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In a complex urban environment, due to the unavoidable interruption of GNSSpositioning signals and the accumulation of errors during vehicle driving, thecollected vehicle trajectory data is likely to be inaccurate and incomplete. Aweighted trajectory reconstruction algorithm based on a bidirectional RNN deepnetwork is proposed. GNSS/OBD trajectory acquisition equipment is used tocollect vehicle trajectory information, and multi-source data fusion is used torealize bidirectional weighted trajectory reconstruction. At the same time, theneural arithmetic logic unit (NALU) is introduced into the trajectoryreconstruction model to strengthen the extrapolation ability of the deepnetwork and ensure the accuracy of trajectory prediction, which can improve therobustness of the algorithm in trajectory reconstruction when dealing withcomplex urban road sections. The actual urban road section was selected fortesting experiments, and a comparative analysis was carried out with existingmethods. Through root mean square error (RMSE, root-mean-square error) andusing Google Earth to visualize the reconstructed trajectory, the experimentalresults demonstrate the effectiveness and reliability of the proposedalgorithm.

A fast and Accurate Sketch Method for Estimating User Similarities over Trajectory Data