Probabilistic map-matching using particle filters

Over the last years we have witnessed a rapid increase in the availability of GPSreceiving devices, such as smart phones or car navigation systems. The devices generate vast amounts of temporal positioning data that have been proven invaluable in various applications, from traffic management (Kühne et al., 2003) and route planning (Gonzalez et al., 2007; Li et al., 2011; Kowalska et al., 2015) to inferring personal movement signatures (Liao et al., 2006). Critical to the utility of GPS data is their accuracy. The data suffer from measurement errors caused by technical limitations of GPS receivers and sampling errors caused by their receiving rates. When digital maps are available, it is common practice to improve the accuracy of the data by aligning GPS points with the road network. The process is known as map-matching. Most map-matching algorithms align GPS trajectories with the road network by considering positions of each GPS point, either in isolation or in relation to other GPS points in the same trajectory.