Predicting mobility events on personal devices

Peddemors, Arjan J. H.; Eertink, Henk; Niemegeers, Ignas

doi:10.1016/j.pmcj.2009.11.001

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…KDE has been used in a previous study for modeling the waiting time distribution that a given place will be revisited [24] other contextual variables too).…”

mentioning

confidence: 99%

A probabilistic kernel method for human mobility prediction with smartphones

Tri

Dousse²,

Miettinen³

et al. 2015

Pervasive and Mobile Computing

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Human mobility prediction is an important problem which has a large number of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address modeling and application aspects. Building personalized location prediction models from smartphone data remains a technical challenge due to data sparsity, which comes from the complexity of human behavior and the typically limited amount of data available for individual users. To address this problem, we propose an approach based on kernel density estimation, a popular smoothing technique for sparse data. Our approach contributes to existing work in two ways. First, our proposed model can estimate the probability that a user will be at a given location at a specific time in the future, by using both spatial and temporal information via multiple kernel functions. Second, we also show how our probabilistic framework extends to a more practical task of location prediction for a time window in the future. Our approach is validated on an everyday life location datasets consisting of 133 smartphone users. Our method reaches an accuracy of 84% for the next hour, and an accuracy of 77% for the next three hours.

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“…KDE has been used in a previous study for modeling the waiting time distribution that a given place will be revisited [24] other contextual variables too).…”

mentioning

confidence: 99%

A probabilistic kernel method for human mobility prediction with smartphones

Tri

Dousse²,

Miettinen³

et al. 2015

Pervasive and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…This approach is, however, task-specific and potentially hard to be extended to a large number of contextual variables. As an alternative, ensemble methods represent a promising direction, as recently addressed in [36]. This work is limited to choosing a single best model rather than combining the (typically available) prior information from multiple models.…”

Section: Related Workmentioning

confidence: 99%

Where and what: Using smartphones to predict next locations and applications in daily life

Tri

Gática-Pérez

2014

Pervasive and Mobile Computing

116

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“…In these works, the combination method is task-specific and hard to be extended to a large number of contextual variables. As an alternative, ensemble methods represent a promising direction, as recently addressed in [19]. This work is, however, limited to choosing a single best model rather than combining the (typically available) prior information from multiple models.…”

Section: Related Workmentioning

confidence: 99%

Contextual conditional models for smartphone-based human mobility prediction

Tri

Gática-Pérez

2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

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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: what is the next place a user will visit? and 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.

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Predicting mobility events on personal devices

Cited by 13 publications

References 18 publications

A probabilistic kernel method for human mobility prediction with smartphones

A probabilistic kernel method for human mobility prediction with smartphones

Where and what: Using smartphones to predict next locations and applications in daily life

Contextual conditional models for smartphone-based human mobility prediction

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