Space-Time Drift Point Detection in Mobility Patterns

Abstract

Location-aware information is now commonplace, as the ubiquity and pervasiveness of technology enabled its generation and storage at large scale. These data constitute a rich representation of entities’ whereabouts and behavior as they move on the map. Although several studies reported considerable predictability of such mobility patterns, several factors may impose significant changes on moving behavior. Being able to detect these changes can benefit several applications. In this article, we formalize and address the problem of detecting mobility drifts in mobility patterns. This problem is particularly challenging due to the noisy and incomplete nature of the data. We design non-parametric tests and present two algorithms to detect mobility drifts when the putative drift point is known in advance and there is no previous knowledge about the existence of potential changes, and we need to search for the most likely drift point rigorously. To evaluate our algorithms, we perform an extensive experimental study with real-world data coming from a variety of scenarios, such as geo-tagged social media data and GPS traces of connected vehicles. The results show the effectiveness of our algorithms, being able to identify existing drift points on spatial mobility patterns correctly.