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.

Fine-grained dynamic population estimation is in an increasingly high demand as it has numerous applications in wireless network engineering, urban planning, location-based services and mobile applications, and advertisement. In this paper, we introduce a framework that dynamically estimates the wireless subscriber population of an arbitrary fine-grained area based on the current cellular phone usage. This framework takes advantage of strong regularities, low variance, and low information entropy in human mobility and phone usage patterns; thus simplifying the estimation for wireless carriers and other big entities while maintaining a high accuracy. We implemented our `regularity-based' framework using empirical data. Comparison with experimentally collected data shows a significant improvement in the accuracy of population estimation compared to population count based on cellular phone usage.

Location-based services enabled by broadband wireless access play an increasingly important role in people's daily navigation and coordination. Location-based applications frequently report user location to Internet servers, and location accuracy is essential to the utility of these services. However, regular and accurate location updates impact efficient usage of network resources and also users' privacy, which is not directly observed by the users. In this paper, we conducted a large scale measurement study to understand location accuracy and communication frequency of such applications. We found that while most location reports are accurate enough, some applications run in the background, reporting user locations with a higher accuracy and frequency than needed for the user's purpose. For example, while from the user perspective, hourly zip code updates are enough location information to obtain local weather conditions, some weather forecast applications report user location every five minutes or less and at GPS-level accuracy. We found that location reports from many phone applications are accurate enough and frequent enough to enable the inference of users home and work addresses, and potentially their identity, exacerbating user privacy concerns.