STMM: Semantic and Temporal-Aware Markov Chain Model for Mobility Prediction

Abstract

Information theoretic measures and probabilistic techniques have been applied successfully to human mobility datasets to show that human mobility is highly predictable up to an upper bound of 95 % prediction accuracy. Motivated by this finding, we propose a novel Semantic and Temporal-aware Mobility Markov chain (STMM) model to predict anticipated mobility of a target individual. Despite being an extensively studied topic in recent years, human mobility prediction by the vast majority of existing studies have mostly focused on predicting the geo-spatial context, and in rare cases, the temporal context of human mobility. We argue that an explicit and comprehensive analysis of semantic and temporal context of users’ mobility is necessary for realistic understanding and prediction of mobility. In line with this, our proposed model simultaneously utilizes semantic and temporal features of a target individual’s historical mobility data to predict their mobility, given his/her current location context (time and semantic tag of the location). We evaluate our approach on a real world GPS trajectory dataset.