Jyotish: A novel framework for constructing predictive model of people movement from joint Wifi/Bluetooth trace

IEEE Trans. on Mobile Comput.

2014

Abstract-The location tracking functionality of modern mobile devices provides unprecedented opportunity to the understanding of individual mobility in daily life. Instead of studying raw geographic coordinates, we are interested in understanding human mobility patterns based on sequences of place visits which encode, at a coarse resolution, most daily activities. This paper presents a study on place characterization in people's everyday life based on data recorded continuously by smartphones. First, we study human mobility from sequences of place visits, including visiting patterns on different place categories. Second, we address the problem of automatic place labeling from smartphone data without using any geo-location information. Our study on a large-scale data collected from 114 smartphone users over 18 months confirms many intuitions, and also reveals findings regarding both regularly and novelty trends in visiting patterns. Considering the problem of place labeling with 10 place categories, we show that frequently visited places can be recognized reliably (over 80%) while it is much more challenging to recognize infrequent places.

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Section: Related Workmentioning

confidence: 99%

The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

IEEE Trans. on Mobile Comput.

2014

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“…Note that the resolution of location data inferred by cell tower IDs is relatively low compared to GPS data, which is used in this paper, resulting in uncertainty on the user position. Besides cell tower and GPS, short distance wireless network data (e.g., Bluetooth and WiFi) can also be used for positioning, with some advantages such as high spatial resolution and the ability to work in indoor environments [16,17].…”

Section: Related Workmentioning

confidence: 99%

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

Pervasive and Mobile Computing

2014

115

“…WiFi access points are another popular data source for localizing and predicting user mobility [23]. Recently, Vu et al proposed a framework to extract places from WiFi records, and predict human movement using joint WiFi and Bluetooth traces [24].…”

Section: Related Workmentioning

confidence: 99%

Contextual conditional models for smartphone-based human mobility prediction

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

2012