Pedestrian network generation based on crowdsourced tracking data期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

按检索

Pedestrian network generation based on crowdsourced tracking data

Authors:	Xue Yang Chang Ren Yang Chen Zhong Xie Qingquan Li

Institution:	1. School of Geography and Information Engineering, China University of Geosciences , Wuhan, China;2. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University , Wuhan, China https://orcid.org/0000-0003-1760-0865;3. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University , Wuhan, China;4. College of Civil Engineering, Shenzhen University , Shenzhen, China

Abstract:	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.

Keywords:	Crowdsourced tracking data walking pattern pedestrian network navigation services

设为首页 | 免责声明 | 关于勤云 | 加入收藏