iLogBook: Enabling Text-Searchable Event Query Using Sparse Vehicle-Mounted GPS Data

Abstract

Querying an incident (i.e., an occurrence of seemingly minor importance) from coarse-grained driving log (i.e., GPS trace) has been a daunting task. For example, “Which restaurant did I drive by at exactly 4 pm yesterday?” The question seems very simple but is nontrivial, because the question is semantics-driven while the actual log data are GPS coordinate-based. Especially, the practical GPS log is very sparse and inaccurate for high-speed mobile objects such as vehicles. This paper seeks to answer any fuzzy query over sparse vehicles GPS data. Our system, called iLogBook, achieves these two goals by leveraging tensor technique and latent semantic analysis to high-precision trajectory recovery and similarity matching. We have implemented and evaluated the iLogBook with the GPS data of over 13, 798 taxicabs collected in eight days in Shenzhen, China. Our results show about 97% accuracy in trajectory inference. Moreover, the system handles about 90% daily queries among 10 marked drivers.