Visual Exploratory Data Analysis of Traffic Volume
Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically.
KeywordsTraffic Flow Traffic Volume Traffic Signal Intelligent Transportation System Transportation Engineer
Unable to display preview. Download preview PDF.
- 1.Miller, H.J., Shaw, S.L.: Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press, New York (2001)Google Scholar
- 2.Han, J., Kamber, M.: Data Mining: Concepts and Technologies. Morgan Kaufmann Publisher, San Francisco (2001)Google Scholar
- 4.Gershon, N.: From perception to visualization. In: Rosenblum, L., et al. (eds.) Scientific Visualization 1994: Advances and Challenges, pp. 129–139. Academic Press, New York (1994)Google Scholar
- 7.Catarci, T., Santucci, G., Costabile, M.F., Cruz, I.: Foundations of the DARE system for drawing adequate representations. In: Proceedings of International Symposium on Database Applications in Non-Traditional Environments, pp. 461–470. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
- 8.Bertin, J.: Semiology of Graphics. University Wisconsin Press, Wisconsin (1983)Google Scholar
- 9.Minnotte, M., West, W.: The data image: a tool for exploring high dimensional data sets. In: Proceedings of the ASA Section on Statistical Graphics, Dallas, Texas, pp. 25–33 (1998)Google Scholar
- 12.Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)Google Scholar