Traj2Traj: A road network constrained spatiotemporal interpolation model for traffic trajectory restoration

In transportation, the trajectory data generated by various mobile vehicles equipped with GPS modules are essential for traffic information mining. However, collecting trajectory data is susceptible to various factors, resulting in the lack and even error of the data. Missing trajectory data could not correctly reflect the actual situation and also affect the subsequent research work related to the trajectory. Although increasing efforts are paid to restore missing trajectory data, it still faces many challenges: (1) the difficulty of data restoration because traffic trajectories are unstructured spatiotemporal data and show complex patterns; and (2) the difficulty of improving trajectory restoration efficiency because traditional trajectory interpolation is computationally arduous. To address these issues, a novel road network constrained spatiotemporal interpolation model, namely Traj2Traj, is proposed in this work to restore the missing traffic trajectory data. The model is constructed with a seq2seq network and integrates a potential factor module to extend environmental factors. Significantly, the model uses a spatiotemporal attention mechanism with the road network constraint to mine the latent information in time and space dimensions from massive trajectory data. The Traj2Traj model completes the road-level restoration according to the entire trajectory information. We present the first attempt to omit the map-matching task when the trajectory is restored to solve the time-consuming problem of map matching. Extensive experiments conducted on the provincial vehicle GPS data sets from April 2018 to June 2018 provided by the Fujian Provincial Department of Transportation show that the Traj2Traj model outperforms the state-of-the-art models.