A spatiotemporal generative adversarial network with a calibrated prior for urban vehicle trajectory imputation and denoising

High-resolution vehicle trajectory data collected by drone-based traffic sensing systems support detailed microscopic traffic analysis, but they are often affected by missing values, measurement noise, and tracking errors. This paper proposes TPC-STGAN for urban vehicle trajectory imputation and denoising. The framework begins with trajectory prior calibration (TPC), which constructs a feature-wise Calibrated Prior from the corrupted trajectory and the observed-value mask. This prior serves as a structured residual anchor for a lightweight spatiotemporal generator based on dense projection, multi-head self-attention, bidirectional GRU sequence modeling, and a time-step sigmoid correction gate. The generator learns gated residual corrections around the prior, while a CNN-based discriminator encourages reconstructed trajectories to remain close to the distribution of clean trajectories. A feature-weighted mean squared error (FWMSE) reconstruction loss is used to reflect the different roles of position, speed, acceleration, and time variables. Experiments on the pNEUMA drone trajectory dataset are conducted under Gaussian+Random, Laplace+Random, and Drift+Block corruption protocols. In the main Gaussian+Random setting, TPC-STGAN achieves an RMSE of 0.0696 and an MAE of 0.0512, reducing corrupted-input RMSE by 86.47%. Under Drift+Block corruption, MovingAvg+Interp obtains the lowest overall RMSE, while TPC-STGAN ranks second overall and achieves the lowest RMSE among neural models. Ablation, sensitivity, robustness, downstream prediction, and deployment analyses further characterize component contributions, behavior across corruption types, and deployment cost.