Addressing robust travel mode identification with individual trip‐chain trajectory noise reduction

Abstract

Identifying travel modes from Global Navigation Satellite System (GNSS) trajectories is helpful for traffic management. In mode identification, the motion features are extracted from trajectories to train the classifiers. However, features would be distorted by the positioning noise when migrating existing frameworks to poor-quality tracks. This study aims to answer how to eliminate the impact of positioning error on mode identification. Specifically, six widely used Trajectory Noise Reduction (TNR) methods were tested. Representative motion features were calculated and sent to several classical classifiers to evaluate the effect of TNR. Then, the extent to which TNR restores motion features is analysed by information gain. To verify the robustness of these methods, multiple noise scenarios are designed to simulate possible positioning noise. The results show that the trajectory smoothing methods perform better than the outlier elimination methods regardless of the type and magnitude of noise. In particular, the Gaussian kernel smoothing can achieve the highest effect in almost all noise scenarios. For untested TNR methods that require a time window radius parameter, a 30-s time window is a good candidate. Moreover, the visualisation verification cannot ensure the best TNR method for travel mode identification.