Abstract:  The rapid development of positioning technology has given rise to trajectory big data, and there are always obviously aberrant outliers in trajectories. Detecting outliers in trajectory data is crucial for improving data quality and the accuracy of subsequent knowledge discovery. The existing trajectory outlier detection algorithm is mainly the constant speed threshold approach, the method does not consider the change of the motion state at different moments of the object, can only detect a part of outliers whose velocity exceeds the specified threshold, and even leads to the detection error, the robustness of the algorithm is poor. Aiming at above problems, this paper proposes a Trajectory Outlier Detection Algorithm based on adaptive Threshold (TODAT). Taking full account of the motion information and observation noise impact of the object in a period of time, the TODAT algorithm applies the local threshold window and the mean filtering window to calculate the threshold and velocity, and also adds the economic speed threshold and the continuous outliers re-judgment mechanism. The experimental results based on real vessel data show that the proposed algorithm can get the adaptive threshold according to trajectory data, effectively detect all the outliers and greatly improve the quality of the trajectory data.

Key words: trajectory data, outlier detection, adaptive threshold, local threshold, mean filter