Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지

The Development of Genetic Fuzzy System for Estimating Link Traveling Speed

주행속도 추정을 위한 Genetic Fuzzy System의 개발

  • Youn, Yeo-Hun (Department of Industrial Systems and Information Engineering, Korea University) ;
  • Lee, Hong-Chul (Department of Industrial Systems and Information Engineering, Korea University) ;
  • Kim, Yong-Sik (Department of Industrial Systems and Information Engineering, Korea University)
  • 윤여훈 (고려대학교 산업시스템정보공학과) ;
  • 이홍철 (고려대학교 산업시스템정보공학과) ;
  • 김용식 (고려대학교 산업시스템정보공학과)
  • Published : 2003.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we develop the Genetic Fuzzy System(GFS) to estimate the link traveling speed. Based on the genetic algorithm, we can get the fuzzy rules and membership functions that reflect more accurate correlation between traffic data and speed. From the fact that there exist missing links that lack traffic data, we added a Case Base Reasoning(CBR) to GFS to support estimating the speed of missing links. The case base stores the fuzzy rules and membership functions as its instances. As cases are accumulated, the case base comes to offer appropriate cases to missing links. Experiments show that the proposed GFS provides the more accurate estimation of link traveling speed than existing methods.

Keywords

References

  1. Berry, M. J. A. and Linoff, G. (1997) Data Mining Techniques, 335-359, Wiley Computer Publishing
  2. Castro, .J. L. (1995), Fuzzy logic controllers are universal approximators, IEEE Trans. Systems. Man. and Cybernetics., 25 (4),629-635 https://doi.org/10.1109/21.370193
  3. Gong, S-G., Kim, I-T., Park, D-H., Park, J-Y., and Shin, Y-A. (1996), Genetic Algorithm, Green, Seoul, Korea
  4. Hwang, I-S., and Lee, H-C. (2000), The Estimation of Link Travel Speed Using Hybrid Neuro-Fuzzy Networks, Journal of the Korean Institute of Industrial Engineers, 26 (4), 306-314
  5. Lin, C. T., and Lee, C. S. G. (1999), Neural Fuzzy Systems, 534-608, PrenticeHall Inc.
  6. Nelson, P. and Palacharla, P. (1993), A Neural Network Model for Data Fusion in Advance, Transtech Pacific Rim Conference, Seattle, Washington
  7. Rouphail, N. M., Tarko, A., Nelson, P. and Palacharla, P. (1993), Travel Time Data Fusion in ADVANCE-A Preliminary Design Concept, Advance Wroking Paper Series, 21, Jan
  8. Schofer, J. L., and Koppelman, F. S. (1995), Use of Multiple Data Sources for Arterial Street Incident Detection, World Conference on Transportation Research
  9. Sisiopiku, V. P., Palacharia, P. and Nelson, P. C. (1994), Fuzzy Reasoning Model for Converting Loop Detector Data into Travel Times, Advance Wroking Paper Series, 38, June
  10. Tarko, A. and Rouphail, N. M. (1993), Travel Time Data Fusion in Advance, Advance Wroking Paper Series, 28, Aug.
  11. Wang, L. X., and Mendel, J. M. (1992), Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems. Man. and Cybernetics 22, 1414-1427