Trajectory data is a very common kind of spatio-temporal data. One typical example of trajectory data is the city traffic trajectory data, which records the tracks of moving objects, e.g. pedestrians, bicycles and various other vehicles. Effective visualization and analysis of such data may help us better understand the urban transportation system, find out strategies to reduce the number of accidents and traffic jams, thus ensuring people's safety, facilitating people's trip and elevating the economic efficiency. In a series of projects, we studied several aspects with trajectory data, including: traffic density, traffic jams and traffic pattern correlation.
We develop an interactive visual analytics system to help users explore the trajectories derived from geo-tagged social media. An uncertainty model integrated with the system is proposed to help users find reliable patterns from such sparse trajectories.
We develop an interactive visual analytics system to help users explore individual trajectories derived from geo-tagged social media. Users can explore their own and friends' footprint with multi-level spatial temporal visualization. Many interesting individual patterns could be derived with such visualilzation for the mass.
We develop a bus visualization interface, Bus Vis, to explore urban bus data. Bus Vis allows users to explore the urban bus information, including traffic animation and passenger counts with time variation.
We develop a visual method, OD-Wheel, to explore OD patterns of a central region. Given a central region, OD-Wheel allows users to explore the dynamic patterns of OD clusters, including the variation of traffic flow volume and travel time.
We develop a visual method, TrajRank, to study the travel behaviour of vehicles along one route. We focus on the spatial-temporal distribution of travel time, that is, the time spent on each road segment, and the travel time variation in rush/non-rush hours.
We show how to explore sparse traffic trajectory generated by urban transporation cells. We focus on macro-traffic analysis, including the traffic patterns on cells and links. We further study the correlation between those patterns.
We show how 2D trajectories can be converted to timelines for easy perception of temporal information, and easy aligned comparison. Our timelines are not limited to time varying attributes of trajectories, but also spatial temporal features such as stop, turn, etc. We provide some use cases to show the benefit of our method.
We show the variation of traffic conditions for each road, and the propagation of traffic jams along the road network. We used the taxi GPS trajectories of Beijing, then automatically extracted the traffic jam information. We allow user to further explore the traffic jams through multilevel exploration and multi-faceted filtering.
Traffic density rendering is important, because it gives an intuitive overview of massive trajectories. It is also related to the study of traffic jams, hot spots and people's behaviors. We use density map algorithm to generate pictures of traffic density in Beijing, with real taxi GPS data.
We built a system, TripVista, to explore the micro behaviors of trajectories in three linked perspectives: space, time and attribute. We applied the system to traffic trajectory data at a road intersection, and a global hurricane trajectory dataset. We are able to give an overview and discover some outliers.