Incident Detection Algorithm using Wavelet Energy Representation of Traffic Patterns
Publication: Journal of Transportation Engineering
Volume 128, Issue 3
Abstract
Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. Earlier algorithms for freeway incident problems have produced less reliable results, especially in recurrent congestion and compression wave traffic conditions. This article presents a new two-stage single-station freeway incident detection model based on advanced wavelet analysis and pattern recognition techniques. Wavelet analysis is used to denoise, cluster, and enhance the raw traffic data, which is then classified by a radial basis function neural network. An energy representation of the traffic pattern in the wavelet domain is found to best characterize incident and nonincident traffic conditions. False alarm during recurrent congestion and compression waves is eliminated by normalization of a sufficiently long time-series pattern. The model is tested under several traffic flow scenarios including compression wave conditions. It produced excellent detection and false alarms characteristics. The model is computationally efficient and can readily be implemented online in any ATMS without any need for recalibration.
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References
Adeli, H., and Hung, S. L. (1995). Machine learning—neural networks, genetic algorithms, and fuzzy systems, Wiley, New York.
Adeli, H., and Karim, A.(2000). “A fuzzy-wavelet RBFNN model for freeway incident detection.” J. Transp. Eng., 126(6), 464–471.
Ahmed, S. A., and Cook, A. R.(1982). “Application of time-series analysis techniques to freeway incident detection.” Transp. Res. Rec., 841, Transportation Research Board, Washington, D.C., 19–28.
Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms, Plenum, New York.
Burrus, C. S., Gopinath, R. A., and Guo, H. (1998). Introduction to wavelets and wavelet transforms—A primer, Prentice-Hall, Upper Saddle River, N.J.
Cannon, R. L., Dave, J. V., and Bezdek, J. C.(1986). “Efficient implementation of the fuzzy c-means clustering algorithms.” IEEE Trans. Pattern Anal. Mach. Intell., PAMI-8(2), 248–255.
Chassiakos, A. P., and Stephanedes, Y. J.(1993). “Smoothing algorithms for incident detection.” Transp. Res. Rec., 1394, Transportation Research Board, Washington, D.C., 8–16.
Cheu, R. L., and Ritchie, S. G.(1995). “Automated detection of lane-blocking freeway incidents using artificial neural networks.” Transp. Res., Part C 3(6), 371–388.
Cook, A. R., and Cleveland, D. E.(1974). “Detection of freeway capacity-reducing incidents by traffic stream measurements.” Transp. Res. Rec., 495, Transportation Research Board, Washington, D.C., 1–11.
Daubechies, I. (1992). Ten lectures on wavelets, SIAM, Philadelphia.
Dia, H., and Rose, G.(1997). “Development and evaluation of neural network freeway incident detection models using field data.” Transp. Res., Part C, 5(5), 313–331.
Hsiao, C.-H., Lin, C.-T., and Cassidy, M.(1994). “Applications of fuzzy logic and neural networks to automatically detect freeway traffic incidents.” J. Transp. Eng., 120(5), 753–772.
Ishak, S. S., and Al-Deek, H. M.(1998). “Fuzzy ART neural network model for automated detection of freeway incidents.” Transp. Res. Rec., 1634, Transportation Research Board, Washington, D.C., 56–63.
Lin, C.-K., and Chang, G.-L.(1998). “Development of a fuzzy-expert system for incident detection and classification.” Math. Comput. Modell., 27(9–11), 9–25.
Lin, W.-H., and Daganzo, C. F.(1997). “A simple detection scheme for delay-inducing freeway incidents.” Transp. Res., Part A, 31(2), 141–155.
Moody, J., and Darken, C. J.(1989). “Fast learning in networks of locally-tuned processing units.” Neural Comput., 1, 281–294.
Payne, H. J., and Tignor, S. C.(1978). “Freeway incident-detection algorithms based on decision trees with states.” Transp. Res. Rec., 682, Transportation Research Board, Washington, D.C., 30–37.
Persaud, B. N., and Hall, F. L.(1989). “Catastrophe theory and patterns in 30-second freeway traffic data—implications for incident detection.” Transp. Res., Part A, 23A(2), 103–113.
Samant, A., and Adeli, H. (2000). “Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis.” Comput.-Aided Civ. Infrastruct. Eng., 15(4).
Weil, R., Wootton, J., and Garcia-Ortiz, A.(1998). “Traffic incident detection: Sensors and algorithms.” Math. Comput. Modell., 27(9–11), 257–291.
Xu, H., Kwan, C. M., Haynes, L., and Pryor, J. D.(1998). “Real-time adaptive on-line traffic incident detection.” Fuzzy Sets Syst., 93, 173–183.
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Copyright © 2002 American Society of Civil Engineers.
History
Received: Aug 22, 2000
Accepted: Sep 18, 2001
Published online: Apr 15, 2002
Published in print: May 2002
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