@article{ref1,
title="Qutevis: visually studying transportation patterns using multi-sketch query of joint traffic situations",
journal="IEEE computer graphics and applications",
year="2019",
author="Al-Dohuki, Shamal and Zhao, Ye and Kamw, Farah and Yang, Jing and Ye, Xinyue and Chen, Wei",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="QuteVis uses multi-sketch 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 are 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.<p /> <p>Language: en</p>",
language="en",
issn="0272-1716",
doi="10.1109/MCG.2019.2911230",
url="http://dx.doi.org/10.1109/MCG.2019.2911230"
}