ABSTRACT Made possible by the availability of spatio-temporal data collected by smart phones and other smart devices, understanding people's mobility patterns has become one of the most promising locationbased services in the past few years, providing various businesswise application possibilities. The simplest version of possibilities is to predict where a user will go next. In this paper, we present a novel approach that goes beyond predicting users' next location and is able to predict their entire mobility patterns. Building on previous work, our models are based on statistical Markov state-space models. In our approach, however, we add temporal information (''arrival profiles'' and ''probability of stay'' profiles) explicitly to the models. Using Monte Carlo simulations on these models enables us to predict multiple future locations, including residence times. We evaluate the models on real-world data sets (1.5 year personally collected mobile phone raw GPS logs and a publicly available Nokia Mobile Data Challenge data set) using different evaluation metrics. An extensive evaluation shows that our proposed methods have better predictive power (higher recall) than standard state-space models. INDEX TERMS Mobility patterns prediction, next location prediction, Markov model, Monte Carlo integration, spatio-temporal analysis.
Kažic et al. REF proposed a new method, based on the statistical Markov state space, it adds the time information (arrival information, stay probability) explicitly to the model, which enables the model can dynamically predict the probability for users to visit the next location.
Predicting Users’ Mobility Using Monte Carlo Simulations
IEEE Access
IEEE Access
Computer Science