Map matching and uncertainty: an algorithm and real-world experiments

A common problem in moving object databases (MOD) is the reconstruction of a trajectory from a trajectory sample (i.e., a finite sequence of time-space points). A typical solution to this problem is linear interpolation. A more realistic model is based on the notion of uncertainty modelled by space-time prisms, which capture the positions where the object could have been, when it moved from a to b. Often, object positions measured by location-aware devices are not on a road network. Thus, matching the user's position to a location on the digital map is required. This problem is called map matching. In this paper we study the relation between map matching and uncertainty, and propose an algorithm that combines weighted k-shortest paths with space-time prisms. We apply this algorithm to two real-world case studies and we show that accounting for uncertainty leads to obtaining more positive matchings.