Pedestrian network generation based on crowdsourced tracking data

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

Acknowledgments

The authors would like to sincerely thank the anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of this article. This work was founded by the National Natural Science Foundation of China (Nos. 41901394, 41971405) and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.162301182737).

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data and codes that support the findings of this study are available in [figshare.com] with the identifier(s) at the link (https://figshare.com/articles/codes_and_data/9975710). The GeoLife data were derived from the following resources available in the public domain: [https://www.microsoft.com/en-us/download/details.aspx?id=52367].

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41901394,41971405]; Open research fund program of LIESMARS [Grant No. 19S01]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [162301182737].

Notes on contributors

Xue Yang

Xue Yang received the Ph.D. degree in State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing from Wuhan University, Wuhan, China, in 2018. She is currently an Associate Professor in China University of Geosciences, Wuhan, China. Her research interests include spatiotemporal trajectories mining, change detection, and human behavior analysis.

Luliang Tang

Luliang Tang received the Ph.D. degree in State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing from Wuhan University, Wuhan, China, in 2007. He is currently a Professor with Wuhan University, Wuhan, China. His research interests include space time GIS, spatiotemporal data analysis, 3-D and dynamic data modeling in GIS.

Chang Ren

Chang Ren is a Ph.D. student in State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing at Wuhan University. He received a bachelor's degree at Wuhan University in 2016. His research focuses on road information extraction and human mobility analysis based on trajectory data.

Yang Chen

Yang Chen received the M.Eng. degree from Liaoning Technical University, Fuxin, China, in 2019. He is currently pursuing the Ph.D. degree with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research interests include deep learning and image processing.

Zhong Xie

Zhong Xie is currently a Professor with China University of Geosciences, Wuhan, China. His research interests include geographic calculation and spatial analysis, spatial database, and mobile geographic information system.

Qingquan Li

Qingquan Li received the Ph.D. degree in geographic information system (GIS) and photogrammetry from Wuhan Technical university of Surveying and Mapping, Wuhan, China, in 1998. He is currently a Professor with Shenzhen University Guangdong, China. His research areas include 3-D and dynamic data modeling in GIS, location-based service, surveying engineering, Global Positioning System and remote sensing, intelligent transportation system and road surface checking.