Inferring road maps from sparsely sampled GPS traces

IF 1.2 Q4 TELECOMMUNICATIONS
Jia Qiu, Ruisheng Wang
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引用次数: 6

Abstract

Abstract We propose a novel segmentation-and-grouping framework for road map inference from sparsely sampled GPS traces. First, we extend Density-Based Spatial Clustering of Application with Noise with an orientation constraint to partition the entire point set of the traces into point clusters representing the road segments. Second, we propose an adaptive k-means algorithm that the k value is determined by an angle threshold to reconstruct nearly straight line segments. Third, the line segments are grouped according to the ‘Good Continuity’ principle of Gestalt Law to form a ‘Stroke’ for recovering the road map. Experimental results demonstrate that our algorithm is robust to noises and sampling rates. In comparison with previous work, our method has advantages to infer road maps from sparsely sampled GPS traces.
从稀疏采样的GPS轨迹推断道路地图
摘要提出了一种基于稀疏采样GPS迹线的道路地图推断的分割分组框架。首先,我们扩展了基于密度的带噪声空间聚类应用,并引入方向约束,将轨迹的整个点集划分为代表道路段的点簇。其次,我们提出了一种由角度阈值确定k值的自适应k-means算法来重建近直线段。第三,根据格式塔法的“良好连续性”原则对线段进行分组,形成恢复路线图的“笔画”。实验结果表明,该算法对噪声和采样率具有良好的鲁棒性。与以往的工作相比,我们的方法具有从稀疏采样的GPS轨迹推断道路地图的优点。
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来源期刊
CiteScore
3.70
自引率
8.70%
发文量
12
期刊介绍: The aim of this interdisciplinary and international journal is to provide a forum for the exchange of original ideas, techniques, designs and experiences in the rapidly growing field of location based services on networked mobile devices. It is intended to interest those who design, implement and deliver location based services in a wide range of contexts. Published research will span the field from location based computing and next-generation interfaces through telecom location architectures to business models and the social implications of this technology. The diversity of content echoes the extended nature of the chain of players required to make location based services a reality.
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