Realistic Data-driven Traffic Flow Animation using Texture Synthesis


IEEE Transactions on Visualization and Computer Graphics, 2018, 24(2): 1167-1178.

Qianwen Chao, Zhigang Deng, Jiaping Ren, Qianqian Ye, Xiaogang Jin

Examples of traffic flows synthesized by our approach. (Left) The synthesized traffic flows on a curvy road. (Middle) The synthesized traffic flows on a traffic-light controlled road. (Right) The synthesized traffic flows on an urban highway network.


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.