Short term traffic flow prediction based on online learning SVR

Proceedings - 2008 Workshop on Power Electronics and Intelligent Transportation System, PEITS 2008

Volumn , Issue , 2008, Pages 616-620

  Zeng, Dehuai ^a,b   Xu, Jianmin ^a   Gu, Jianwei ^c   Liu, Liyan ^c   Xu, Gang ^b  

^a South China University of China   (China)

^b SHENZHEN UNIVERSITY   (China)

^c Guangzhou Post and Telecom Equipment Co Ltd   (China)

Author keywords

Intelligent transportation systems; Support vector regression; Traffic flow prediction

Indexed keywords

BACKPROPAGATION; COMPUTER NETWORKS; DATA FLOW ANALYSIS; ELECTRIC LOAD FORECASTING; ERRORS; FORECASTING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; INTERNET; LEARNING SYSTEMS; NEURAL NETWORKS; POWER ELECTRONICS; TECHNICAL PRESENTATIONS; TIME SERIES ANALYSIS; TRAFFIC SURVEYS; VECTORS; VEHICLE LOCATING SYSTEMS;

ARTIFICIAL NEURAL NETWORKS; FORECASTING METHODS; INCREMENTAL LEARNINGS; INTELLIGENT TRANSPORTATION SYSTEMS; MEAN ERRORS; ON-LINE LEARNINGS; ONLINE SUPPORTS; PREDICTION FUNCTIONS; REAL TIMES; REAL-TIME TRAFFICS; ROOT MEAN SQUARED ERRORS; SHORT TERM TRAFFIC FLOW PREDICTIONS; SUPPORT VECTOR REGRESSION; TIME EFFICIENCIES; TIME SERIES; TRAFFIC DATUMS; TRAFFIC FLOW FORECASTING; TRAFFIC FLOW PREDICTION; TRAFFIC FLOW PREDICTIONS; TRAVEL TIMES;

STREET TRAFFIC CONTROL;

EID: 56449099841     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PEITS.2008.134     Document Type: Conference Paper

References (12)

1. Davis, G. A. and Nihan, N. L. "Nonparametric regression and short term freeway traffic forecasting." Journal of Transportation Engineering, pp.178-188, 1991.
2. Liang Zhao Fei-Yue Wang "Short-term traffic flow prediction based on ratio-median lengths of intervals two-factors high-order fuzzy time series", Proc. of Conf. on Vehicular Electronics and Safety, Dec. 2007, pp.1-7
3. Smith. B. L. and Demetsky. M. J., "Traffic flow forecasting: comparison of modeling approaches." Journal of Transportation Engineering, vol. 123, no. 4, p.261-266, 1997.
4. Kaysi. I., Ben-Akiva. M., and Koutsopoulos. H.,"An integrated approach to vehicle routing and congestion prediction for real-time driver guidance." Transportation Research Record 1408, TRB, Washington, D.C., pp. 66-74,1993.
5. Hobeika A.G,Chang kyun Kim,"Traffic-flow-prediction system based on upstream traffic [A]". Vehical Navigation and Information System Conference [C],1994.
6. Ben-Akiva. M., Cascetta. E. and Whittaker. J., "Recent progress in short-range traffic prediction." Compendium of Technical Papers, Institute of Transportation Engineers (ITE), pp. 262-265,1993.
7. D. C. Sansom, T. Downs and T. K. Saha, "Evaluation of Support Vector Machine Based Forecasting Tool in Electricity Price Forecasting For Australian National Electricity Market Participants", in Proceedings of Australasian Universities Power Engineering Conference, 2002.
8. J.W.C. van Lint, S.P. Hoogendoorn and H.J. van Zuylen, "Robust and adaptive travel time prediction with neural networks," proceedings of the 6th annual TRAIL Congress (part 2), December 2000.
9. Mangasarian, O.L. and Musicant, D.R., "Lagrangian Support Vector Machines", Journal of Machine Leamlng Research, Jan.2000, pp. 161-177.
10. Suykens, J.A.K. and Vandewalle, J.,"Least Squares Support Vector Machine Classifiers", Neural Process Letter. Sept. 1999, pp.293-300.
11. Junshui Ma, "Accurate On-line Support Vector Regression", Neural Computation, Vol.15, 2003, pp2683-2703
12. G.H. Golub, C.F. Van Loan, Matrix Computations, Baltimore MD: Johns Hopkins University Press, 3th edition, 1996.