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.

摘要

运动轨迹数据的迅速增加导致数据存储，传输，计算处理和可视化问题。这些问题可以通过简化轨迹来缓解，这可以从原始轨迹中删除不必要的细节。现有的大多数专注于轨迹简化的研究都考虑了自由移动的物体，他们试图以二维平面的简化表示形式来最大程度地减少位置误差。但是，大量对象在道路网络的约束下移动。在这种受限的轨迹简化中，应该在网络空间中测量位置误差。此外，受约束的轨迹包含大量的速度变化信息，这些信息反映了运动对象的运动模式。在这项研究中，我们设计了一个数据模型，提出了误差测量方法，并开发了一种两成分方法来简化受约束的轨迹。我们方法中的几何成分扩展了经典的Douglas–Peucker方法，使用网络距离简化了轨迹，并保证了在网络空间中的位置误差。语义组件通过采用允许用户控制速度损失的数据丰富策略来增强了简化表示。真实的轨迹数据被用来评估该方法的有效性。实验结果表明，在简化路网约束轨迹数据的情况下，该方法比现有算法具有更低的位置误差。该方法还可以在简化表示中保持原始速度，而数据大小的增加相对较低。