LAUSR

ABSTRACT The rapid increase in movement trajectory data causes data storage, transmission, computational processing, and visualization problems. These issues can be alleviated through trajectory simplification, which removes unnecessary details from raw trajectories. Most existing studies that focused on trajectory simplification considered freely moving objects, and they attempted to minimize position errors in their simplified representations in a two-dimensional plane. However, a large number of objects move within the constraint of road networks. In such constrained trajectory simplification, position error should be measured in the network space. Moreover, constrained trajectories contain a wealth of speed-change information that reflects the movement patterns of moving objects. In this study, we designed a data model, proposed error measurements, and developed a two-component method to simplify constrained trajectories. The geometric component in our method extended the classic Douglas–Peucker method using network distance to simplify trajectories with a guaranteed position error bound in network space. The semantic component enhanced the simplified representation by employing a data-enrichment strategy that allows users to control speed loss. Real trajectory data were used to assess the effectiveness of the proposed method. Experimental results show that our method has a lower position error than existing algorithms do when road network constrained trajectory data are simplified. The method can also preserve original speed in the simplified representation with a relatively low increase in data size. Our study thus provides an approach to simplifying trajectory data that guarantees error bounds in both location and speed.