IEEE Trans Vis Comput Graph. 2018 Feb;24(2):1167-1178. doi: 10.1109/TVCG.2017.2648790. Epub 2017 Jan 11.
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.
我们提出了一种新颖的数据驱动方法，用于为虚拟道路网络填充逼真的交通流。具体来说，给定有限数量的车辆轨迹作为输入样本，我们的方法首先合成大量的车辆轨迹。通过将交通流的时空信息作为二维纹理，新交通流的生成可以被公式化为纹理合成过程，通过最小化新开发的交通纹理能量来解决。合成的输出捕获输入交通流的时空动态，并且其中的车辆交互严格遵循交通规则。之后，我们使用基于笼的注册方案将合成的车辆轨迹数据定位到虚拟道路网络中，其中强制执行一些特定于交通的约束，以保持每个车辆的原始空间位置并与其相邻车辆协同同步其运动。我们的方法易于控制且可扩展到虚拟道路网络的复杂性。我们通过许多实验和配对比较用户研究验证了我们的方法。
