VAIT: A visual analytics system for metropolitan transportation

Siyuan Liu, Jiansu Pu, Qiong Luo, Huamin Qu, Lionel M. Ni, Ramayya Krishnan

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


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.

Original languageEnglish (US)
Article number6522454
Pages (from-to)1586-1596
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
StatePublished - Dec 2013

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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