With the technological development of global positioning and video surveillance systems and extensive utilization of social media platforms, large amounts of trajectories data are captured. Processing and analyzing of moving objects such as people, animals, and vehicles provides valuable information for industrial and academic studies. In this study, three different algorithms are investigated to find similarities of trajectory data. These algorithms are Euclidean distance based similarity measurement (ED), Dynamic-time Warping based similarity measurement (DTW) and Longest Common Subsequence based similarity measurement (LCSS). Tests are evaluated both on raw data and reduced data. Reduced data is obtained by using Douglas-Peucker algorithm. The tests for the evaluation of the similarity algorithms are performed on both a synthetic dataset and Geolife real trajectory dataset, with varying data sizes.