Real-time reconstruction of fragmented trajectories: An integrated machine learning and behavior-based spatiotemporal framework

Abstract

High-quality road user trajectories are essential for various transportation applications. Despite the significant advancement of detection and tracking technologies, observed trajectories often suffer from several issues that impact their applicability, such as intrinsic errors, noise, and fragmentation. This paper introduces a real-time reconstruction framework for road user trajectories, designed to reconstruct coherent trajectories from potentially fragmented segments. The framework begins with processing the raw trajectories to extract several dynamic features such as velocity, acceleration, curvature, and heading. A Random Forest classifier is then utilized to identify trajectory segments likely belonging to the same path. The classifier incorporates the Subsequence Dynamic Time Warping (sDTW) metric and other spatiotemporal features. Next, similar segments are grouped into cohesive clusters where a trajectory reconstruction module merges the identified segments and interpolates missing segments using the Gaussian kernel-based regression. Finally, the reconstructed trajectories are smoothed using integrated wavelet transforms and Savitzky-Golay filters. The framework was trained and validated using trajectory data acquired from the Lyft Level 5 AV dataset. We focused on the reconstruction of pedestrian and cyclist trajectories due to their inherent complexity and unpredictability. Validation results confirmed the accuracy of the different system components as well as the accuracy of the reconstructed trajectories compared to ground truth data (RMSE of 0.1138 m and MAPE of 0.01%). Computational assessments indicate that the framework scales linearly with data size, with optimal performance for real-time applications achieved for 5- to 10-minute windows.