基于无人机测量的微观拥堵交通数据异质性研究

Yildirim Dülgar, M. Menth, H. Rehborn, Micha Koller
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引用次数: 0

摘要

研究了交通拥堵发生和存在时的车辆轨迹,揭示了交通拥堵在独立车道上的微观特征。在德国高速公路的三车道路段上,可以使用无人机观察移动车辆的微观数据。基于这些详细的经验交通数据,我们揭示了拥堵交通的异质性和复杂性,并讨论了其后果。例如,为了让驾驶员辅助系统或自动驾驶车辆实现安全舒适的驾驶行为,应该适应车道水平的交通状态。如果只有三车道高速公路的左侧车道出现拥堵和密集的交通状态,可能会造成严重的危险。此外,我们提出了一种经验方法来计算局部交通密度,可以用来提前警告车辆对前面的高密度。我们利用这个概念来研究当地的交通堵塞,并讨论其车道级属性。我们揭示了在不同的高速公路车道上高密度局部结构的异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heterogeneity of Microscopic Congested Traffic Data Based on Drone Measurements
We study vehicle trajectories at the onset and existence of traffic congestion and reveal its microscopic features on separate highway lanes. Drone observations of microscopic data of moving vehicles have been made available on three-lane road segments of German highways. Based on these detailed empirical traffic data we reveal heterogeneity and complexity of congested traffic and discuss its consequences. E.g., to perform a safe and comfortable driving behavior by driver assistance systems or automated vehicles lane-level traffic states should be adapted. A congested and dense traffic state only on the left lane of a three-lane highway could be a serious danger. Moreover, we propose an empirical method to calculate local traffic densities that could be used to warn vehicles in advance about high preceding densities. We leverage that concept to study a local traffic jam and discuss its lane-level properties. We reveal the heterogeneity of high local density structures on separate highway lanes.
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