Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection
Author:
Affiliation:
1. Prof., Dept. of Civ. and Envir. Engrg. and Geodetic Sci., Ohio State Univ., Columbus, OH 43210.
2. Grad. Res. Assoc., Dept. of Civ. and Envir. Engrg. and Geodetic Sci., Ohio State Univ., Columbus, OH.
Publisher
American Society of Civil Engineers (ASCE)
Subject
Transportation,Civil and Structural Engineering
Link
http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290733-947X%282000%29126%3A6%28464%29
Reference33 articles.
1. Adeli H. and Hung S. L. ( 1995). Machine learning—Neural networks genetic algorithms and fuzzy systems Wiley New York.
2. Adeli H. and Park H. S. ( 1998). Neurocomputing for design automation CRC Boca Raton Fla.
3. 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.
4. Amin S. M. Rodin E. Y. Liu A.-P. Rink K. and Garcia-Ortiz A. ( 1998). “Traffic prediction and management via RBF neural nets and semantic control.” Comp.-Aided Civ. and Infrastructure Engrg. 13 315–325.
5. Bezdek J. C. ( 1981). Pattern recognition with fuzzy objective function algorithms Plenum New York.
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