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VAIT: A Visual Analytics System for Metropolitan Transportation

Authors Liu, Siyuan HKUST affiliated (currently or previously)
Pu, Jiansu HKUST affiliated (currently or previously).
Luo, Qiong View this author's profile
Qu, Huamin View this author's profile
Ni, Lionel Ming-Shuan View this author's profile
Krishnan, Ramayya
Issue Date 2013
Source IEEE Transactions on Intelligent Transportation Systems , v. 14, (4), December 2013, article number 6522454, p. 1586-1596
Summary With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research.
ISSN 1524-9050
Language English
Format Article
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