Decentralized Attention-based Personalized Human Mobility Prediction

Abstract

Human mobility prediction is essential to a variety of human-centered computing applications achieved through upgrading of location-based services (LBS) to future-location-based services (FLBS). Previous studies on human mobility prediction have mainly focused on centralized human mobility prediction, where user mobility data are collected, trained and predicted at the cloud server side. However, such a centralized approach leads to a high risk of privacy issues, and a real-time centralized system for processing such a large volume of distributed data is extremely difficult to apply. Moreover, a large and dynamic set of users makes the predictive model extremely challenging to personalize. In this paper, we propose a novel decentralized attention-based human mobility predictor in which 1) no additional training procedure is required for personalized prediction, 2) no additional training procedure is required for incremental learning, and 3) the predictor can be trained and predicted in a decentralized way. We tested our method on big data of real-world mobile phone user GPS and on Android devices, and achieved a low-power consumption and a good prediction accuracy without collecting user data in the server or applying additional training on the user side.