Mining moving patterns for predicting next location

Highlights：

• We propose three models (PMM, GMM and RMM) and combine them in different ways to obtain new models to predict the next location of a moving object.

• GMM uses all available trajectories to discover collective patterns; PMM models the individual patterns of each moving object using its own past trajectories; RMM clusters the trajectories to mine the movement patterns.

• Based on the observation that the movement patterns often change over time, we propose methods that can capture the relationships between the patterns in different time periods, and use this knowledge to build more refined models.

• To the best of our knowledge, the proposed models are the first ones that take a holistic approach and consider individual, collective movement patterns and the similarity between trajectories in making prediction.

• We conduct extensive experiments using a real dataset and the results demonstrate the effectiveness of the proposed models.

摘要

Highlights•We propose three models (PMM, GMM and RMM) and combine them in different ways to obtain new models to predict the next location of a moving object.•GMM uses all available trajectories to discover collective patterns; PMM models the individual patterns of each moving object using its own past trajectories; RMM clusters the trajectories to mine the movement patterns.•Based on the observation that the movement patterns often change over time, we propose methods that can capture the relationships between the patterns in different time periods, and use this knowledge to build more refined models.•To the best of our knowledge, the proposed models are the first ones that take a holistic approach and consider individual, collective movement patterns and the similarity between trajectories in making prediction.•We conduct extensive experiments using a real dataset and the results demonstrate the effectiveness of the proposed models.