A pyramid-based approach to visual exploration of a large volume of vehicle trajectory data

Advances in positioning and wireless communicating technologies make it possible to collect large volumes of trajectory data of moving vehicles in a fast and convenient fashion. These data can be applied to traffic studies. Behind this application, a methodological issue that still requires particular attention is the way these data should be spatially visualized. Trajectory data physically consists of a large number of positioning points. With the dramatic increase of data volume, it becomes a challenge to display and explore these data. Existing commercial software often employs vector-based indexing structures to facilitate the display of a large volume of points, but their performance downgrades quickly when the number of points is very large, for example, tens of millions. In this paper, a pyramid-based approach is proposed. A pyramid method initially is invented to facilitate the display of raster images through the tradeoff between storage space and display time. A pyramid is a set of images at different levels with different resolutions. In this paper, we convert vector-based point data into raster data, and build a gridbased indexing structure in a 2D plane. Then, an image pyramid is built. Moreover, at the same level of a pyramid, image is segmented into mosaics with respect to the requirements of data storage and management. Algorithms or procedures on grid-based indexing structure, image pyramid, image segmentation, and visualization operations are given in this paper. A case study with taxi trajectory data in Shanghai is conducted. Results demonstrate that the proposed method outperforms the existing commercial software.