Mobility episode detection from CDR's data using switching Kalman filter

The detection of stay-jump-and-moving movement episodes using only cellular data is a big challenge due to the nature of the data. In this article, we propose a method to automatically detect the movement episodes (stay-jump-and-moving) from sparsely sampled spatio-temporal data, in our case Call Detail Records (CDRs), using switching Kalman filter with a new integrated movement model and cellular coverage optimization approach. The algorithm is capable of estimating the movement episodes and classifying the trajectory sequences associated to a stay, a jump or a moving action. The result of this approach can be beneficial for applications using cellular data related to traffic management, mobility profiling, and semantic enrichment.