ABSTRACT

Map construction algorithms attempt to derive a spatial graph representing a road network from GPS-sampled movement trajectories. Existing methods commonly use trajectories without considering the specific sampling methodology. Hence, the movement information is not preserved in the map construction results. The proposed map-construction method considers the particularities of the sampling process and how they affect the trajectory data to improve the overall result quality. Specifically, our proposed algorithm constructs nodes by clustering turn points. We use an adaptive clustering approach that considers when a turn point was sampled in relation to the ‘true’ node location based on the trajectory geometry. As nodes are the aggregates of turn points, edges are constructed by conflating trajectories that either connect turn points or are in close proximity to inferred nodes. Experiments using trajectory datasets at different spatial scales, data complexities, and data sources in combination with several assessment methods show that the proposed movement-aware map construction method produces maps of greater accuracy than those from the existing approaches.

摘要

地图构建算法试图从GPS采样的运动轨迹中得出代表道路网络的空间图。现有方法通常使用轨迹而不考虑特定的采样方法。因此，运动信息没有保留在地图构造结果中。提出的地图构造方法考虑了采样过程的特殊性以及它们如何影响轨迹数据以提高整体结果质量。具体来说，我们提出的算法通过对转折点进行聚类来构造节点。我们使用一种自适应聚类方法，该方法基于轨迹几何形状考虑何时相对于“真实”节点位置采样了一个转折点。由于节点是转折点的集合，通过融合连接转折点或紧邻推断节点的轨迹来构造边。使用不同空间尺度，数据复杂性和数据源的轨迹数据集并结合多种评估方法进行的实验表明，与现有方法相比，所提出的运动感知地图构建方法可生成精度更高的地图。