论文标题
使用时空交通图定义交通状态
Defining Traffic States using Spatio-temporal Traffic Graphs
论文作者
论文摘要
十字路口是交通拥堵的主要来源之一，因此，了解交叉路口的交通行为很重要。特别是在汽车密度高，交通混合类型和无车道驾驶行为的发展中国家，很难区分拥挤和正常的交通行为。在这项工作中，我们提出了一种使用交通图在交叉点上较小空间区域的交通状态的方法。这些交通图随着时间的流逝而发展的方式揭示了不同的交通状态 - a）拥塞正在形成（结块），拥塞正在分散（不倾斜），或c）流量正常流动（中性）。我们训练一个时空深网，以识别这些变化。另外，我们引入了一个名为Eyeonfaffic（EOT）的大数据集，其中包含在印度艾哈迈达巴德（Ahmmedabad）的3个繁忙十字路口收集的3个小时的空中视频。我们在EOT数据集上进行的实验表明，交通图可以帮助正确识别交叉路口不同空间区域中易于拥塞的行为。
Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior at intersections. Particularly, in developing countries with high vehicle density, mixed traffic type, and lane-less driving behavior, it is difficult to distinguish between congested and normal traffic behavior. In this work, we propose a way to understand the traffic state of smaller spatial regions at intersections using traffic graphs. The way these traffic graphs evolve over time reveals different traffic states - a) a congestion is forming (clumping), the congestion is dispersing (unclumping), or c) the traffic is flowing normally (neutral). We train a spatio-temporal deep network to identify these changes. Also, we introduce a large dataset called EyeonTraffic (EoT) containing 3 hours of aerial videos collected at 3 busy intersections in Ahmedabad, India. Our experiments on the EoT dataset show that the traffic graphs can help in correctly identifying congestion-prone behavior in different spatial regions of an intersection.
