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 grid-based 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.
In recent years, the increasing development of traffic information collection technology based on floating car data has been recognized, which gives rise to the establishment of real-time traffic information dissemination system in many cities. However, the recent massive construction of urban elevated roads hinders the processing of corresponding GPS data and further extraction of traffic information (e.g., identifying the real travel path), as a result of the frequent transfer of vehicles between ground and elevated road travel. Consequently, an algorithm for identifying the travel road type (i.e., elevated or ground road) of vehicles is designed based on the vehicle traveling features, geometric and topological characteristics of the elevated road network, and a trajectory model proposed in the present study. To be specific, the proposed algorithm can detect the places where a vehicle enters, leaves or crosses under elevated roads. An experiment of 10 sample taxis in Shanghai, China was conducted, and the comparison of our results and results that are obtained from visual interpretation validates the proposed algorithm.
A three dimensional numerical model based on the hydrodynamic module of finite-volume coastal ocean (FVCOM) was established for the Yellow River estuary. The model has been calibrated by observed data and proved to be suitable to reflect the hydrodynamic force in the research area. We employed the model to simulate the tidal shear front off the Yellow River estuary and analyzed the formation, spread and duration of two different types of shear front. To examine the effect of bathymetry evolution on the position of tidal shear front, subaqueous bathymetry of the Yellow River estuary was changed according to the changing patterns obtained from the past few years. Tidal shear front was modeled on both the original and the changed bathymetry. The results show that the position of shear front moved from a shallow to a deep area due to the deposition of bathymetry. The influence of bathymetry evolution on hydrodynamic characteristics including the distribution of salinity and the movement of particles was studied. We found the dispersion areas of low salinity became larger after changing bathymetry and the particles on the surface, middle and bottom layer are able to move further both north and west of Laizhou Bay on the changed bathymetry.