Density Adaptive Approach for Generating Road Network from GPS Trajectories

Abstract

Road networks are fundamental parts of intelligent transportation and smart cities. With the emergence of crowdsourcing geographic data, road mapping approaches by using crowdsourcing trajectories have been developed. Existing road map inference algorithms from trajectories can extract relatively accurate road networks, however, these algorithms are not robust to different trajectory datasets and the parameter optimization task is tedious and time-consuming. Therefore, we propose an adaptive approach based on trajectory density. The proposed approach contains two stages. Firstly, the density distribution for each trajectory is adaptively estimated by the Gaussian fitting approach and the density peak points are extracted to construct road centerlines corresponding to each trajectory. Secondly, these extracted road centerlines are incrementally merged by the 'matching-refinement-merging' process to generate a road network. We compare the proposed approach against four representative methods through trajectory datasets that are completely different in sampling frequency, trajectory density, road density, and noise. The results show that the proposed approach provides better accuracy in terms of precision and integrity and does not require additional parameter adjustment.