Route-Aware Edge Bundling for Visualizing Origin-Destination Trails in Urban Traffic

Abstract

Origin-destination (OD) trails describe movements across space. Typical visualizations thereof use either straight lines or plot the actual trajectories. To reduce clutter inherent to visualizing large OD datasets, bundling methods can be used. Yet, bundling OD trails in urban traffic data remains challenging. Two specific reasons hereof are the constraints implied by the underlying road network and the difficulty of finding good bundling settings. To cope with these issues, we propose a new approach called Route Aware Edge Bundling (RAEB). To handle road constraints, we first generate a hierarchical model of the road-and-trajectory data. Next, we derive optimal bundling parameters, including kernel size and number of iterations, for a user-selected level of detail of this model, thereby allowing users to explicitly trade off simplification vs accuracy. We demonstrate the added value of RAEB compared to state-of-the-art trail bundling methods on both synthetic and real-world traffic data for tasks that include the preservation of road network topology and the support of multiscale exploration.