Probabilistic map-matching using particle filters
This addresses the need for accurate GPS data alignment with road networks for applications like traffic management, but it is incremental as it builds on existing sequential Monte Carlo methods.
The paper tackles the problem of improving GPS data accuracy for traffic management and location-based services by proposing a probabilistic map-matching approach using particle filters, which generates candidate solutions with probability scores and is validated on varied GPS data.
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.