Abstract
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 in data gaps, and have potential for disaggregated movement data analysis in uncertain environments.
Highlights
Information, communication, and location-based technology have boosted the collection of human movement data at the individual level [1,2,3,4], supporting applications in intelligent location-based service and mobility pattern modelling [5,6,7,8,9]
We proposed a probabilistic space-time model based on historical trajectories (PSM-H) to estimate visit probability distributions during gaps
We visualized visit probability distributions for two representative gaps to seek a general sense of the model performance on long gaps
Summary
Information, communication, and location-based technology have boosted the collection of human movement data at the individual level [1,2,3,4], supporting applications in intelligent location-based service and mobility pattern modelling [5,6,7,8,9]. These data sample the location of moving objects at a finite set of moments as a series of timestamped locations, i.e., trajectories [10]. These studies do not model habits and preferences of movement
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.