With the increasing availability of metropolitan transportation data, such as those from vehicle GPSs (Global Positioning systems) and road-side sensors, it becomes viable for authorities, operators, as well as individuals to analyze the data for a better understanding of the transportation system and possibly improved utilization and planning of the system. We report our experience in building the VAST (Visual Analytics for Smart Transportation) system. Our key observation is that metropolitan transportation data are inherently visual as they are spatiotemporal around road networks. Therefore, we visualize traffic data together with digital maps and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAST on real-world taxi GPS and meter data sets from 15,000 taxis running two months in a Chinese city of over 10 million population. We discuss the technical challenges in data cleaning, storage, visualization, and query processing, and offer our first-hand lessons learned from developing the system.
Vehicle trajectory, Spatiotemporal data, Visual analytics
Databases and Information Systems
Liu, Siyuan; Liu, Ce; Luo, Qiong; Ni, Lionel M.; and Qu, Huamin.
A Visual Analytics System for Metropolitan Transportation. (2011). LARC Research Publications.
Available at: http://ink.library.smu.edu.sg/larc/7