“Flow reconstruction for data-driven traffic animation” by Wilkie, Sewall and Lin

  • ©David Wilkie, Jason Sewall, and Ming C. Lin




    Flow reconstruction for data-driven traffic animation

Session/Category Title: Data-Driven Animation




    ‘Virtualized traffic’ reconstructs and displays continuous traffic flows from discrete spatio-temporal traffic sensor data or procedurally generated control input to enhance a sense of immersion in a dynamic virtual environment. In this paper, we introduce a fast technique to reconstruct traffic flows from in-road sensor measurements or procedurally generated data for interactive 3D visual applications. Our algorithm estimates the full state of the traffic flow from sparse sensor measurements (or procedural input) using a statistical inference method and a continuum traffic model. This estimated state then drives an agent-based traffic simulator to produce a 3D animation of vehicle traffic that statistically matches the original traffic conditions. Unlike existing traffic simulation and animation techniques, our method produces a full 3D rendering of individual vehicles as part of continuous traffic flows given discrete spatio-temporal sensor measurements. Instead of using a color map to indicate traffic conditions, users could visualize and fly over the reconstructed traffic in real time over a large digital cityscape.


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