The visual analysis of flow pattern for public bicycle system. (April 2018)

• Record Type: Journal Article
• Authors: Shi, Xiaoying; Yu, Zhenhai; Chen, Jing; Xu, Haitao; Lin, Fei
• Is Part Of: Journal of visual languages & computing. Volume 45(2018)
• Page Start: 51
• Page End: 60
• Publication Date: 2018-04
• Subjects: Visual analytics -- Intelligent transportation system -- Public bicycle system -- Traffic data visualization -- Human mobility; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.03.007
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Public Bicycle System(PBS) is an increasingly popular mode of public transit, with the advantage of pollution-free and flexibility. In this paper, we present an interactive visual analytic system for exploring complex flows generated by PBS. Four inter-linked visualization views are designed to illustrate multiple perspectives of data, such as the spatial-temporal changes, the relationships and differences between flow OD pairs and the multi-dimensional factors(weather condition, calendar events) influencing on the rental numbers. A new presentation "Parallel coordinates with line and set" combined with flexible interaction schemes is proposed to support the exploration of multivariate association. We exemplify our approach with a real citywide PBS dataset. The results of case study demonstrate that our system is helpful for visually classifying stations with different flow patterns, speculating in-depth reasons, as well as investigating abnormal behaviors, helping decision makers to gain a better understanding of the large dataset.