Spatio-temporal human mobility prediction based on trajectory data mining for resource management in mobile communication networks

Abstract

In the future mobile communication, communication based on various mobility models is expected. In 5G mobile communication network that can flexibly allocate network resources, it is necessary to predict traffic demands in order to appropriately allocate network resources. Therefore, it is important to predict the behavior of spatio-temporal mobility in order to appropriately allocate network resources. The pervasiveness of mobile devices based services leading to an increasing volume of spatiotemporal datasets and to the opportunity of discovering usable knowledge about mobility behavior. This knowledge is useful to provide stable communication to mobile networks expected to increase traffic flow. In this paper, we propose a method to grasp the behavior of the mobility in spatio-temporal by mining the trajectory data of the mobility obtained from the GPS data to predict the future mobility of the user from frequent patterns. We propose a mining and prediction algorithm that employs the huge amount of trajectory data. We apply sequential pattern mining algorithms including PrefixSpan and BIDE to obtain frequent trajectory patterns from trajectory database. We evaluate the proposed method using actual trajectory dataset, Geolife project, and demonstrate that the proposed method successfully extracts sufficient number of frequent trajectory patterns to predict the future trajectory of mobility.