A review of some main models for traffic flow forecasting

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn 1, Issue , 2003, Pages 216-219

  Han, Chao ^a   Song, Su ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

ARIMA model; Neural network model; Nonparametric model; Traffic flow; Traffic forecasting

Indexed keywords

FORECASTING; INTELLIGENT SYSTEMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; REAL TIME SYSTEMS; TRAFFIC CONTROL; TRANSPORTATION;

ARIMA MODELING; NEURAL NETWORK MODEL; NON-PARAMETRIC MODEL; TRAFFIC FLOW; TRAFFIC FORECASTING;

STREET TRAFFIC CONTROL;

EID: 67649193495     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2003.1251951     Document Type: Conference Paper

References (16)

1. Bashir, Z. and El-Hawary, M.E., Short term load forecasting by using wavelet neural networks,Canadian Conference on Electrical and Computer Engineering, (2000) 163-166.
2. Brockwell, P.J. and Davis, R.A. Time Series: Theory and Methods (Springer-Verlag, NewYork, 1991)..
3. Cristi, R. and Tummula, M., Multirate, multiresolution, recursive Kalman filter, Signal Processing 80 (2000) 1945-1958.
4. Brian L. Smith, Billy M. Williams, R. Keith Oswald, Comparison of parametric and nonparametric models for traffic flow forecasting, Transportation Research Part C 10 (2002) 303-321
5. Disbro, J.E., Frame, M., 1989. Traffic flow theory and chaotic behavior. In: Transportation Research Record, TRB, vol. 1225. National Research Council, Washington, DC, pp. 109-115.
6. S.C.Chang, R.S.Kim, S.J.Kim, B.H.Ahn, Traffic-Flow Forecasting Using a 3-stage Model,Dept. of Mechatronics, K-JIST 1, Oryong-dong, Puk-ku, Kwangju 500-712, Korea.
7. K. Hornik, "Multilayer feedforward networks are universal approimators", Neural Networks, vol.2, pp359-366, 1989.
8. Funahashi, "On the approximate realization of continuous mapping by neural networks", Neural Networks, vol.2, pp. 183-192, 1989.
9. H. White, "Connectionist non-parametric regression: multiplayer feedforward networks can learn arbitrary mapping", Neural Networks, vol.3, pp.535-549, 1990.
10. Lee, Jae K., Yum, C.S. and Kim, W.J., "Neural Network Based Judgemental Adjustment for Time Series Forecasting", Proceedings of the International Conference EANN '95, pp.299-392, Aug., 1995.
11. Haibo Chen and Susan Grant-Muller. Use of sequential learning for short-term traffic flow forecasting,Transportation Research Part C: Emerging Technologies, Volume 9, Issue 5, October 2001, Pages 319-336
12. Smith, Brian L: Demetsky, Michael J, Traffic flow forecasting: comparison of modeling approaches, Journal of Transportation Engineering v 123 July/Aug 1997 p.261-266.
13. Principal Investigator: Brian L. Smith, ATA Fellow: Kevin W. Smith. AN INVESTIGATION INTO INCIDENT DURATION FORECASTING FOR FLEETFORWARD. http://www.paragoncom.com, August 29, 2000.
14. Bart Van Arem, Howard R. Kirby, Martie J. M. Van Der Vlist and Joe C. Whittaker, Recent advances and applications in the field of short-term traffic forecasting, International Journal of Forecasting, Volume 13, Issue 1, March 1997, Pages 1-12
15. Hongbin Yin, S. C. Wong, Jianmin Xu and C. K. Wong, Urban traffic flow prediction using a fuzzy-neural approach, Transportation Research Part C: Emerging Technologies. Volume 10. Issue 2, April 2002. Pages 85-98
16. Dougherty, M. and Cobbett, M., Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting 13, 1997. pp. 21-31.