NLPMM: a Next Location Predictor with Markov Modeling

March 16, 2020 · Declared Dead · 🏛 Pacific-Asia Conference on Knowledge Discovery and Data Mining

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Authors Meng Chen, Yang Liu, Xiaohui Yu arXiv ID 2003.07037 Category cs.AI: Artificial Intelligence Cross-listed cs.SI Citations 118 Venue Pacific-Asia Conference on Knowledge Discovery and Data Mining Last Checked 3 months ago
Abstract
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.
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