QuteVis: Visually Studying Transportation Patterns Using Multisketch Query of Joint Traffic Situations

被引：4

作者：

AL-Dohuki, Shamal ^{[1
]}

Zhao, Ye ^{[1
]}

Kamw, Farah ^{[1
]}

Yang, Jing ^{[2
]}

Ye, Xinyue ^{[3
]}

Chen, Wei ^{[4
]}

机构：

[1] Kent State Univ, Kent, OH 44242 USA

[2] Univ N Carolina, Charlotte, NC USA

[3] New Jersey Inst Technol, Newark, NJ 07102 USA

[4] Zhejiang Univ, Hangzhou 310058, Peoples R China

来源：

IEEE COMPUTER GRAPHICS AND APPLICATIONS | 2021年 / 41卷 / 02期

关键词：

Data visualization; Urban areas; Transportation; Tools; Visual analytics; Trajectory; EXPLORATION; VISUALIZATION;

D O I：

10.1109/MCG.2019.2911230

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

QuteVis uses multisketch query and visualization to discover specific times and days in history with specified joint traffic patterns at different city locations. Users can use touch input devices to define, edit, and modify multiple sketches on a city map. A set of visualizations and interactions is provided to help users browse and compare retrieved traffic situations and discover potential influential factors. QuteVis is built upon a transport database that integrates heterogeneous data sources with an optimized spatial indexing and weighted similarity computation. An evaluation with real-world data and domain experts demonstrates that QuteVis is useful in urban transportation applications in modern cities.

引用

页码：35 / 47

页数：13

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