TY - GEN
T1 - A visual analytics system for metropolitan transportation
AU - Liu, Siyuan
AU - Liu, Ce
AU - Luo, Qiong
AU - Ni, Lionel M.
AU - Qu, Huamin
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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 spatio-temporal 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.
AB - 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 spatio-temporal 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.
UR - http://www.scopus.com/inward/record.url?scp=84863032308&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863032308&partnerID=8YFLogxK
U2 - 10.1145/2093973.2094053
DO - 10.1145/2093973.2094053
M3 - Conference contribution
AN - SCOPUS:84863032308
SN - 9781450310314
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 477
EP - 480
BT - 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011
T2 - 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011
Y2 - 1 November 2011 through 4 November 2011
ER -