIA |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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