Transferability of Expressway Incident Detection Algorithms to Singapore and Melbourne
Publication: Journal of Transportation Engineering
Volume 131, Issue 2
Abstract
This study investigates the performance of several existing automatic incident detection algorithms along the Central Expressway in Singapore and freeways in Melbourne, Australia. These algorithms were originally developed for freeways in the United States. Thus, it is of interest to evaluate how they would perform when applied to cities in other countries. The evaluation is carried out on two databases containing 160 and 100 incidents collected from Singapore and Melbourne, respectively. These databases reflect differences in vehicle detector system used to collect traffic parameters and in driver behavior. The following empirical findings were obtained: (1) the Minnesota and Standard Normal Deviate (SND) algorithms appear to possess transferable properties as well as being able to receive wide-area traffic measurements from a machine-vision vehicle detector system; (2) California Algorithm number 7 and the Double Exponential Smoothing algorithm performed poorly in Singapore but may be transferable to Melbourne freeways; and (3) no significant difference in average efficiencies between the better-performing algorithms (SND and Minnesota) on both databases.
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Acknowledgments
The writers wish to thank the Land Transport Authority of Singapore and VicRoads of Victoria State, Australia for giving their permissions to the authors to collect the traffic and incident data used in this study. Sincere appreciation is also extended to the reviewers for their helpful comments and suggestions on improving this paper.
References
Abdulhai, B. (1996). “A neuro-genetic-based universally transferable freeway incident detection framework.” PhD thesis, Univ. of California, Irvine, Calif.
Chassiakos, A. P., and Stephanedes, Y. J. (1993). “Smoothing algorithms for incident detection.” Transportation Research Record 1394, Transportation Research Board, Washington, D.C., 8–16.
Cheu, R. L. (1994). “Neural network models for automated detection of lane-blocking incidents on freeways.” PhD thesis, Univ. of California, Irvine, Calif.
Coifman, B., Beymer, D., McLauchlan, P., and Malik, J. (1998). “A real-time computer vision system for vehicle tracking and traffic surveillance.” Transp. Res., Part C: Emerg. Technol., 6C(4), 271–288.
Cook, A. R., and Cleveland, D. E. (1974). “Detection of freeway capacity-reducing incidents by traffic-stream measurements.” Transportation Research Record 495, Transportation Research Board, Washington, D.C., 1–11.
Dudek, C. L., Messer, C. J., and Nuckles, N. B. (1974). “Incident detection on urban freeways.” Transportation Research Record, Transportation Research Board, Washington, D.C., 12–24.
Michalopoulos, P. G., Jacobson, R. D., Anderson, C. A., and DeBruycker, T. B. (1993). “Automatic incident detection through video image processing.” Traffic Eng. Control, 34(2), 66–74.
Payne, H. J., Hwelfenbein, E. D., and Knobel, H. C. (1976). “Development and testing of incident detection algorithms, Vol. 2: Research methodology and results.” Rep. No. FHWA-RD-76-20, U.S. Federal Highway Administration, Washington, D.C.
“Signs like these help save $40M every year.” (2002). The Straits Times Singapore, Feb. 9, H1.
Stephanedes, Y. J., and Chassiakos, A. P. (1993). “Freeway incident detection through filtering.” Transp. Res., Part C: Emerg. Technol., 1C(3), 219–233.
Stephanedes, Y. J., and Hourdakis, J. (1996). “Transferability of freeway incident detection algorithms.” Transportation Research Record 1554, Transportation Research Board, Washington, D.C., 184–195.
VicRoads (1997). “Tullamarine and South Eastern Freeways incident data, documentation and graphical presentation.” Traffic System Development, Australia.
Weil, R., Wootton, J., and Garcia-Ortiz, A. (1998). “Traffic incident detection: Sensors and algorithms.” Math. Comput. Modell., 27(9–1l), 257–291.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 131 • Issue 2 • February 2005
Pages: 101 - 111
Copyright
© 2005 ASCE.
History
Received: Jun 27, 2003
Accepted: Mar 26, 2004
Published online: Feb 1, 2005
Published in print: Feb 2005
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