By the rise of mobile devices, trajectory data could be easily collected and used in several applications, like destination prediction, public transportation optimization, and travel route recommendation. However, due to the spatio-temporal nature, raw trajectory data usually contain redundant movement information. This observation motivates the trajectory simplication approaches which discard some points with preserving some specific features, such as position features, direction features, and so on. Most of existing simplifications ignore the importance of velocity features. This paper proposes an adaptive trajectory approaches while taking the velocity feature into account. Specifically, the Adaptive Trajectory Simplification (ATS) algorithm is proposed, which not only preserves the position feature, but the velocity feature from the given trajectories. ATS algorithm groups the velocity values into several intervals, which are used to partition trajectories into velocity-preserving segments. The simplified trajectory could be derived by applying the position-preserving simplification approach on each segment, where the threshold in a position-preserving approach could be determined without manual setting. Extensive experiments are conducted by using a real trajectory dataset in Porto. The experimental results show ATS algorithm could simplify trajectories effectively while preserving the velocity feature and the position feature at the same time.