Prediction of Traffic Density and Interest Using Real Time Mobile Traffic Data

With the increasing number of mobile users and applications, data services and on-demand suggestions gradually play an important role in mobile life. In this work, based on the wireless personal communication network, we propose a novel prediction method of user traffic density and personal interest based on real time mobile traffic data and wireless positioning information. Service provider could send precise information to target users based on the prediction to ensure the quality and push service. Then we execute a simulation using real traffic data around Tsinghua University obtained from network service provider, and evaluate the accuracy of the prediction. Simulation results indicate that, our proposed method by using the traffic data and position jointly can significantly increase the rate of successful recommendation.