Learning significant locations and predicting user movement with GPS

Ashbrook, Daniel; Starner﻿﻿, Thad

doi:10.1109/iswc.2002.1167224

Cited by 222 publications

(180 citation statements)

References 8 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…Finally, as mentioned in Section 5, our work subsumes related work in user modeling and movement by Ashbrook [10] and Patterson [11].…”

Section: Homementioning

confidence: 90%

“…Two probabilistic models have been proposed in the recent literature for describing outdoor movement routines of a user [10,11]. We will briefly review them and point out their fundamental inadequacies with respect to the set of requirements laid out above.…”

Section: Inference Enginesmentioning

confidence: 99%

“…In Section 6 we will evaluate the new model with respect to our needs. On the left, a second order Markov Model (2MM) from [10] showing dependencies between observed and hidden variables. Shaded nodes are observed.…”

Section: Inference Enginesmentioning

confidence: 99%

“…Ashbrook, et al have proposed using a second order Markov model (2MM -see Figure 3-left) as a predictive tool for reasoning about likely destinations toward which a user may next be traveling [10]. The system logs continuous GPS signals, extracts places where the user seems to have stopped for a significant amount of time and then clusters them into significant locations.…”

Section: Previous Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services

Patterson

Liao

Gajos

et al. 2004

UbiComp 2004: Ubiquitous Computing

127

View full text Add to dashboard Cite

Abstract. We present an automated transportation routing system, called "Opportunity Knocks," whose goal is to improve the efficiency, safety and independence of individuals with mild cognitive disabilities. Our system is implemented on a combination of a Bluetooth sensor beacon that broadcasts GPS data, a GPRS-enabled cell-phone, and remote activity inference software. The system uses a novel inference engine that does not require users to explicitly provide information about the start or ending points of their journeys; instead this information is learned from users' past behavior. Futhermore, we demonstrate how route errors can be detected and how the system helps to correct the errors with real-time transit information. In addition we present a novel solution to the problem of labeling positions with place names.

show abstract

“…Finally, as mentioned in Section 5, our work subsumes related work in user modeling and movement by Ashbrook [10] and Patterson [11].…”

Section: Homementioning

confidence: 90%

Section: Inference Enginesmentioning

confidence: 99%

Section: Inference Enginesmentioning

confidence: 99%

Section: Previous Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services

Patterson

Liao

Gajos

et al. 2004

UbiComp 2004: Ubiquitous Computing

127

View full text Add to dashboard Cite

show abstract

“…A closely related work to ours has been reported in [11], where a GPS system is used to collect location information. The proposed system then automatically clusters GPS data taken into meaningful locations at multiple scales.…”

Section: Prior Workmentioning

confidence: 97%

An Online Adaptive Model for Location Prediction

Anagnostopoulos

Hadjiefthymiades

2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Abstract. Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal online clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.

show abstract