VISOS: A Visual Interactive System for Spatial-Temporal Exploring Station Importance Based on Subway Data

In urban cities, multiple intelligent transportation systems generate a large amount of traffic data. Researchers can make well use of these data to provide solutions for solving numerous existing traffic problems, such as traffic congestions and urban transportation resource allocating. Thus, it is meaningful and feasible for traffic researchers to collect these data and analyze the concealed human mobility based on them. In this paper, we propose a visual interactive subway system (VISOS). The system incorporates subway data visualization module, spatial–temporal exploration module, and station clustering module. VISOS utilizes $k$ -means clustering algorithm to explore the subway data interactively, analyze human mobility pattern responsively, and identify functional characteristics of subway stations precisely. In addition, in this paper, we provide a comprehensive spatial–temporal exploration based on the real Shanghai subway data to analyze the importance level of subway stations.