Heterogeneous Driver‐Aware Vehicle Trajectory Reconstruction and Fusion for Multiple Long‐Range Traffic Detectors

Traditional methods of vehicle trajectory reconstruction heavily rely on the data of cross‐sectional traffic detectors, but the effectiveness of existing methods is limited by insufficient information of the cross‐sectional data. In response to this, this study proposes a novel trajectory reconstruction method based on long‐range traffic detectors. It builds upon Newell's car‐following model and its derived inverse following model. By taking driver heterogeneity into account through individualised calibration of the key parameter, namely the spatial shift, which is optimised by the whale optimisation algorithm, the accuracy of trajectory reconstruction is enhanced. Furthermore, to connect trajectories of the same vehicle in the same area that are reconstructed by adjacent long‐range detectors, a particle filter‐based trajectory fusion method is developed. It can fuse overlapped reconstructed trajectories and smoothly connect sectional trajectories into a complete and seamlessly connected one. The performance of the trajectory reconstruction method is evaluated on the NGSIM I‐80 dataset, while the trajectory fusion method was tested on both the I‐80 and the TRJD TS datasets. Results show that the reconstruction method generates complete vehicle trajectories across various traffic flow conditions, achieving an average of 28.65% reduction in mean absolute error compared to methods that do not account for driver heterogeneity. The mean absolute error of the fused trajectories was reduced by 49.23% and 59.69% on average for two datasets, respectively, compared to reconstructed trajectories using a single detector. The trajectory reconstruction accuracy of the proposed method also outperforms that of a deep convolutional neural network and an improved adaptive smoothing method.