Recent progress on location aware services, GPS and wireless technologies has made it possible to real-timely track moving object and collect a large quarlity of trajectories data. As a result, how to effectively discover the knowledge from these trajectory data becomes an attractive and interesting research topic. The new trajectory outlier detection, proposed in this paper, can be used to determine whether two trajectories are globally matched by calculating the local matching degree between every base comparing unit pairs. Firstly, this paper proposes a new distance measure approach, which treats k consecutive points as a local comparing unit to depict the local features in terms of trajectories, via calculating the matching degree between trajectory segments. In addition, the critical concepts as local match, global match and trajectory outlier are presented. Secondly, based on this distance measure method, a new trajectory outlier detection algorithm based on R-tree is proposed to improve the efficiency of outlier detection. The main idea behind this algorithm is to eliminate unnecessary distance computation by R-tree and distance characteristic matrix between every trajectory pair. Extensive experiments demonstrate the efficiency and effectiveness of the proposed algorithm for trajectory outlier detection.