Visual analysis of traffic data via spatio-temporal graphs and interactive topic modeling

Analyzing of traffic data is an important task for urban planners and managers. Traffic data have the spatio-temporal characteristics, which can reflect the variation of the presence of vehicle in different places over time as well as traffic flow dynamics among different places. The analysis of large-scale GPS trajectory data is a very challenging research due to the complexity of data and the need to extract useful information under cover in data. In this study, we combine temporal and geospatial aggregation of traffic data for obtaining key areas and creating legible traffic flow maps; meanwhile, we make full use of the topic model to capture latent semantic information. Nevertheless, most of the topic models always encounter the plague of choosing the optimal number of topics and cannot easily incorporate numerous types of user feedback. To tackle these problems, we propose an interactive topic modeling equipped with various interactive capabilities which empowers users to explore data from different levels of detail. Finally, we design and implement an interactive visual analytics prototype system based on the spatio-temporal graphs and the interactive topic modeling. The feasibility and validity of our system is demonstrated by conducting two case studies with a real-world traffic data in Hangzhou. Graphical Abstract