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We investigate techniques for similarity analysis of spatio-temporal trajectories for mobile objects. Such data may contain a large number of outliers, which degrade the performance of Euclidean and time warping distance. Therefore, we propose the use of non-metric distance functions based on the longest common subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various Lp norms and also to time warping distance (for real and synthetic data) and present experimental results that validate the accuracy and efficiency of our approach, especially in the presence of noise.