Similarity search with respect to time series has received much attention from research and industry in the last decade. Dynamic time warping is one of the most widely used distance measures in this context. This is due to the simplicity of its definition and the surprising quality of dynamic time warping for time series classification. However, dynamic time warping is not well-behaving with respect to many dimensionality reduction techniques as it does not fulfill the triangle inequality. Additionally, most research on dynamic time warping has been performed with one-dimensional time series or in multivariate cases of varying dimensions. With this paper, we propose three extensions to LBRotation for two-dimensional time series (trajectories). We simplify LBRotation and adapt it to the online and data streaming case and show how to tune the pruning ratio in similarity search by using presorting strategies based on simple summaries of trajectories. Finally, we provide a thorough evaluation of these aspects on a large variety of datasets of spatial trajectories.