An improved Markov method for prediction of user mobility

The developments of Information and Communication Technology (ICT) and Internet of Things (IoT) are being used to enhance quality, performance and interactivity of urban services. Benefited from the widespread adoption of mobile devices, we can collect amount of mobile data for user mobility analysis. Mining hidden information from users' mobile data is important for builders of smart city to provide better location-based service. This paper focuses on two classical domain-independent prediction models and one improved Markov model that are capable of estimating the next location. By using 27-day-long traffic data of mobile network, we extract trajectories of 4914 individuals for experiments. We find that the original Markov algorithm has a better performance in resource consumption than LZ family algorithms, but its prediction accuracy is lower than prediction accuracy of LeZi Update and Active LeZi algorithm. In order to improve the prediction accuracy of Markov and overcome drawbacks of traditional prediction algorithms, we present a new method based on Markov, which considers both temporal and spatial factors. Extensive experiments demonstrate our improved method has a better performance in location prediction. In addition, we further study the relationship between prediction accuracy and trajectory's regularity, to identify the most suitable prediction algorithm for a trajectory.