A generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis. (21st May 2019)

• Record Type: Journal Article
• Authors: Wang, Shuangyan; Mei, Gang; Cuomo, Salvatore
• Other Names: Jeon Gwanggil guestEditor.; Abdellah Chehri guestEditor.; Cuomo Salvatore guestEditor.; Din Sadia guestEditor.; Jabbar Sohail guestEditor.; Kosta Sokol guestEditor.; Laccetti Giuliano guestEditor.; Lapegna Marco guestEditor.; Mele Valeria guestEditor.; Montella Raffaele guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-21
• Subjects: complex network analysis -- data mining -- GPS trajectory -- human mobility; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5335
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The mining of human mobility can be exploited to support the design of traffic planning, route recommendations, urban planning, emergency management, and land use. Currently, various methods such as the machine learning algorithms, statistical methods, and semantic analysis are widely applied to identify and extract human mobility patterns. In this paper, we propose a simple and generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis. The essential ideas behind the proposed paradigm mainly include (1) creating weighted complex networks of GPS trajectories and (2) extracting the human mobility patterns by analyzing the structures and metrics of the created complex networks of GPS trajectories. To evaluate the performance of the proposed paradigm, we design five groups of experiments and identify the mobility patterns of the selected five persons based on the selected five network analysis metrics. Experimental results indicate that (1) the proposed paradigm is effective and (2) the quite interesting potential information about the human mobility can be mined easily. The proposed paradigm is simple and generic, which can be employed to rapidly identify the human mobility patterns based on the GPS trajectory data.