A User Modeling Approach to Improving Estimation Accuracy in Location-tracking Applications

Location tracking systems are discrete in nature Location information about each moving object (MO) is sampled at certain points in time. To determine the location of a MO between location reports or sometime in the future, we have to estimate the location at that point in time using the location reports we already have. The sampling frequency could affect the system's estimation accuracy as well as operating costs. Poor estimation accuracy could also carry a cost. The objective is to maximize the estimation accuracy while minimizing the operating cost. In this paper we introduce the novel idea of using user modeling to improve the estimation accuracy of both the route and speed of a MO without the need to increase the sampling rate. We focus on a subset of moving objects we call Roving Users, or RUs for short. A RU is a human or human-controlled MO. The idea is that humans are creatures of habit. Knowing how a RU behaved in the past could help us estimate what he/she will do in the future. This knowledge could help us estimate the user location more accurately.