论文标题
高架图像的动态交通建模
Dynamic Traffic Modeling From Overhead Imagery
论文作者
论文摘要
我们的目标是使用架空图像来了解交通流的模式，例如回答问题，例如您在周日凌晨3点可以越过时代广场的速度。解决此问题的传统方法是将每个路段的速度建模为时间的函数。但是，此策略是有限的，因为必须先收集大量数据，然后才能使用模型，并且无法推广到新领域。取而代之的是，我们提出了一种使用卷积神经网络生成交通速度动态图的自动方法。我们的方法在高架图像上运行，以位置和时间为条件，并输出一个本地运动模型，该模型捕获可能的旅行方向和相应的旅行速度。为了培训我们的模型，我们利用了从纽约市收集的历史交通数据。实验结果表明，我们的方法可以应用于生成准确的城市规模交通模型。
Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount of data must first be collected before a model can be used and it fails to generalize to new areas. Instead, we propose an automatic approach for generating dynamic maps of traffic speeds using convolutional neural networks. Our method operates on overhead imagery, is conditioned on location and time, and outputs a local motion model that captures likely directions of travel and corresponding travel speeds. To train our model, we take advantage of historical traffic data collected from New York City. Experimental results demonstrate that our method can be applied to generate accurate city-scale traffic models.
