Vehicle trajectory reconstruction for intersections: an integrated wavelet transform and Savitzky-Golay filter approach

Abstract

Reconstructing vehicle trajectories is an essential step in obtaining high-precision vehicle trajectory data owing to observation errors. Although considerable efforts have been made to reconstruct trajectories on road segments, it is still challenging to reconstruct two-dimensional vehicle trajectories at intersections. In this study, a three-step vehicle trajectory reconstruction method for intersections was proposed to correct abnormal velocities and direction angles in raw trajectories. First, outliers were identified by wavelet transform. They were then filtered out and interpolated using Gaussian kernel-based locally weighted linear regression. Finally, the trajectories were smoothed using a Savitzky-Golay filter. The proposed method was validated using empirical trajectory data collected at 15 intersections in Shanghai, China. The results demonstrate that the proposed trajectory reconstruction method performs well in terms of trajectory rationality and consistency. The mean outlier rate and root mean square error of the reconstructed trajectories are 3% and 0.821 m, respectively.

KEYWORDS:

• Two-dimensional trajectory reconstruction
• wavelet transform
• locally weighted regression
• Savitzky-Golay filter

Acknowledgements

Thank Dr. Jan-Dirk Schmoecker from Kyoto University for providing us with insightful suggestions during the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grants 52122215, 71971140 and 52202401; the Natural Science Foundation of Shanghai under Grant 20ZR1439300; and China Postdoctoral Science Foundation under Grant 2022M712139.