Prediction of User Mobility Pattern on a Network Traffic Analysis Platform

Abstract

The mobile Internet brings tremendous opportunities for researchers to analyze user mobility pattern, which is of great importance for Internet Service Providers (ISP) to provide better location-based services. This paper focuses on predicting user mobility patterns based on their different mobility characteristics. For that, we collect real-world data from Long Term Evolution (LTE) mobile network by a specially developed network traffic analysis platform followed by clustering the user into stationary one or mobile one with a location-entropy-based method for distinguishing groups with distinct mobility characteristics, and then we present the tailored Intelligent Time Division (ITD) method and Time-Based Markov (TBM) predictor for the location prediction of stationary and mobile users respectively. Extensive experiments demonstrate the effectiveness and better performance of our proposed methods compared with the baselines, as well as the adaptabilities of different predictors according to individual's mobility characteristics.