NLPMM: a Next Location Predictor with Markov Modeling
This work addresses location prediction for moving objects, which is incremental as it builds on Markov modeling with added features like time adaptation.
The paper tackles the problem of predicting next locations of moving objects using historical trajectory data, presenting NLPMM which incorporates individual and collective patterns, handles sparse data, and adapts to time periods, and reports it outperforms existing methods in experiments on a real dataset.
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.