  In a complex urban environment, due to the unavoidable interruption of GNSS
positioning signals and the accumulation of errors during vehicle driving, the
collected vehicle trajectory data is likely to be inaccurate and incomplete. A
weighted trajectory reconstruction algorithm based on a bidirectional RNN deep
network is proposed. GNSS/OBD trajectory acquisition equipment is used to
collect vehicle trajectory information, and multi-source data fusion is used to
realize bidirectional weighted trajectory reconstruction. At the same time, the
neural arithmetic logic unit (NALU) is introduced into the trajectory
reconstruction model to strengthen the extrapolation ability of the deep
network and ensure the accuracy of trajectory prediction, which can improve the
robustness of the algorithm in trajectory reconstruction when dealing with
complex urban road sections. The actual urban road section was selected for
testing experiments, and a comparative analysis was carried out with existing
methods. Through root mean square error (RMSE, root-mean-square error) and
using Google Earth to visualize the reconstructed trajectory, the experimental
results demonstrate the effectiveness and reliability of the proposed
algorithm.
