Enhancing Vehicle Trajectory Quality: A Two-Step Data Reconstruction Method Using Wavelet Transform and Normal Acceleration Value

Abstract

Data reconstruction is essential in enhancing the quality of vehicle trajectory data. Previous studies have identified the location of abnormal data inaccurately, resulting in poor trajectory reconstruction results. This study proposed a two-step reconstruction method. The first step detected the locations of obviously abnormal speed data using wavelet transform. Then, the abnormal data were repaired by the cubic spline curve interpolation algorithm. The second stage identified the locations of abnormal acceleration data based on the general acceleration value. And the vehicle trajectory data were reconstructed using Lagrange interpolation and Kalman filter algorithms. The approach was utilized on NGSIM trajectory data. The results show that the acceleration values of the proposed method range from −6.69 m/s² to 4.96 m/s², with a standard deviation of 0.87. The reconstructed results are more closely matching drivers' physiological capabilities compared to other methods. These findings verify the reliability of the proposed approach and notably improve the quality of the trajectory data. It provides critical foundational data support for traffic planning, design, and management.