We present PATHFINDERVIS, a tool which is able to efficiently retrieve and visualize massive trajectory data on up to continent-sized networks. PATHFINDERVIS is an extension of the existing PATHFINDER framework which was designed as a pure backend and lacked a visualization. PATHFINDERVIS features many prototypes of different approaches to efficiently transmit and visualize large trajectory data.

In this paper, we consider the problem of gradual road network simplification, where given an embedded road network the goal is to compute a fine-grained succession of simplifications with decreasing level-of-detail. This allows to render the network on any desired zoom level or with a user-defined number of segments. Previous work has established that this can be achieved based on a Contraction Hierarchies (CH) data structure. CH was originally developed as a graph preprocessing method to speed up shortest path planning in road networks. However, since it is inherently based on a graph simplification mechanism, it can also serve as a basis for rendering. But the existing method exhibits several shortcomings, for example, topological inconsistencies arise on many simplification levels and the preservation of the shape of routes and the overall network for coarser graph representations is insufficient. This severely impairs the navigability of the map. We significantly improve upon the existing method by modifying the CH construction process as well as the rendering algorithm.

We present OSM Ski Resort Routing, an app that combines the concept of pathfinding and navigation with skiing. It provides an interactive 2D and 3D visualisation of arbitrary ski resorts using OpenStreetMap data and can be used to compute and display the optimal route between any waypoints in the resort.

We consider the problem of indexing massive trajectory data in an underlying road network. Our Pathfinder index structure is based on a state-of-the-art speed-up technique for shortest path planning and allows to both compress and access huge amounts of trajectory data. In a continent-sized network with more than 400 million nodes and almost a billion edges, Pathfinder allows to retrieve all trajectories within a given space-time cube in a few microseconds per reported trajectory. The applicability of Pathfinder is shown using both synthetic and real-world trajectory sets.