  Increasing availability of vehicle GPS data has created potentially
transformative opportunities for traffic management, route planning and other
location-based services. Critical to the utility of the data is their accuracy.
Map-matching is the process of improving the accuracy by aligning GPS data with
the road network. In this paper, we propose a purely probabilistic approach to
map-matching based on a sequential Monte Carlo algorithm known as particle
filters. The approach performs map-matching by producing a range of candidate
solutions, each with an associated probability score. We outline implementation
details and thoroughly validate the technique on GPS data of varied quality.
