Exploring Textural Characteristics of Spatiotemporal Traffic Contour Maps
Publication: Journal of Transportation Engineering
Volume 130, Issue 1
Abstract
This study presents an approach to extract properties from spatiotemporal traffic speed contour maps using tools from the field of digital image analysis. The new measures are derived from second-order statistics and quantify properties such as smoothness, homogeneity, regularity, and randomness in traffic operations. Four measures were selected: angular second moment (ASM), contrast (CON), inverse difference moment, and entropy. Each measure was used to characterize speed contour maps derived from different traffic conditions. To avoid information redundancy, correlation was examined between first- and second-order measures, which resulted in disqualifying one of the four measures. The sensitivity of the new measures to variations in traffic conditions was also investigated using nearly 14,000 30 min speed contour maps generated from a section of 5.44 km of the freeway in five weekdays. It was found that the speed range of 32–48 km/h exhibited the highest randomness (entropy) and least smoothness (ASM). The lowest level of homogeneity (CON) was observed in the range of 48–80 km/h. The new measures were also used to evaluate the level of service.
Get full access to this article
View all available purchase options and get full access to this article.
References
Choe, T., Skabardonis, A., and Varaiya, P. (2002). “Freeway performance measurement system (PeMS): An operational analysis tool.” 81st Annual Meeting of the Transportation Research Board, Preprint CD-Rom, Washington, D.C.
Highway capacity manual. (2000). Transportation Research Board, National Research Council, Washington, D.C.
Ishak, S., and Al-Deek, H. (1998). “Fuzzy ART neural network model for automated detection of freeway incidents.” Transportation Research Record 1634, Transportation Research Board, Washington, D.C., 56–63.
Ishak, S., and Al-Deek, H.(1999). “Performance of automatic ANN-based incident detection on freeways.” J. Transp. Eng., 125(4), 281–290.
Kwon, J., Coifman, B., and Bickel, P. (2000). “Day-to-day travel time trends and travel time prediction from loop detector data,” Transportation Research Record 1717, Transportation Research Board, Washington, D.C., 120–129.
Liu, H., and Motoda, H. (2001). Feature extraction construction and selection, a data mining perspective, 2nd Ed., Kluwer, Norwell, Mass.
Micheli-Tzanakou, E. (2000). Supervised and unsupervised pattern recognition, feature extraction and computational intelligence, CRC Press, Boca Raton, Fla.
Nixon, M., and Aguado, A. (2002). Feature extraction and image processing, Newnes, Woburn, Mass.
Roess, R., McShane, W., and Prassas, E. (1998). Traffic engineering, Prentice–Hall, Englewood Cliffs, N.J.
Seul, M., O’Gorman, L., and Sammon, M. (2000). Practical algorithms for image analysis, Cambridge Univ. Press, New York.
Theodoridis, S., and Koutroumbas, K. (1999). Pattern recognition, Academic, San Diego.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 130 • Issue 1 • January 2004
Pages: 122 - 131
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Jun 6, 2002
Accepted: Feb 6, 2003
Published online: Dec 15, 2003
Published in print: Jan 2004
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.