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Visual Exploration of Sparse Traffic Trajectory Data

机译:稀疏交通轨迹数据的可视化探索

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In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.
机译:在本文中,我们提出了一种视觉分析系统,以探索由运输单元记录的稀疏交通轨迹数据。此类数据包含城市主要道路上几乎所有行驶中的车辆的行驶情况。因此,它非常适合进行宏流量分析。但是,仅在车辆运动通过单元时才记录它们。两个连续单元之间的确切轨迹是未知的。为了解决这种不确定性,我们首先设计一个局部动画,仅在单元附近显示车辆的运动。此外,我们忽略了单个车辆的微观行为,而是关注宏观交通模式。我们将现有的轨迹聚合技术应用于数据集,研究细胞状态模式和细胞间流动模式。除此之外,我们建议使用动态图可视化技术研究这两种模式之间的相关性。它使我们能够检查一个小区上的流量拥塞如何与相邻链路上的流量以及附近的路由选择相关联。案例研究表明了我们系统的有效性。

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