Progressive visual analysis of traffic data based on hierarchical topic refinement and detail analysis

Urban traffic data records people's individual movement and behavioral histories, which implies underlying human mobility patterns. How to effectively mine this pattern is the key to the building of a smart city. Traditional visual analysis methods only support exploration based on mobility patterns with fixed granularity, which limits the possibility of knowledge discovery at the beginning of analysis. However, the methods based on multi-granularity mobility patterns proposed in recent years have varying degrees of deficiencies in human perception or analysis efficiency. To balance the above problems, we design a comprehensive visual analysis system that supports both adaptive granularity refinement and complex semantic exploration of human mobility patterns. We introduce a hierarchical topic model H-NMF to extract multi-granularity traffic topics to capture mobility patterns with different levels of detail. Then, we design a progressive granularity refinement visualization method based on interactive Sankey diagram, which is more in line with the top-down cognitive process in human perception. The user can follow the visual cues provided by our system and gradually make decisions to obtain the desired granularity of traffic topics even for dataset without any prior knowledge. We further optimize the analysis efficiency of the refining process by taking the complete visual analysis process of traffic topics into consideration. The tensor decomposition model is used to extract the general spatio-temporal features of traffic topics to help users quickly obtain the basic semantics of traffic topics, which effectively reduces the analysis cost of users in the refinement. Besides, we design rich detail analysis visualizations to help users fully explore the semantics of traffic topics. All the above designs are well integrated in our visual analysis system which contains clear visualizations and user-friendly interactions. Finally, we verify the effectiveness of our method on real-world dataset with two case studies.