Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data
IEEE Transactions on Mobile Computing
Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
Periodic properties of user mobility and access-point popularity
Personal and Ubiquitous Computing
Mining call and mobility data to improve paging efficiency in cellular networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
Identification via location-profiling in GSM networks
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Identifying important places in people's lives from cellular network data
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
The Impact of Temporal Factors on Mobility Patterns
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Are call detail records biased for sampling human mobility?
ACM SIGMOBILE Mobile Computing and Communications Review
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Many of today's applications such as cellular network management, prediction and control of the spread of biological and mobile viruses, etc., depend on the modeling and prediction of human locations. However, having widespread wireless localization technology, such as pervasive cell-tower/GPS location estimation available for only the last few years, many factors that impact human mobility patterns remain under researched. Further more, many industries including telecom providers are still in need of low-cost and simple location/place prediction methods that can be implemented on a large scale. In this paper, we focus on "temporal factors" and demonstrate that they significantly impact randomness, size, and probability distribution of people's movements. We also use this information to make simple and inexpensive prediction models for subscribers' visited places. We monitored individuals for a month and divided days and hours into segments for each user to obtain probability distribution of their places for each segment of time intervals and observed major improvement in future "time-based" predictions of their location compared to when temporal factors were not considered. In addition to quantifying the improvement in place prediction, we show that significant improvements can actually be achieved through an intuitive division of time intervals with no added computational complexity.