DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns

SUN Guodao; LI Si; CAO Dizhou; LIU Chunhui; JIANG Xiaorui; LIANG Ronghua

doi:10.1049/cje.2017.12.008

Volume 27 Issue 5

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SUN Guodao, LI Si, CAO Dizhou, et al., “DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 942-950, 2018, doi: 10.1049/cje.2017.12.008

Citation:

SUN Guodao, LI Si, CAO Dizhou, et al., “DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 942-950, 2018, doi: 10.1049/cje.2017.12.008

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DiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns

doi: 10.1049/cje.2017.12.008

1.
College of Information Engineering, Zhejiang University of Techonology, Hangzhou 310012, China;
2.
Department of Computer Science and Mathematics, Aston University, Birmingham B4 7ET, UK

Funds: This work is supported by the National Natural Science Foundation of China (No.61602409), Zhejiang Provincial NSFC (No.LR14F020002), joint project Data-Driven Intelligent Transportation between China and Europe announced by the Ministry of Science and Technology of China (No.SQ2013ZOC200020), and the Open Projects Program of Key Laboratory of Ministry of Public Security based on Zhejiang Police College (No.2016DSJSYS003).

More Information

Corresponding author: LIANG Ronghua (corresponding author) received the Ph.D. degree in computer science from Zhejiang University in 2003. He worked as a research fellow at the University of Bedfordshire, UK, from April 2004 to July 2005 and as a visiting scholar at the University of California, Davis, US, from March 2010 to March 2011. He is currently a professor of Computer Science and Dean of College of Information Engineering, Zhejiang University of Technology, China. His research interests include visual analytics, computer vision, and medical visualization. (Email:rhliang@zjut.edu.cn)
Received Date: 2017-03-16
Rev Recd Date: 2017-07-09
Publish Date: 2018-09-10

Abstract

Abstract

Traffic jam has become a severe urban problem to most metropolises in the world. How to understand and resolve these traffic problems has become a global issue. In the new era of big data, visualization and analysis with traffic-related data are increasingly appreciated. This paper presents DiffusionInsighter, a web-based visual traffic analysis system, that allows users to explore the traffic flow and diffusion patterns with different spatial and temporal granularity. The DiffusionInsighter first applies a visual data cleaning and filtering component to remove dirty data and remain available ones for further analysis. A set of carefully designed interaction and visualization tools including geographical view, pixel map view, chord diagram and network diffusion view is proposed in the DiffusionInsighter to support level-of-detail exploration of diffusion patterns of the traffic flow. Different views are collaborated together and are integrated into geographic map. A series of real-life case studies are conducted using a large GPS trajectory dataset of taxis in Hangzhou.
- GPS trajectories,
- Visual analysis,
- Traffic dynamics,
- Spatio-temporal aggregation

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References(17)

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