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.

VIStory: interactive storyboard for exploring visual information in scientific publications

“…The layout has many variations, such as Neighbor-Joining tree (Eler et al 2009), multi-dimensional scaling (Joia et al 2011), Voronoi treemap (Tan et al 2012), or picture collage (Liang et al 2018). Some image browsers make use of images' semantic information that can be generated from conventional image annotation (Yang et al 2006), emerging deep learning (Xie et al 2018), and mutual information (Zeng et al 2019). Besides similarities and semantics, images can also comprise multi-dimensional metadata such as place and categories, which can be used to facilitate searching and browsing (Corput and Wijk 2016).…”

“…2018 ), and mutual information (Zeng et al. 2019 ). Besides similarities and semantics, images can also comprise multi-dimensional metadata such as place and categories, which can be used to facilitate searching and browsing (Corput and Wijk 2016 ).…”

Using Bundling to Visualize Multivariate Urban Mobility Structure Patterns in the Sao Paulo Metropolitan Area

“…Zeng et al [19] adapted the KDEEB technique into Road Aware Edge Bundling (RAEB), which constraints the bundles along the road network on which the respective trails are recorded. RAEB was demonstrated on 166K taxi trajectories from New York City.…”

“…The literature is not clear on how to choose good bundling parameters [8]. This was observed and explicitly studied in Zeng et al [19], which also proposed ways to compute good parameter settings. However, as they also mention, these settings are…”

“…The high correlation between the paths and the rail lines also indicate good parameter settings for the bundled visualization in the metropolitan scale. Note that this type of correlation (of bundles with rails) is not the same as RAEB [19]. In RAEB, the bundling was explicitly made to follow roads.…”