Artificial ground truth data generation for map matching with open source software

Map matching is a widely used technology for assigning tracks recorded by Global Navigation Satellite Systems (GNSS) to existing road networks. Due to the measurement uncertainty of GNSS positions, the biggest challenge is to map them accurately. To develop and verify suitable algorithms for map matching, ground truth, i.e., the traveled routes in the road network, is required as a reference. However, GNSS recorded tracks naturally lack the ground truth routes. Providing this data is time-consuming and costly in these cases, as it requires manual correction of the routes based on human memorization. This is not practical on a large scale, e.g., with floating car data (FCD). This is why there exist only a few isolated ground truth data sets that were created in this way for map matching. To close this gap, we introduce and evaluate in this work a new open source tool-chain for artificially generating large amounts of simulated ground truth routes for map matching. Based on these routes, we generate simulated FCD and we apply comparably authentic and parameterizable artificial GNSS noise with varying noise characteristics. The generated data allows to thoroughly evaluate and improve the performance of existing map matching algorithms and facilitates in future research the development of novel algorithms based on sufficiently large and diverse labeled data. In this work, we evaluate different scenarios of varying noise characteristics of our artificially generated ground truth data to compare the robustness, individual strengths, and weaknesses of existing open source map matching implementations. Our new approach of artificially generating ground truth data for map matching addresses the existing lack of sufficient available reference data for ongoing map matching research.