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• W2577894039 startingPage "1167" @default.
• W2577894039 abstract "We present a novel data-driven approach to populate virtual road networks with realistic traffic flows. Specifically, given a limited set of vehicle trajectories as the input samples, our approach first synthesizes a large set of vehicle trajectories. By taking the spatio-temporal information of traffic flows as a 2D texture, the generation of new traffic flows can be formulated as a texture synthesis process, which is solved by minimizing a newly developed traffic texture energy. The synthesized output captures the spatio-temporal dynamics of the input traffic flows, and the vehicle interactions in it strictly follow traffic rules. After that, we position the synthesized vehicle trajectory data to virtual road networks using a cage-based registration scheme, where a few traffic-specific constraints are enforced to maintain each vehicle's original spatial location and synchronize its motion in concert with its neighboring vehicles. Our approach is intuitive to control and scalable to the complexity of virtual road networks. We validated our approach through many experiments and paired comparison user studies." @default.
• W2577894039 created "2017-01-26" @default.
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• W2577894039 date "2018-02-01" @default.
• W2577894039 modified "2023-10-12" @default.
• W2577894039 title "Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis" @default.
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• W2577894039 doi "https://doi.org/10.1109/tvcg.2017.2648790" @default.
• W2577894039 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28092561" @default.
• W2577894039 hasPublicationYear "2018" @default.
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