Time-Clustering-Based Place Prediction for Wireless Subscribers

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 underresearched. Furthermore, 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.