This paper presents a new approach for processing vehicle trajectories collected from the field. Unlike traditional approaches such as Finite Differencing or Locally Weighed Regression, the proposed approach combines bi-level optimization with spline interpolations, seeking to minimize not only measurement errors, but also internal inconsistency errors in positions, speeds and accelerations data. Real-life vehicle trajectories collected from I-94 WB were used to test the proposed approach. Results indicate the new approach is effective in eliminating both measurement and inconsistency errors. Moreover, the proposed approach is further compared to Locally Weighted Regression, an approach that has been commonly used in earlier studies, by conducting a sensitivity analysis where the magnitude of measurement errors is varied with different values. The comparison results show that the proposed approach is not only more robust with respect to varying measurement errors, but also more effective in removing data inconsistency from vehicle speed and acceleration profiles.