Modelling Place Visit Probability Sequences during Trajectory Data Gaps Based on Movement History

Modelling Place Visit Probability Sequences during Trajectory Data Gaps Based on Movement History

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Anti-Perspirant The acquisition of human trajectories facilitates movement data analytics and location-based services, but gaps in trajectories limit the extent in which many tracking datasets can be utilized.We present a model to estimate place visit probabilities at time points within a gap, based on empirical mobility patterns derived from past trajectories.Different from previous models, our model makes use of prior information from historical data to build a chain of empirically biased random walks.

Therefore, it is applicable to gaps of varied lengths and can be fitted to empirical data conveniently.In this model, long gaps are broken into a chain of multiple episodes according to past patterns, while short episodes are estimated with anisotropic location transition probabilities.Experiments show that our model is able to hit almost 60% of the ground truth for short gaps of several minutes and over 40% for longer gaps up to weeks.

In comparison, existing models are only able to hit less than 10% and 1% for short and long gaps, respectively.Visit probability distributions estimated by the model are useful for generating paths Camera / Camcorder Battery Charger in data gaps, and have potential for disaggregated movement data analysis in uncertain environments.