  In this paper, we solve the problem of predicting the next locations of the
moving objects with a historical dataset of trajectories. We present a Next
Location Predictor with Markov Modeling (NLPMM) which has the following
advantages: (1) it considers both individual and collective movement patterns
in making prediction, (2) it is effective even when the trajectory data is
sparse, (3) it considers the time factor and builds models that are suited to
different time periods. We have conducted extensive experiments in a real
dataset, and the results demonstrate the superiority of NLPMM over existing
methods.
