Open AccessDiffusionInsighter: Visual Analysis of Traffic Diffusion Flow Patterns
Author(s) -
Liu Chunhui,
Sun Guodao,
Li Si,
Cao Dizhou,
Jiang Xiaorui,
Liang Ronghua
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.12.008
Subject(s) - computer science , flow (mathematics) , diffusion , statistical physics , mechanics , physics , thermodynamics
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.