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

Shamal Al-Dohuki; Ye Zhao; Farah Kamw; Jing Yang; Xinyue Ye; Wei Chen

doi:10.1109/MCG.2019.2911230

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

Shamal Al-Dohuki, Ye Zhao, Farah Kamw, Jing Yang, Xinyue Ye, Wei Chen

Informatics

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

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.

Original language	English (US)
Article number	8691491
Pages (from-to)	35-48
Number of pages	14
Journal	IEEE Computer Graphics and Applications
Volume	41
Issue number	2
DOIs	https://doi.org/10.1109/MCG.2019.2911230
State	Published - Mar 1 2021

All Science Journal Classification (ASJC) codes

Software
Computer Graphics and Computer-Aided Design

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10.1109/MCG.2019.2911230

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abstract = "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.",

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AU - Ye, Xinyue

AU - Chen, Wei

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

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