ABSTRACT Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.

中文翻译：

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基于众包跟踪数据的行人网络生成

摘要 行人网络在各种应用中发挥着重要作用，例如行人导航服务和移动建模。本文提出了一种基于两层框架从众包跟踪数据中提取行人网络的新方法。该框架包括步行模式分类层和行人网络生成层。在第一层，我们提出了一种多尺度分形维数 (MFD) 算法，以识别两种不同类型的步行模式：有明确目的地的步行 (WCD) 或没有明确目的地的步行 (WOCD)。在第二层，我们通过结合行人区域和行人路径来生成行人网络。基于改进的连通分量分析 (CCA) 算法从 WOCD 轨迹中提取行人区域。我们使用基于内核密度估计 (KDE) 的点聚类算法从 WCD 轨迹生成行人路径。使用两个实际众包数据集的行人网络生成结果表明，该方法在几何正确性和拓扑正确性方面均具有良好的性能。