LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Cognitive personal positioning based on activity map and adaptive particle filter
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
IEEE Transactions on Information Theory
Universal prediction of individual sequences
IEEE Transactions on Information Theory
Mining trajectory patterns using hidden Markov models
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
A “semi-lazy” approach to probabilistic path prediction in dynamic environments
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Extrapolating sparse large-scale GPS traces for contact evaluation
Proceedings of the 5th ACM workshop on HotPlanet
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
An unsupervised learning approach to social circles detection in ego bluetooth proximity network
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
How long are you staying?: predicting residence time from human mobility traces
Proceedings of the 19th annual international conference on Mobile computing & networking
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The ability to foresee the next moves of a user is crucial to ubiquitous computing. Disregarding major differences in individuals' routines, recent ground-breaking analysis on mobile phone data suggests high predictability in mobility. By nature, however, mobile phone data offer very low spatial and temporal resolutions. It remains largely unknown how the predictability changes with respect to different spatial/temporal scales. Using high-resolution GPS data, this paper investigates the scaling effects on predictability. Given specified spatial-temporal scales, recorded trajectories are encoded into long strings of distinct locations, and several information-theoretic measures of predictability are derived. Somewhat surprisingly, high predictability is still present at very high spatial/temporal resolutions. Moreover, the predictability is independent of the overall mobility area covered. This suggests highly regular mobility behaviors. Moreover, by varying the scales over a wide range, an invariance is observed which suggests that certain trade-offs between the predicting accuracy and spatial-temporal resolution are unavoidable. As many applications in ubiquitous computing concern mobility, these findings should have direct implications.