Algorithm Fusion for Detecting Incidents on Singapore’s Central Expressway
Publication: Journal of Transportation Engineering
Volume 132, Issue 4
Abstract
Many studies have focused mainly on the development of a single automatic incident detection algorithm to detect incident occurrences, and the performance obtained has often been mixed. In this study, an algorithm fusion method was developed using incident data collected from the Central Expressway in Singapore. This method explores the possibility of enhancing incident detection performance by combining the complementary advantages of a group of existing algorithms. It was found that the fused algorithm with the following options has outperformed the existing algorithms: Fusion Option II—selecting a combination of the best-performing algorithms, and Fusion Option III—applying different weightings to a group of algorithms. Compared with the existing dual variable/combined detector evaluation algorithms developed earlier for the same studied site, the fused algorithms performed significantly better, with false alarm rates between 0.2 and 1.0%. At a detection rate of 90%, these fused algorithms were able to reduce the false alarm rate by more than 55%. This algorithm fusion method had yielded promising performance and thus can serve as an alternative technique to the commonly practiced approach of either developing a new algorithm or applying the existing algorithms to detect expressway incident occurrences.
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Acknowledgments
The writers wish to thank the Land Transport Authority of Singapore for giving them permission 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., and Ritchie, S. G. (1999). “Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network.” Transp. Res., Part C: Emerg. Technol. 7(5), 261–280.
Byun, S. C., Choi, D. B., and Ahn, B. H. (1999). “Traffic incident detection using evidential reasoning based data fusion.” Proc., 6th World Congress on Intelligent Transport Systems, Toronto.
Chung, E., and Rosalion, N. (1999). “Effective incident detection and management on freeways.” Research Rep. ARR 327, ARRB Transport Research, Victoria, Australia.
Cohen, S., and Christine, J. Y. (1995). DAISI user manual: Release 4.2, INRETS, Arcueil, France.
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.
Dia, H., and Rose, G. (1997). “Development and evaluation of neural network freeway incident detection models using field data.” Transp. Res., Part C: Emerg. Technol. 5(5), 313–331.
Dudek, C. L., Messer, C. J., and Nuckles, N. B. (1974). “Incident detection on urban freeways.” Transportation Research Record 495, Transportation Research Board, Washington, D.C., 12–24.
Jin, X., Cheu, R. L., and Srinivasan, D. (2002). “Development and adaptation of constructive probabilistic neural network in freeway incident detection.” Transp. Res., Part C: Emerg. Technol. 10(2), 121–147.
Karim, A., and Adeli, H. (2002). “Incident detection algorithm using wavelet energy representative of traffic patterns.” J. Transp. Eng., 128 (3), 232–242.
Mak, C. L. (2002). “New dual-variable algorithms for detecting lane-blocking incidents on expressways.” Ph.D. thesis, Nanyang Technological Univ., Singapore.
Mak, C. L., and Fan, H. S. L. (2005). “Transferability of expressway incident detection algorithms to Singapore and Melbourne.” J. Transp. Eng., 131(2), 101–111.
Mak, C. L., and Fan, H. S. L. (2006). “Single-station algorithm using video-based data for detecting expressway incidents.” Comput. Aided Civ. Infrastruct. Eng., 21(2), 120–135.
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, Federal Highway Administration, Washington, D.C.
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.
Teng, H., and Qi, Y. (2003). “Application of wavelet technique to freeway incident detection.” Transp. Res., Part C: Emerg. Technol. 11(3-4), 289–308.
Zhou, D. S. (2000). “An integrated traffic incident detection model.” Ph.D. thesis, Univ. of Texas at Austin, Austin, Tex.
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© 2006 ASCE.
History
Received: May 25, 2004
Accepted: Aug 2, 2005
Published online: Apr 1, 2006
Published in print: Apr 2006
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